CN108875615B - Deposition area remote sensing identification method and device, electronic equipment and storage medium - Google Patents

Abstract

The embodiment of the specification discloses a deposition area remote sensing identification method and device, electronic equipment and a storage medium. The method comprises the following steps: grouping adjacent remote sensing data in the remote sensing data set aiming at the target area based on the brightness to obtain a remote sensing data subset; calculating at least two different characteristic values of the sub-region at least based on the remote sensing data subset; screening out adjacent sub-areas which meet the conditions from the sub-areas of the target area to form at least one sedimentation area; wherein the condition is that the eigenvalues of adjacent sub-regions meet a threshold range of corresponding eigenvalues determined by the sedimentation. The deposition area can be identified through the embodiment of the invention, and then the characteristic value of the deposition is obtained.

Description

Deposition area remote sensing identification method and device, electronic equipment and storage medium

Technical Field

The invention relates to the field of geological exploration, in particular to a remote sensing identification method and device for a sedimentary region, electronic equipment and a storage medium.

Background

In oil exploration, the identification of depositional areas is an important part. For example, alluvial fans of different scales are distributed in almost all middle and new-generation hydrocarbon-containing basins in China, and the research on the sedimentation characteristics of the alluvial fans becomes very important and becomes an important exploration object for continental basins in China. At present, two main modern alluvial fan deposition research means are deposition investigation and simulation experiment, the traditional deposition investigation has the defect of limited observation range, and the simulation experiment mode has difficulty in recovering a real natural deposition process due to limited simulation parameters. And the existing remote sensing identification method of the alluvial fan only stays on the level of spectral feature analysis. According to the method, during spectral feature analysis, image analysis results are relatively fragmented based on information of different spectral wave bands of a single pixel, and clear identification cannot be achieved when the alluvial fan is identified, such as identification of the boundary of the alluvial fan.

Disclosure of Invention

The purpose of the embodiment of the specification is to provide a remote sensing identification method and device for a deposition area, an electronic device and a storage medium, which can identify the distribution position of a modern deposition body in a target area.

The embodiment of the specification provides a remote sensing identification method for a deposition area, which comprises the following steps: grouping adjacent remote sensing data in the remote sensing data set aiming at the target area based on the brightness to obtain a remote sensing data subset; wherein the remote sensing data subset corresponds to a sub-region of the target region; the deposition area is located within the target area; calculating at least two different characteristic values of the sub-region at least based on the remote sensing data subset; one of the different characterization values is a spectral feature index based on a spectral rule, and the spectral feature index is used for characterizing the spectral feature of the sub-region; the spectrum rule is established by taking the spectrum reflectivity as the feature of the ground feature; screening out adjacent sub-areas which meet the conditions from the sub-areas of the target area to form at least one sedimentation area; wherein the condition is that the characteristic values of adjacent sub-regions satisfy a threshold range of corresponding characteristic values of the deposition region.

The embodiment of the present specification further provides a deposition area remote sensing identification device, including: the first processing unit is used for grouping adjacent remote sensing data in the remote sensing data set aiming at the target area based on the brightness to obtain a remote sensing data subset; wherein the remote sensing data subset corresponds to a sub-region of the target region; the deposition area is located within the target area; the second processing unit is used for calculating at least two different characteristic values of the sub-region at least based on the remote sensing data subset; one of the different characterization values is a spectral feature index based on a spectral rule, and the spectral feature index is used for characterizing the spectral feature of the sub-region; the spectrum rule is established by taking the spectrum reflectivity as the feature of the ground feature; a third processing unit, configured to screen out, from the sub-regions of the target region, adjacent sub-regions that meet a condition to form at least one deposition region; wherein the condition is that the characteristic values of adjacent sub-regions satisfy a threshold range of corresponding characteristic values of the deposition region.

The embodiment of the present specification further provides an electronic device, which is applied to remote sensing identification of a deposition area, and includes: a memory and a processor; the memory having stored therein computer instructions; the processor is used for executing the computer instructions to realize the following steps: grouping adjacent remote sensing data in the remote sensing data set aiming at the target area based on the brightness to obtain a remote sensing data subset; wherein the remote sensing data subset corresponds to a sub-region of the target region; the deposition area is located within the target area; calculating at least two different characteristic values of the sub-region at least based on the remote sensing data subset; one of the different characterization values is a spectral feature index based on a spectral rule, and the spectral feature index is used for characterizing the spectral feature of the sub-region; the spectrum rule is established by taking the spectrum reflectivity as the feature of the ground feature; screening out adjacent sub-areas which meet the conditions from the sub-areas of the target area to form at least one sedimentation area; wherein the condition is that the characteristic values of adjacent sub-regions satisfy a threshold range of corresponding characteristic values of the deposition region.

Embodiments of the present specification also provide a computer storage medium for remote sensing identification of a deposition area, the computer storage medium storing computer program instructions that, when executed, implement: grouping adjacent remote sensing data in the remote sensing data set aiming at the target area based on the brightness to obtain a remote sensing data subset; wherein the remote sensing data subset corresponds to a sub-region of the target region; the deposition area is located within the target area; calculating at least two different characteristic values of the sub-region at least based on the remote sensing data subset; one of the different characterization values is a spectral feature index based on a spectral rule, and the spectral feature index is used for characterizing the spectral feature of the sub-region; the spectrum rule is established by taking the spectrum reflectivity as the feature of the ground feature; screening out adjacent sub-areas which meet the conditions from the sub-areas of the target area to form at least one sedimentation area; wherein the condition is that the characteristic values of adjacent sub-regions satisfy a threshold range of corresponding characteristic values of the deposition region.

As can be seen from the technical solutions provided in the embodiments of the present specification, the embodiments of the present specification group remote sensing data in a remote sensing data set for a target area based on brightness to obtain a remote sensing data subset; wherein the remote sensing data subset corresponds to a sub-region of the target region; calculating at least two characteristic values of the sub-region at least based on the remote sensing data subset; screening out adjacent sub-areas which meet the conditions from the sub-areas of the target area to form at least one sedimentation area; wherein the condition is that the characteristic values of adjacent sub-regions satisfy a threshold range of corresponding characteristic values of the deposition region. By doing so, the corresponding zone location of the deposition within the target zone can be identified.

Drawings

In order to more clearly illustrate the embodiments of the present specification or the technical solutions in the prior art, the drawings needed to be used in the description of the embodiments or the prior art will be briefly introduced below, it is obvious that the drawings in the following description are only some embodiments described in the specification, and other drawings can be obtained by those skilled in the art without inventive labor.

FIG. 1 is a flow chart of a method for remote sensing identification of a deposition area provided herein;

FIG. 2 is a schematic diagram of remote sensing of geographic locations of a research area provided herein;

FIG. 3 is a schematic representation of the results of image segmentation of a region of interest provided herein;

FIG. 4 is a diagram illustrating an image spectral information feature extraction result provided in the present specification;

FIG. 5 is a diagram illustrating an image texture information feature extraction result provided in the present specification;

FIG. 6 is a diagram illustrating an image topographic information feature extraction result provided in the present specification;

FIG. 7 is a schematic view of the distribution of the spread of the alluvial fan provided herein;

FIG. 8 is a schematic view of a remote sensing process for identifying deposition areas by alluviation fans as provided herein;

FIG. 9 is a schematic view of a contour line assisted impulse fan edge determination provided herein;

FIG. 10 is a graph of alluvial fan boundary extraction results provided herein;

fig. 11 is a schematic diagram of an electronic device provided in the present specification.

Detailed Description

The technical solutions in the embodiments of the present disclosure will be clearly and completely described below with reference to the drawings in the embodiments of the present disclosure, and it is obvious that the described embodiments are only a part of the embodiments of the present disclosure, and not all of the embodiments. All other embodiments obtained by a person skilled in the art without making any inventive step based on the embodiments in this specification shall fall within the scope of protection of this application.

Please refer to fig. 1. The specification provides a remote sensing identification method for a deposition area. The remote sensing identification method for the deposition area can comprise the following steps.

In this embodiment, the object for executing the remote sensing method for identifying a deposition area may be an electronic device having a logical operation function. The electronic devices may be servers and clients. The client can be a desktop computer, a tablet computer, a notebook computer, a smart phone, a digital assistant and the like. Of course, the client is not limited to the electronic device with certain entities, and may also be software running in the electronic device. It may also be program software formed by program development, which may be run in the above-mentioned electronic device. The server can be an electronic device with the functions of operation and network interaction; software that runs in the electronic device and provides business logic for data processing and network interaction is also possible.

In the present embodiment, the sediment may be that rocks, gravel, soil, and the like entrained in the water flow are settled and deposited in low-lying areas such as riverbeds and gulfs, and thus the settled materials form a wash layer or a natural deposit. Wherein, the alluvial fan can be a fan-shaped accumulation body at the river mountain outlet. The deposition system of the flush fan can be a deposition body which is formed by accumulating a large amount of debris materials and has a shape close to a fan shape due to sharp reduction of terrain, dispersion of water in four directions, sudden reduction of flow speed and rapid accumulation of the debris materials when temporary flood or intermittent flood flows out of a mountain opening. A river sedimentation system in the transition from mountain canyons to open plains is a fan-shaped or semi-cone-shaped deposit with a preponderance of coarse debris. In this embodiment, the deposition may be, for example, deposition by a alluvial fan, deposition by a flood fan, or the like. The deposition area characterizes an area of the deposition. The deposition may be a modern deposition. Correspondingly, the sedimentary area may be a modern sedimentary area, as distinguished from subterranean sediments in the field of petroleum sedimentology.

Step S10: grouping adjacent remote sensing data in the remote sensing data set aiming at the target area based on the brightness to obtain a remote sensing data subset; wherein the remote sensing data subset corresponds to a sub-region of the target region; the deposition area is located within the target area.

In this embodiment, the target region may refer to a location region where a target study object is located. For example, when studying alluvial fan deposits in an oil and gas basin, the oil and gas basin is the target region.

In this embodiment, the remote sensing data may be data obtained by obtaining a remote sensing image of the target area by using a remote sensing technique. The Remote Sensing Image (RS) is Image data for recording the size of electromagnetic waves of various ground features. The remote sensing data can correspond to each pixel or pixel point on the target area, and the remote sensing data set is a set of remote sensing data. The remote sensing data subset is a set of remote sensing data of sub-regions divided corresponding to the target region.

In this embodiment, the remote sensing data may be preprocessed remote sensing data. For example, GF1 image data for the target region for days 7, 23, 2015 includes 4 multispectral bands (red, green, blue, near-infrared) and 1 panchromatic band with multispectral resolution of 8m and a panchromatic band of 2 m. Because the 1A-level data is used, the data is preprocessed, including atmospheric correction, orthorectification, image fusion, geometric correction and the like, to obtain preprocessed images, so that the image quality is improved, and a foundation is laid for subsequent image analysis work. The level 1A data is remote sensing raw data which is subjected to data reconstruction and has time reference, auxiliary information (including radiation, geometric correction coefficients and the like) and geographic coordinate parameters. The preprocessed image can be 2B-level remote sensing data, and the remote sensing data is subjected to system geometric correction and DN value conversion to be reflectivity and accurate spatial position calibration.

In this embodiment, the brightness may be a value representing a degree of shading of an image in the remote sensing data. In particular, a value indicative of the degree of brightness at the location of each picture element may be translated from a plurality of bands for that picture element.

In the present embodiment, the remote sensing data in the remote sensing data set for the target area is grouped based on brightness to obtain a remote sensing data subset. Specifically, for example, the image is segmented according to the spectral characteristics of adjacent pixels, and the image is segmented into a plurality of spot objects by selecting appropriate segmentation and merging dimensions; the image segmentation is a process of segmenting the whole image area into a plurality of non-empty sub-areas which are not overlapped with each other according to the homogeneity and heterogeneity standard, and the same area has the same or similar characteristics. When the image is segmented, some features may be wrongly segmented, or the features may be segmented into a plurality of parts, and the problems can be solved by segmenting and combining the features at the same time. Segmentation algorithm, the Intensity algorithm can be selected as follows: the segmentation algorithm based on brightness is very suitable for small gradient changes. The merging algorithm can select the Full lamb Schedule algorithm: and combining the areas with large blocks and strong texture, realizing the segmentation of the image, and obtaining the sub-areas corresponding to the remote sensing data in each sub-area. Referring to FIG. 2, a target region (region of interest) for identifying alluviation fan depositions in one example is characterized. Wherein the target area (research area) belongs to the northwest region of China. Please refer to fig. 3, which is a diagram illustrating the result of dividing the target area based on brightness.

Step S12: calculating at least two different characteristic values of the sub-region at least based on the remote sensing data subset; one of the different characterization values is a spectral feature index based on a spectral rule, and the spectral feature index is used for characterizing the spectral feature of the sub-region; the spectrum rule is established by taking the spectrum reflectivity as the feature of the ground feature;

in this embodiment, the characterization value is used to distinguish a characteristic of a deposition area from other areas in the target area, so as to identify the deposition area in the target area. Specifically, the characterization value may be a spectral characteristic index, a vegetation index, a soil index, and the like based on the remote sensing data of each sub-region. For example, for the identification of alluvial fan deposition, the characterization values may extract spectral information, texture information, terrain information, and the like for each sub-region by establishing spectral rules, texture rules, terrain rules, and the like. The spectrum information is the electromagnetic wave spectrum characteristics of the ground features, and different ground features can be identified by the difference of the electromagnetic wave spectrum characteristics of the satellite because different ground features have different spectral characteristics of the ground features. The characterization values corresponding to the spectral information may include an Alluvial Fan Index (AFI), a vegetation index (NDVI), a soil index (NDSI), and the like; the texture information is a spatial correlation characteristic of gray levels in an image formed by repeatedly appearing gray level distributions at spatial positions, and the gray level co-occurrence matrix is a method for describing texture by studying the spatial correlation characteristic of gray levels. The characterization values corresponding to the texture information may include energy (ASM), Contrast (CON), and Correlation (COR) of the gray level co-occurrence matrix, i.e., texture feature energy index, texture feature contrast, texture feature correlation value, etc.; the topographical information is topographical and topographic features that may be derived from digital elevation Data (DEM) that describe the target. The characteristic values corresponding to the terrain information may include a Digital Elevation (DEM), a slope (slope) and an incline (ASPECT), i.e., a terrain feature elevation value, a terrain feature slope angle, and the like.

In this embodiment, the different characterization values may include a spectral rule-based characterization value, or a texture rule-based characterization value, or a terrain rule-based characterization value. The spectrum rule may refer to a rule established by identifying a target feature through a difference in spectral characteristics of electromagnetic waves and using spectral reflectance as a feature of the feature. The texture rule may be a rule established by identifying a target ground object through a gray scale space correlation characteristic difference and using a ground surface texture representing a gray scale space correlation characteristic as a ground object feature. The terrain rules may be rules established by using digital elevation data of the area under study to calculate a series of terrain feature factors describing the features of the terrain and the terrain. The characterization value based on the spectrum rule can comprise other indexes such as a spectrum characteristic index, a vegetation index, a soil index and the like; specifically, the above three kinds of characteristic values are not limited. The characterization value based on the texture rule may include a texture feature energy index, a texture feature contrast, a texture feature correlation value, and the like; specifically, the above three kinds of characteristic values are not limited. The characteristic value based on the terrain rule can comprise a terrain feature elevation value, a terrain feature slope value and a terrain feature slope angle; specifically, the above three kinds of characteristic values are not limited.

In this embodiment, the plurality of token values for each of the sub-regions may form a token value set. At least two characterizing values of the set are present, one of which is a spectral feature index. The spectral feature index is an index that characterizes spectral characteristics within the target region. The spectral characteristic index is constructed according to the typical characteristics of ground objects in red light wave bands and near infrared wave bands and the characteristic that the reduction rate of the brightness value of the shadow in blue-green light wave bands is different. Specifically, the spectral feature index may be obtained according to the following formula: the method comprises the following steps of (1) AFI (atomic interface) ═ R/NIR-B/G, wherein AFI is the spectral characteristic index and can also be called as an impulse fan index when an impulse fan is identified; r represents a red light band, G represents a green light band, B represents a blue light band, and NIR represents a near infrared band. The resulting AFI value characterizes the spectral feature index of the subregion.

In this embodiment, the token value set may further include other token values. For example, the vegetation index may be obtained by the following formula: NDVI ═ (NIR-R)/(NIR + R). NDVI is the vegetation index of the sub-area, R represents a red light wave band, and NIR represents a near infrared wave band. The soil index may be obtained by the following formula: NDSI ═ R-G)/(R + B). The NDSI is the soil index of the sub-area, R represents a red light wave band, G represents a green light wave band, B represents a blue light wave band, and NIR represents a near infrared wave band.

In this embodiment, when at least two different characteristic values of the sub-region are obtained through calculation, the carrier for calculation may be a client or a server with an operation function, and details are not described here.

Step S14: screening out adjacent sub-areas which meet the conditions from the sub-areas of the target area to form at least one sedimentation area; wherein the condition is that the characteristic values of adjacent sub-regions satisfy a threshold range of corresponding characteristic values of the deposition region.

In this embodiment, the eligible sub-regions may be deposition feature eligible sub-regions that collectively form the deposition region. Specifically, in the set of characterization values of the sub-region that meets the condition, at least the spectral feature index is included, and the spectral feature index satisfies a preset threshold range. And setting threshold ranges of different dimension characteristic values through comparison analysis according to characteristics such as deposition shapes. If the threshold range of the AFI index is set to be-0.04444-0.09700, the spectral feature index AFI of the subregion is screened out within-0.04444-0.09700, and the screened subregion forms a deposition region.

In this embodiment, adjacent sub-regions that meet the condition are screened out, and the sub-region that meets the condition may be determined from the token value set of the sub-region based on the segmented image patch object (sub-region). Specifically, the characterization values in the sub-regions all conform to a set threshold range, the attribution of the segmentation object (sub-region) is determined, the segmentation object belongs to the deposition region, and finally, a deposition spread range (deposition region) is generated. For example, for a target area, the range of an AFI index is defined to be-0.04444-0.09700, the range of an NDVI index is defined to be-0.03437-0.01303, the range of a texture characteristic energy index ASM is defined to be 0-19.30000, the range of a terrain characteristic elevation value DEM is defined to be 1198.61735-1383.30153, and the range of a terrain characteristic gradient angle SLOP is defined to be 0.24573-0.96708. Referring to fig. 4, fig. 4 shows a graph of the extraction result of the spectral information features of the image, where a in fig. 4 is a graph of the extraction result of the characteristic value AFI, b in fig. 4 is a graph of the extraction result of the characteristic value NDVI, and c in fig. 4 is a graph of the extraction result of the characteristic value NDSI. Referring to fig. 5, fig. 5 shows a graph of an image texture information feature extraction result, where a in fig. 5 is a graph of a characterization value ASM extraction result; b in FIG. 5 is a graph showing the result of extracting the token CON; fig. 5 c is a graph of the extraction result of the characteristic value COR. Referring to fig. 6, fig. 6 shows a result graph of extracting topographic information features of an image, and a in fig. 6 is a result graph of extracting a characteristic value DEM; b of fig. 6 is a graph of the extraction result of the token value SLOP; fig. 6 c is a graph showing the results of ASPECT extraction. And when the characteristic values in the sub-areas all fall into the correspondingly limited range, marking the sub-areas as belonging to the deposition area, judging each sub-area of the target area to obtain all sub-areas meeting the conditions, and forming the deposition area.

In this embodiment, the threshold range of the characterization value determined according to the deposition may be a region formed by sub-regions screened according to the threshold range, which is in accordance with the deposition characteristics. Specifically, the areas formed by the sub-areas meeting the conditions may meet the deposited morphological characteristics, texture characteristics, topographic characteristics, and the like. Referring to fig. 7, fig. 7 shows a distribution diagram of a alluvial fanning area obtained by screening the sub-areas.

Through the above embodiment, the target region may be segmented to obtain a plurality of sub-regions, and then one or more characteristic values may be calculated for each of the sub-regions, where the characteristic values at least include a spectral feature index. To enable more accurate identification of the deposition of the target area.

Referring to FIG. 8, in one specific example scenario. The deposition to be identified is the alluvial fan deposition of a target area.

In the scene example, a client or a server selects an optical remote sensing image of a target area and performs image preprocessing; the image preprocessing comprises a series of processing procedures of atmospheric correction, orthorectification, image fusion and geometric correction. The image quality is improved, and a foundation is laid for subsequent image analysis work.

In the present scenario example, the image is segmented according to the spectral features of neighboring pixels, and the image is segmented into several spot objects (sub-regions) by selecting appropriate segmentation and merging dimensions. Then, extracting spectral information, texture information and topographic information of the image based on the processed image, and establishing a series of rules to realize feature extraction; the spectral information is the spectral characteristics of the electromagnetic waves of the ground features, and different ground features can be identified by the difference of the spectral characteristics of the electromagnetic waves of the satellite, wherein the spectral characteristics of the different ground features comprise an Alluvial Fan Index (AFI), a vegetation index (NDVI), a soil index (NDSI) and the like; the texture information is a spatial correlation characteristic of gray scales in an image formed by repeatedly appearing gray scale distribution at a spatial position, and the gray scale co-occurrence matrix is a method for describing textures by researching the spatial correlation characteristic of the gray scales, and comprises energy (texture feature energy index ASM), contrast (texture feature contrast CON), correlation (texture feature correlation value COR) and the like of the gray scale co-occurrence matrix; the terrain information is derived from digital elevation data (terrain feature elevation value DEM) to describe the terrain and landform features of the target, and comprises digital elevation (terrain feature elevation value DEM), gradient (terrain feature gradient value SLOP), gradient (terrain feature gradient angle ASPECT) and the like. Respectively setting appropriate threshold rules for the three types of information to realize feature extraction;

in the present scenario example, different token threshold ranges are set by the comparison analysis. AFI ranges from-0.04444 to 0.09700, NDVI ranges from-0.03437 to-0.01303, ASM ranges from 0 to 19.30000, DEM ranges from 1198.61735 to 1383.30153, and SLOP ranges from 0.24573 to 0.96708. And selecting a logical combination logical method for each sub-area, and screening to obtain the sub-areas meeting the conditions. That is, each of the characteristic values of the sub-regions that meet the condition is within a preset threshold value.

In the scene example, the alluvial fan spread range is combined with contour line trend auxiliary judgment by contour lines extracted from the DEM (Digital Elevation Model) of the target area, and alluvial fan boundaries are determined; and realizing the quantitative characterization of the deposition space distribution characteristics of the alluvial fan on the basis of remote sensing and DEM data. Specifically, the quantitative characteristics comprise sector perimeter, sector area, sector radius, sector height difference and sector inclination angle. Sector circumference: the length of the outer boundary of the alluviation fan; the area of the sector: area within fan boundaries is alluded to; sector radius: the maximum length of the alluvial fan from the fan root to the fan edge; sector height difference: the maximum height difference from the fan root to the fan edge is accumulated; the inclination angle of the fan body is as follows: tangent of included angle between sector and bottom of deposit. In the present scenario example, the quantitative characteristics of the resulting alluviation fan are 3.09km of fan radius, 5.05km of horizontal axis, 14.58km of fan perimeter, 11.71km2 of fan area, 150m of fan height difference, and 4.85% of fan inclination angle (tan).

By the scene example, spectral information, texture information and topographic information of satellite remote sensing are fully utilized, GIS and GPS technologies are combined, the remote sensing technology is introduced into the field of modern sedimentology, the remote sensing-based alluvial fan sedimentary remote sensing identification and quantitative characterization method is provided, the space-time distribution rule of the modern alluvial fan is analyzed, and a basis is provided for modern alluvial fan type modern sedimentary feature summarization and fine portrayal under specific geological conditions. The traditional deposition investigation mode is expanded from point to surface and from two dimensions to three dimensions, a new idea of researching the deposition distribution rule and characteristics of the modern alluvial fan is created, a geologist is facilitated to develop theoretical research work of a scientific system of modern deposition, a deposition mode which is useful for exploration, development and production of alluvial fan reservoir bodies in oil-gas basins is established under the ancient comparison research of the current theory, and the productivity and the efficiency of exploration and development of oil fields are improved.

In one embodiment, the different characterization values further include at least one of: vegetation index and soil index based on spectrum rule; the vegetation index is used for characterizing the spectral characteristics of the vegetation in the sub-region; the soil index is used to characterize the spectral characteristics of the sub-area soil.

In the present embodiment, the spectrum rule refers to a rule in which a target feature is recognized by a difference in spectral characteristics of electromagnetic waves, and spectral reflectance is established as a feature of the feature. The characterization value based on the spectrum rule can comprise other indexes such as a spectrum characteristic index, a vegetation index, a soil index and the like; specifically, the above three kinds of characteristic values are not limited.

In this embodiment, the vegetation index is used to characterize the spectral characteristics of the sub-region vegetation. Can be calculated by the following formula: NDVI ═ (NIR-R)/(NIR + R). NDVI is the vegetation index of the sub-area, R represents a red light wave band, and NIR represents a near infrared wave band. The soil index is used to characterize the spectral characteristics of the sub-area soil. The soil index may be obtained by the following formula: NDSI ═ R-G)/(R + B). The NDSI is the soil index of the sub-area, R represents a red light wave band, G represents a green light wave band, B represents a blue light wave band, and NIR represents a near infrared wave band.

Through the embodiment, the characteristic value based on the spectrum rule except the spectrum characteristic index is also considered, so that the identification region is more accurate.

In one embodiment, the calculating the characteristic value of the sub-region further comprises at least one of the following characteristic values: a characterization value based on the texture rule; or a characterization value based on a terrain rule; the texture rule is a ground surface texture which represents gray scale space correlation characteristics and is used as a ground feature establishing rule; the terrain rules are rules established by terrain feature factors describing terrain and landform features.

In this embodiment, the texture rule may be a ground surface texture establishing rule that identifies a target ground object by a difference in gray scale space correlation characteristics and takes a ground surface texture representing the gray scale space correlation characteristics as a ground object feature. The characterization value based on the texture rule may include a texture feature energy index, a texture feature contrast, a texture feature correlation value, and the like; specifically, the above three kinds of characteristic values are not limited. The texture characteristic energy index represents the uniformity degree and the texture thickness of the image gray level distribution; the definition of the texture feature contrast image and the depth of texture grooves; the texture feature contrast characterizes local gray scale correlation in the image. The gray level co-occurrence matrix is obtained by counting the condition that two pixels which keep a certain distance on an image respectively have a certain gray level.

In this embodiment, the texture feature energy index may be a sum of squares of element values of the gray level co-occurrence matrix, and may also be referred to as energy, which reflects a degree of uniformity of gray level distribution of an image and a thickness of a texture. If all the values of the co-occurrence matrix are equal, the ASM value is small; conversely, if some of the values are large and others are small, the ASM value is large. When the elements in the co-occurrence matrix are distributed in a concentrated manner, the ASM value is large. A large ASM value indicates a more uniform and regularly varying texture pattern.

In this embodiment, the texture feature contrast CON may reflect the definition of the image and the depth of the texture grooves. Specifically, the contrast of the brightness of a certain pixel value and the pixel value in the field thereof can be reflected. If the off-diagonal elements have large values, i.e. the image luminance values change very fast, the CON will have a large value. The deeper the texture groove, the higher the contrast, and the clearer the visual effect; otherwise, if the contrast is small, the grooves are shallow and the effect is blurred. The larger the number of pairs of pixels having a large gray scale difference, i.e., a large contrast, the larger the value. The larger the value of the element far from the diagonal in the gray-scale public matrix, the larger CON.

In this embodiment, the texture feature correlation value COR may reflect the consistency of the image texture due to the correlation. If there is horizontal texture in the image, the COR of the horizontal matrix is greater than the COR values of the remaining matrices. It measures the degree of similarity of the spatial gray level co-occurrence matrix elements in the row or column direction. The magnitude of the correlation value reflects the local gray level correlation in the image. When the matrix element values are uniform and equal, the correlation value is large; conversely, if the matrix pixel values differ greatly then the correlation value is small.

In this embodiment, the terrain rules may be established by using a series of terrain feature factors describing terrain and landform features calculated by using digital elevation data of a research area. The characteristic value based on the terrain rule can comprise a terrain feature elevation value, a terrain feature slope value and a terrain feature slope angle; specifically, the above three kinds of characteristic values are not limited.

In the embodiment, at least one of the following characteristic values of the sub-area based on the terrain rule is obtained by calculation based on the height value corresponding to the remote sensing data in each remote sensing data set; a terrain feature elevation value, a terrain feature slope value and a terrain feature slope angle; the topographic feature elevation value characterizes the distance from the sub-area to an absolute base plane along the direction of the plumb line; the topographic feature slope value represents the degree of steepness of the sub-region; and the topographic characteristic slope angle represents the projection direction of the slope normal of the sub-region on the horizontal plane.

In this embodiment, the height value corresponding to the remote sensing data may refer to a height attribute value at a corresponding position of each pixel. Wherein the height value may be an altitude or a height corresponding to a base. In particular, the height value at each picture element position may be assigned. For example, the measurement can be obtained by a Digital Elevation Model (DEM). The digital elevation model is a solid ground model which realizes digital simulation of ground terrain (namely digital expression of terrain surface morphology) through limited terrain elevation data and expresses the ground elevation in a group of ordered numerical array forms. Of course, the height value corresponding to the remote sensing data in each remote sensing data set may also be obtained in other manners, which is not described herein in detail.

In this embodiment, the topographic feature elevation characterizes a distance of the sub-area from an absolute base plane in a direction of a plumb line. Specifically, for example, for a sub-region, each piece of remote sensing data in the corresponding subset of remote sensing data is assigned with a height value, and an average value of the height values in the sub-region may be used as the height value of the terrain feature of the sub-region.

In this embodiment, the topographical feature gradient value is indicative of the degree to which the sub-region is steep. Specifically, according to the height value of each pixel position in the sub-region, a space curved surface of the sub-region is determined, a tangent plane of the curved surface is made, and according to the included angle between the tangent plane and the horizontal plane, the terrain characteristic gradient value of the sub-region is obtained.

In this embodiment, the topographic feature slope angle represents a direction of a projection of the sub-region slope normal onto a horizontal plane. Specifically, the spatial curved surface of the sub-region can be determined according to the height value of each pixel position in the sub-region, and the tangent plane of the curved surface is made. And determining a normal of the tangent plane, projecting the normal to a horizontal plane, and forming an included angle with the due north direction, wherein the included angle can be used as the slope angle of the topographic features.

Referring to fig. 5, fig. 5 shows a graph of an image texture information feature extraction result, where a in fig. 5 is a graph of a characterization value ASM extraction result; b in FIG. 5 is a graph showing the result of extracting the token CON; fig. 5 c is a graph of the extraction result of the characteristic value COR. Referring to fig. 6, fig. 6 is a diagram illustrating a result of extracting topographic information features of an image, where a in fig. 6 is a diagram illustrating a result of extracting topographic feature elevation values; b in fig. 6 is a result graph of extracting a slope value of a topographic feature, which may also be called an SLOP factor extraction result graph; fig. 6 c is a result graph of the slope angle value of the topographic feature, which may also be referred to as an ASPECT factor extraction result graph. Through the embodiment, besides the spectral characteristic index, one or more of the textural characteristic energy index, the textural characteristic contrast and the textural characteristic correlation value are comprehensively considered, so that the deposition is closer to the actual situation when the remote sensing data is used for identifying the deposition. In addition, a terrain height value is introduced, a terrain characteristic height value, a terrain characteristic gradient value and a terrain characteristic gradient angle of the sub-area can be obtained, and the sub-area is used for identifying the deposition.

In one embodiment, the texture rule based characterization values include at least one of: texture feature energy index, texture feature contrast and texture feature correlation value; the texture characteristic energy index represents the uniformity degree and the texture thickness of the image gray level distribution; the texture feature contrast degree characterizes the definition of an image and the depth degree of texture grooves; the texture feature correlation values characterize local gray level correlations in the image.

Specific terms of relevance may be specifically explained with reference to the foregoing embodiments. This embodiment provides 3 typical texture rule-based characterization values for identifying deposition areas.

In one embodiment, the terrain rule-based characterizing value includes at least one of: a terrain feature elevation value, a terrain feature slope value and a terrain feature slope angle; wherein the topographic feature elevation characterizes a distance of the sub-area to an absolute base along a plumb line direction; the topographic feature slope value represents the degree of steepness of the sub-region; and the topographic characteristic slope angle represents the projection direction of the slope normal of the sub-region on the horizontal plane.

Specific terms of relevance may be specifically explained with reference to the foregoing embodiments. The present embodiment provides 3 typical terrain-based rule characterizing values for identifying deposition areas.

In one embodiment, the step of calculating at least two different characteristic values of the sub-region includes a spectral feature index based on a spectral rule, a characteristic value based on a texture rule, and a characteristic value based on a terrain rule.

Specific terms of relevance may be specifically explained with reference to the foregoing embodiments. In the deposition area remote sensing identification method, the calculated characteristic values at least simultaneously include a spectrum characteristic index based on a spectrum rule, a characteristic value based on a texture rule and a characteristic value based on a terrain rule. The spectral rule, the texture rule and the terrain rule can be simultaneously considered, so that the identified deposition area is more accurate.

In one embodiment, the step of screening out the sub-areas of the target area that meet the condition of adjacent sub-areas to form at least one deposition area may further include the following steps: and correcting the boundary of the deposition area according to the contour line trend of the target area.

In this embodiment, the contour trend of the target area may specifically refer to a trend characteristic between contours of the target area. Specifically, when identifying the deposition of the alluvial fan, the height of the boundary position of the alluvial fan is lower than the height of the terrain on both sides. The top and the side edges of the alluvial fan can be determined by contour line trend auxiliary judgment. The contour line protrudes to a place with high elevation, and the elevation of the place pointed by the protrusion is relatively low relative to the elevations of the two sides of the place. The fan is formed by spreading and depositing materials flushed by water flow, the water always flows to a relatively low position, and the points are connected to determine the side edge of the fan body. For example, contour lines of the study area at intervals of 30m are extracted from the digital elevation DEM data, the contour lines are superposed on the spread range of the generated alluvial fan, and the accurate boundaries of the fan top and the side edges of the alluvial fan are determined according to the contour line running direction. Referring to FIG. 9, the convex points of the contour are connected by the contour to form the boundary of the deposition area, which is then corrected. Referring to fig. 10 and 10, a curve is a modified boundary of the deposition region.

In the present embodiment, the boundary of the deposition area may be corrected by connecting convex points in a certain range near the boundary of the deposition area according to the contour line of the target area in addition to the boundary of the deposition area to form a new boundary so as to correct the boundary of the area where the result is originally calculated.

Through the embodiment, the deposition is more accurately identified by further correcting the boundary of the deposition area on the basis of obtaining the deposition area by utilizing the characteristic that the height of the deposition edge is lower than the height values of the landforms on two sides of the deposition edge. Especially when applied to the recognition of alluvial fan deposition.

In one embodiment, when the method is applied to remote sensing identification of deposition areas of alluvial fans, the method may further include the following steps: at least one of the following alluvial fan deposition characteristic values is calculated: sector area, sector perimeter, sector radius, sector horizontal axis, sector height difference and sector inclination angle.

In this embodiment, when the method is applied to remote sensing identification of deposition areas of alluvial fans, the circumference of the fan surface may refer to the length of the outer boundary of the alluvial fan; the fan area may refer to the area within the alluvial fan boundary; the sector radius can refer to the maximum length of the alluvial fan from the fan root to the fan edge; the fan body height difference can be the maximum height difference from the fan root to the fan edge of the alluvial fan; the inclination angle of the fan body can be a tangent value of an included angle between the fan surface and the deposition bottom surface; the sector axis of abscissas may refer to the maximum length perpendicular to the bed.

In this embodiment, the characteristic value may be calculated according to the physical meaning represented by the characteristic value, such as the height value of the deposition area and each position of the deposition area, based on the deposition obtained by identification, and may also be calculated by professional software. And will not be described in detail herein.

Through the implementation mode, the alluvial fan characteristic values can be obtained, and the spatial distribution characteristic values can provide important parameters for the subsequent theoretical research of the deposition mode of the modern alluvial fan and the exploration, development and production of the alluvial fan reservoir body oil field in the oil-gas basin.

The embodiment of the specification also provides a remote sensing identification device for the deposition area, and the remote sensing identification device is described in the following embodiment. Because the principle of solving the problems of the remote sensing identification device for the deposition area is similar to that of the remote sensing identification method for the deposition area, the implementation of the remote sensing identification device for the deposition area can refer to the implementation of the remote sensing identification method for the deposition area, and repeated parts are not repeated. As used hereinafter, the term "unit" or "module" may be a combination of software and/or hardware that implements a predetermined function. Although the means described in the embodiments below are preferably implemented in software, an implementation in hardware, or a combination of software and hardware is also possible and contemplated. The device may specifically include: the device comprises a first processing unit, a second processing unit and a third processing unit. This structure will be specifically explained below.

The first processing unit is used for grouping adjacent remote sensing data in the remote sensing data set aiming at the target area based on the brightness to obtain a remote sensing data subset; wherein the remote sensing data subset corresponds to a sub-region of the target region; the deposition area is located within the target area.

The second processing unit is used for calculating at least two different characteristic values of the sub-region at least based on the remote sensing data subset; one of the different characteristic values is a spectral characteristic index, and the spectral characteristic index is used for characterizing the spectral characteristics of the sub-region.

A third processing unit, configured to screen out, from the sub-regions of the target region, adjacent sub-regions that meet a condition to form at least one deposition region; wherein the condition is that the characteristic values of adjacent sub-regions satisfy a threshold range of corresponding characteristic values of the deposition region.

The related terms in this embodiment can be referred to the comparison explanation of the previous embodiment, and are not described herein again.

Please refer to fig. 11. There is also provided in an embodiment of the present specification an electronic device, including: a memory and a processor.

The memory has stored therein computer instructions.

The processor is used for executing the computer instructions to realize the following steps: grouping adjacent remote sensing data in the remote sensing data set aiming at the target area based on the brightness to obtain a remote sensing data subset; wherein the remote sensing data subset corresponds to a sub-region of the target region; the deposition area is located within the target area; calculating at least two different characteristic values of the sub-region at least based on the remote sensing data subset; one of the different characterization values is a spectral feature index based on a spectral rule, and the spectral feature index is used for characterizing the spectral feature of the sub-region; the spectrum rule is established by taking the spectrum reflectivity as the feature of the ground feature; screening out adjacent sub-areas which meet the conditions from the sub-areas of the target area to form at least one sedimentation area; wherein the condition is that the characteristic values of adjacent sub-regions satisfy a threshold range of corresponding characteristic values of the deposition region.

The memory may be for storing information. The Memory includes, but is not limited to, a Random Access Memory (RAM), a Read-Only Memory (ROM), a Cache (Cache), a Hard Disk Drive (HDD), or a Memory Card (Memory Card). The memory may be used to store computer program instructions.

The processor may be implemented in any suitable way. For example, the processor may take the form of, for example, a microprocessor or processor and a computer-readable medium that stores computer-readable program code (e.g., software or firmware) executable by the (micro) processor, logic gates, switches, an Application Specific Integrated Circuit (ASIC), a programmable logic controller, an embedded microcontroller, and so forth.

In this embodiment, the functions and effects specifically realized by the electronic device can be explained by comparing with other embodiments, and are not described herein again.

There is also provided in an embodiment of the present specification a computer storage medium for remote sensing identification of a deposition area, the computer storage medium storing computer program instructions that, when executed, implement: grouping adjacent remote sensing data in the remote sensing data set aiming at the target area based on the brightness to obtain a remote sensing data subset; wherein the remote sensing data subset corresponds to a sub-region of the target region; the deposition area is located within the target area; calculating at least two different characteristic values of the sub-region at least based on the remote sensing data subset; one of the different characterization values is a spectral feature index based on a spectral rule, and the spectral feature index is used for characterizing the spectral feature of the sub-region; the spectrum rule is established by taking the spectrum reflectivity as the feature of the ground feature; screening out adjacent sub-areas which meet the conditions from the sub-areas of the target area to form at least one sedimentation area; wherein the condition is that the characteristic values of adjacent sub-regions satisfy a threshold range of corresponding characteristic values of the deposition region.

In this embodiment, the Memory includes, but is not limited to, a Random Access Memory (RAM), a Read-Only Memory (ROM), a Cache (Cache), a Hard Disk Drive (HDD), or a Memory Card (Memory Card). The memory may be used to store computer program instructions. The network communication unit may be an interface for performing network connection communication, which is set in accordance with a standard prescribed by a communication protocol.

In this embodiment, the functions and effects specifically realized by the program instructions stored in the computer storage medium can be explained by comparing with other embodiments, and are not described herein again.

Although the application content refers to a remote sensing identification method, a remote sensing identification device, an electronic device and a storage medium for a deposition area, the application is not limited to the situations described by the industry standards or the examples, and the like, some industry standards or the implementation slightly modified based on the implementation described by the custom mode or the examples can also achieve the same, equivalent or similar implementation effects or the expected implementation effects after the modification. Embodiments employing such modified or transformed data acquisition, processing, output, determination, etc., may still fall within the scope of alternative embodiments of the present application.

Although the present application provides method steps as described in an embodiment or flowchart, more or fewer steps may be included based on conventional or non-inventive means. The order of steps recited in the embodiments is merely one manner of performing the steps in a multitude of orders and does not represent the only order of execution. When an apparatus or client product in practice executes, it may execute sequentially or in parallel (e.g., in a parallel processor or multithreaded processing environment, or even in a distributed data processing environment) according to the embodiments or methods shown in the figures. The terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, the presence of additional identical or equivalent elements in a process, method, article, or apparatus that comprises the recited elements is not excluded.

The devices or modules and the like explained in the above embodiments may be specifically implemented by a computer chip or an entity, or implemented by a product with certain functions. For convenience of description, the above devices are described as being divided into various modules by functions, and are described separately. Of course, in implementing the present application, the functions of each module may be implemented in one or more pieces of software and/or hardware, or a module that implements the same function may be implemented by a combination of a plurality of sub-modules, and the like. The above-described apparatus embodiments are merely illustrative, and for example, the division of the modules is merely a logical division, and other divisions may be realized in practice, for example, a plurality of modules or components may be combined or integrated into another system, or some features may be omitted, or not executed.

Those skilled in the art will also appreciate that, in addition to implementing the controller as pure computer readable program code, the same functionality can be implemented by logically programming method steps such that the controller is in the form of logic gates, switches, application specific integrated circuits, programmable logic controllers, embedded microcontrollers and the like. Such a controller may therefore be considered as a hardware component, and the means included therein for performing the various functions may also be considered as a structure within the hardware component. Or even means for performing the functions may be regarded as being both a software module for performing the method and a structure within a hardware component.

The application may be described in the general context of computer-executable instructions, such as program modules, being executed by a computer. Generally, program modules include routines, programs, objects, components, data structures, classes, etc. that perform particular tasks or implement particular abstract data types. The application may also be practiced in distributed computing environments where tasks are performed by remote processing devices that are linked through a communications network. In a distributed computing environment, program modules may be located in both local and remote computer storage media including memory storage devices.

From the above description of the embodiments, it is clear to those skilled in the art that the present application can be implemented by software plus necessary general hardware platform. Based on such understanding, the technical solutions of the present application may be embodied in the form of a software product, which may be stored in a storage medium, such as a ROM/RAM, a magnetic disk, an optical disk, or the like, and includes several instructions for enabling a computer device (which may be a personal computer, a mobile terminal, a server, or a network device) to execute the method according to the embodiments or some parts of the embodiments of the present application.

The embodiments in the present specification are described in a progressive manner, and the same or similar parts among the embodiments are referred to each other, and each embodiment focuses on the differences from the other embodiments. The application is operational with numerous general purpose or special purpose computing system environments or configurations. For example: personal computers, server computers, hand-held or portable devices, tablet-type devices, multiprocessor systems, microprocessor-based systems, set top boxes, programmable electronic devices, network PCs, minicomputers, mainframe computers, distributed computing environments that include any of the above systems or devices, and the like.

While the present application has been described with examples, those of ordinary skill in the art will appreciate that there are numerous variations and permutations of the present application without departing from the spirit of the application, and it is intended that the appended claims encompass such variations and permutations without departing from the present application.

Claims (10)

1. A remote sensing identification method for a deposition area is characterized by comprising the following steps:

grouping adjacent remote sensing data in the remote sensing data set aiming at the target area based on the brightness to obtain a remote sensing data subset; wherein the remote sensing data subset corresponds to a sub-region of the target region; the deposition area is located within the target area; the remote sensing data is preprocessed, and the preprocessing comprises the following steps: atmospheric correction, orthorectification, image fusion, geometric correction and DN value conversion into reflectivity, wherein the brightness is a value representing the brightness of an image in the remote sensing data;

calculating at least two different characteristic values of the sub-region at least based on the remote sensing data subset; wherein the at least two different characterization values comprise spectral rule based characterization values comprising: the spectral characteristic index is used for representing the spectral characteristics of the sub-area; the spectrum rule is established by taking the spectrum reflectivity as the feature of the ground feature; the spectral characteristic index is constructed according to the typical characteristics of ground objects in red light wave bands and near infrared wave bands and the characteristic that the reduction rate of the brightness value of shadows in blue-green light wave bands is different;

screening out adjacent sub-areas which meet the conditions from the sub-areas of the target area to form at least one sedimentation area; wherein the condition is that the token values of adjacent sub-regions satisfy a threshold range of corresponding token values of the deposition region;

wherein the step of screening out eligible adjacent sub-areas from among the sub-areas of the target area to form at least one deposition area further comprises: extracting contours of the target area from the digital elevation model of the target area; and correcting the boundary of the deposition area according to the trend characteristics among the contour lines of the target area.

2. The method of claim 1, wherein the different characterization values further include at least one of:

vegetation index and soil index based on spectrum rule; the vegetation index is used for characterizing the spectral characteristics of the vegetation in the sub-region; the soil index is used to characterize the spectral characteristics of the sub-area soil.

3. The method of claim 1, wherein calculating the characterization value for the sub-region further comprises at least one of:

a characterization value based on the texture rule;

or a characterization value based on a terrain rule;

the texture rule is a ground surface texture which represents gray scale space correlation characteristics and is used as a ground feature establishing rule; the terrain rules are rules established by terrain feature factors describing terrain and landform features.

4. The method of claim 3, wherein the texture rule-based characterization values include at least one of:

texture feature energy index, texture feature contrast and texture feature correlation value;

the texture characteristic energy index represents the uniformity degree and the texture thickness of the image gray level distribution; the texture feature contrast degree characterizes the definition of an image and the depth degree of texture grooves; the texture feature correlation values characterize local gray level correlations in the image.

5. A method according to claim 3, wherein the terrain-rule based characterizing values comprise at least one of:

a terrain feature elevation value, a terrain feature slope value and a terrain feature slope angle;

wherein the topographic feature elevation characterizes a distance of the sub-area to an absolute base along a plumb line direction; the topographic feature slope value represents the degree of steepness of the sub-region; and the topographic characteristic slope angle represents the projection direction of the slope normal of the sub-region on the horizontal plane.

6. The method of claim 1, wherein the step of calculating at least two different characterizing values for the sub-region comprises spectral feature index based on spectral rules, characterizing values based on texture rules, characterizing values based on terrain rules.

7. The method of claim 1, applied to remote sensing identification of alluvial fan deposition areas, the method further comprising:

at least one of the following alluvial fan deposition characteristic values is calculated: sector area, sector perimeter, sector radius, sector horizontal axis, sector height difference and sector inclination angle.

8. A remote sensing device for identifying a deposition area, the device comprising:

the first processing unit is used for grouping adjacent remote sensing data in the remote sensing data set aiming at the target area based on the brightness to obtain a remote sensing data subset; wherein the remote sensing data subset corresponds to a sub-region of the target region; the deposition area is located within the target area; the remote sensing data is preprocessed, and the preprocessing comprises the following steps: atmospheric correction, orthorectification, image fusion, geometric correction and DN value conversion into reflectivity, wherein the brightness is a value representing the brightness of an image in the remote sensing data;

the second processing unit is used for calculating to obtain at least two different characteristic values of the sub-area at least based on the remote sensing data subset; wherein the at least two different characterization values comprise spectral rule based characterization values comprising: the spectral characteristic index is used for representing the spectral characteristics of the sub-area; the spectrum rule is established by taking the spectrum reflectivity as the feature of the ground feature; the spectral characteristic index is constructed according to the typical characteristics of ground objects in red light wave bands and near infrared wave bands and the characteristic that the reduction rate of the brightness value of shadows in blue-green light wave bands is different;

a third processing unit, configured to screen out, from the sub-regions of the target region, adjacent sub-regions that meet a condition to form at least one deposition region; wherein the condition is that the token values of adjacent sub-regions satisfy a threshold range of corresponding token values of the deposition region; wherein the step of screening out eligible adjacent sub-areas from among the sub-areas of the target area to form at least one deposition area further comprises: extracting contours of the target area from the digital elevation model of the target area; and correcting the boundary of the deposition area according to the trend characteristics among the contour lines of the target area.

9. An electronic device, applied to remote sensing identification of a deposition area, comprising: a memory and a processor;

the memory having stored therein computer instructions;

the processor is used for executing the computer instructions to realize the following steps: grouping adjacent remote sensing data in the remote sensing data set aiming at the target area based on the brightness to obtain a remote sensing data subset; wherein the remote sensing data subset corresponds to a sub-region of the target region; the deposition area is located within the target area; the remote sensing data is preprocessed, and the preprocessing comprises the following steps: atmospheric correction, orthorectification, image fusion, geometric correction and DN value conversion into reflectivity, wherein the brightness is a value representing the brightness of an image in the remote sensing data; calculating at least two different characteristic values of the sub-region at least based on the remote sensing data subset; wherein the at least two different characterization values comprise spectral rule based characterization values comprising: the spectral characteristic index is used for representing the spectral characteristics of the sub-area; the spectrum rule is established by taking the spectrum reflectivity as the feature of the ground feature; the spectral characteristic index is constructed according to the typical characteristics of ground objects in red light wave bands and near infrared wave bands and the characteristic that the reduction rate of the brightness value of shadows in blue-green light wave bands is different; screening out adjacent sub-areas which meet the conditions from the sub-areas of the target area to form at least one sedimentation area; wherein the condition is that the token values of adjacent sub-regions satisfy a threshold range of corresponding token values of the deposition region; wherein the step of screening out eligible adjacent sub-areas from among the sub-areas of the target area to form at least one deposition area further comprises: extracting contours of the target area from the digital elevation model of the target area; and correcting the boundary of the deposition area according to the trend characteristics among the contour lines of the target area.

10. A computer storage medium for remote sensing identification of a deposition area, the computer storage medium storing computer program instructions which, when executed, implement:

grouping adjacent remote sensing data in the remote sensing data set aiming at the target area based on the brightness to obtain a remote sensing data subset; wherein the remote sensing data subset corresponds to a sub-region of the target region; the deposition area is located within the target area; the remote sensing data is preprocessed, and the preprocessing comprises the following steps: atmospheric correction, orthorectification, image fusion, geometric correction and DN value conversion into reflectivity, wherein the brightness is a value representing the brightness of an image in the remote sensing data;

calculating at least two different characteristic values of the sub-region at least based on the remote sensing data subset; wherein the at least two different characterization values comprise spectral rule based characterization values comprising: the spectral characteristic index is used for representing the spectral characteristics of the sub-area; the spectrum rule is established by taking the spectrum reflectivity as the feature of the ground feature; the spectral characteristic index is constructed according to the typical characteristics of ground objects in red light wave bands and near infrared wave bands and the characteristic that the reduction rate of the brightness value of shadows in blue-green light wave bands is different;

screening out adjacent sub-areas which meet the conditions from the sub-areas of the target area to form at least one sedimentation area; wherein the condition is that the token values of adjacent sub-regions satisfy a threshold range of corresponding token values of the deposition region; wherein the step of screening out eligible adjacent sub-areas from among the sub-areas of the target area to form at least one deposition area further comprises: extracting contours of the target area from the digital elevation model of the target area; and correcting the boundary of the deposition area according to the trend characteristics among the contour lines of the target area.

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