CN111738916A - Remote sensing image generalized shadow spectrum reconstruction method and system based on statistics - Google Patents
Remote sensing image generalized shadow spectrum reconstruction method and system based on statistics Download PDFInfo
- Publication number
- CN111738916A CN111738916A CN202010846356.2A CN202010846356A CN111738916A CN 111738916 A CN111738916 A CN 111738916A CN 202010846356 A CN202010846356 A CN 202010846356A CN 111738916 A CN111738916 A CN 111738916A
- Authority
- CN
- China
- Prior art keywords
- shadow
- remote sensing
- reconstruction
- generalized
- spectral
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
- 238000001228 spectrum Methods 0.000 title claims abstract description 100
- 238000000034 method Methods 0.000 title claims abstract description 57
- 230000003595 spectral effect Effects 0.000 claims abstract description 143
- 230000005855 radiation Effects 0.000 claims abstract description 57
- 230000005540 biological transmission Effects 0.000 claims abstract description 20
- 238000000354 decomposition reaction Methods 0.000 claims abstract description 19
- 230000009466 transformation Effects 0.000 claims description 36
- 238000013507 mapping Methods 0.000 claims description 33
- 239000000126 substance Substances 0.000 claims description 12
- 238000009795 derivation Methods 0.000 claims description 11
- 238000004422 calculation algorithm Methods 0.000 claims description 9
- 238000000605 extraction Methods 0.000 claims description 8
- 238000013519 translation Methods 0.000 claims description 7
- 230000000694 effects Effects 0.000 abstract description 36
- 230000007704 transition Effects 0.000 abstract description 12
- 230000008901 benefit Effects 0.000 abstract description 9
- 238000012937 correction Methods 0.000 description 40
- 230000006870 function Effects 0.000 description 16
- 238000011160 research Methods 0.000 description 11
- 239000013598 vector Substances 0.000 description 10
- 238000001914 filtration Methods 0.000 description 8
- 238000002310 reflectometry Methods 0.000 description 8
- 230000003287 optical effect Effects 0.000 description 7
- 238000001514 detection method Methods 0.000 description 6
- 238000010586 diagram Methods 0.000 description 6
- 238000012986 modification Methods 0.000 description 5
- 230000004048 modification Effects 0.000 description 5
- 230000008569 process Effects 0.000 description 5
- 241000132092 Aster Species 0.000 description 4
- 238000005516 engineering process Methods 0.000 description 4
- 238000012545 processing Methods 0.000 description 4
- 230000007547 defect Effects 0.000 description 3
- 230000007246 mechanism Effects 0.000 description 3
- 206010027146 Melanoderma Diseases 0.000 description 2
- 238000004458 analytical method Methods 0.000 description 2
- 239000000284 extract Substances 0.000 description 2
- 230000010354 integration Effects 0.000 description 2
- 238000005192 partition Methods 0.000 description 2
- 238000011426 transformation method Methods 0.000 description 2
- 238000002834 transmittance Methods 0.000 description 2
- 241000287196 Asthenes Species 0.000 description 1
- 241000212384 Bifora Species 0.000 description 1
- 241000764238 Isis Species 0.000 description 1
- 241000219000 Populus Species 0.000 description 1
- 230000002411 adverse Effects 0.000 description 1
- 230000004075 alteration Effects 0.000 description 1
- 238000013398 bayesian method Methods 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000004364 calculation method Methods 0.000 description 1
- 230000001419 dependent effect Effects 0.000 description 1
- 230000008030 elimination Effects 0.000 description 1
- 238000003379 elimination reaction Methods 0.000 description 1
- 238000002474 experimental method Methods 0.000 description 1
- 238000005286 illumination Methods 0.000 description 1
- 230000003993 interaction Effects 0.000 description 1
- 239000000463 material Substances 0.000 description 1
- 230000000877 morphologic effect Effects 0.000 description 1
- 235000008001 rakum palm Nutrition 0.000 description 1
- 238000011084 recovery Methods 0.000 description 1
- 238000000611 regression analysis Methods 0.000 description 1
- 230000008439 repair process Effects 0.000 description 1
- 230000011218 segmentation Effects 0.000 description 1
- 238000013179 statistical model Methods 0.000 description 1
Images
Classifications
-
- G06T3/04—
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/10—Image acquisition modality
- G06T2207/10032—Satellite or aerial image; Remote sensing
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/20—Special algorithmic details
- G06T2207/20112—Image segmentation details
- G06T2207/20132—Image cropping
Abstract
The invention discloses a remote sensing image generalized shadow spectrum reconstruction method and a system based on statistics.A remote sensing image data to be subjected to spectrum reconstruction is obtained, and is subjected to radiometric calibration and converted into radiometric data, and meanwhile, a spectrum reconstruction area to be subjected to data cutting is carried out; performing mixed spectrum decomposition on the spectrum reconstruction area subjected to data cutting, and extracting shadow basis components; according to a radiation transmission theory, deducing a generalized shadow consistency spectrum reconstruction equation; and acquiring spectral parameters based on statistical equation estimation to realize the generalized shadow spectral reconstruction of the remote sensing image to be subjected to spectral reconstruction. The method breaks through the classical definition of the traditional remote sensing image shadow, deduces the spectral reconstruction equation of the generalized shadow consistency, and has the advantages of natural color transition, more prominent texture, no edge effect and very obvious effect of the reconstruction result; the fidelity of the information recorded by the image data is kept to the maximum extent.
Description
Technical Field
The invention relates to the technical field of remote sensing image data processing, and particularly discloses a statistics-based remote sensing image generalized shadow spectrum reconstruction method and system.
Background
The correction or spectral reconstruction of the optical remote sensing image data shadow belongs to the research of remote sensing mechanism and method in the national science foundation classification catalogue, and the classification code is D0106; in the concrete research content, the method belongs to remote sensing image processing and enhancement. The existence of the shadow in the remote sensing image data can cause the loss of partial or all information, and the optical characteristic information in the image is weakened, so that the subsequent remote sensing interpretation and information extraction are influenced, and the method is particularly applied to the quantitative remote sensing inversion based on a reflectivity spectrum curve, the hidden target tracking, the image multi-scale segmentation and the like.
Classical definition of optical remote sensing image shadows: shadows are common in scenes where objects obstruct all or part of the direct light in the light source (Arevalo et al, 2008); analyzing from the optical cause type of the shadow, the shadow can be divided into two main categories: self-shadow is the portion of the terrain itself not illuminated by direct light, generally with a higher brightness than projection (Dare, 2005); the projection is a part of the ground object which shields the direct light projection of the light source, and is further divided into a home shadow in which all the direct light is shielded and a penumbra in which part of the direct light is shielded (Arevalo et al, 2005). From the on-ground analysis of the terrain causing the optical remote sensing image shadow, the shadow is divided into a terrain shadow, an urban (building) shadow, a cloud shadow, and a mixed shadow (Shahtamassebi et al, 2013).
In view of the important role, particularly adverse effects, played by the optical remote sensing image shadow in the multi-field remote sensing scientific research and application, many scholars at home and abroad (Nakajinma et al, 2002; Sarabandi et al, 2004; Chen et al, 2007; Arevalo et al, 2008; Arora and Mathur, 2001; rieno et al, 2003; Sala et al, 2005; Yang et al, 2007; richter et al, 2009; Simpson and titt, 1998; Arellano, 2003; warm mega fly et al, 2016; tsunami et al, 2006; sudang et al, 2017; rei et al, 2018; populus et al, 2012; yuexi et al, 2018; bellar et al, 2006; gajun et al, 2012) have paid enormous efforts to research the forming mechanism, detection method, removal or recovery technology, and have been achieved at the same time as the image shadow correction technology. The shadow detection-correction methods can be roughly classified into three types of terrain shadow correction, urban building shadow correction and cloud shadow correction from the shadow category, and can be roughly classified into a statistical model, a physical model, a geoscience data space autocorrelation model, an auxiliary data dependent model and a mixed model from the characteristic attribute of a correction model.
The research on the correction of the remote sensing image shadow in the complex terrain area is early, many of the remote sensing image shadows belong to a physical model and a mixed model, the research focuses on mechanism research, modeling correction is carried out by researching the physical process of interaction between electromagnetic waves and the earth surface under different solar illumination conditions, and two important assumptions are as follows: in 1989, the earth surface is assumed to be a lambertian body, and the total radiation of pixels received by a sensor is divided into path radiation of direct solar radiation reflected by a ground object, downward scattered radiation reflected by atmosphere, light radiation reflected by surrounding environment and upward scattered radiation reflected by atmosphere; secondly, Sandmeier et al assume that the earth surface is a non-Lambert body, and further divide the uplink radiation of the pixel into uplink radiation reflected by the pixel through direct light, uplink radiation reflected by the pixel through various anisotropic scattered lights around the sun, uplink radiation reflected by various isotropic scattered lights through the pixel, and uplink radiation reflected by the adjacent pixel through the pixel. The calibration model can be roughly summarized into a method based on a wave band ratio, a method based on DEM, multi-source and multi-temporal auxiliary data and a plurality of geoscience data space autocorrelation methods developed in recent years, wherein the research result of the calibration method based on DEM is very abundant, and the calibration method based on DEM can be roughly divided into a statistical-empirical model, a normalized model, a Lambert body reflectivity model and a non-Lambert body reflectivity model (Gaoyongnian and Zhang Wanchang, 2008). The band ratio method is the simplest method for eliminating terrain shadows, and can reduce the influence of the terrain shadows to a certain extent, but the effect is usually limited, and particularly when the surface coverage has similar reflection characteristics, the albedo difference becomes blurred. In the method based on DEM auxiliary data, the statistical-empirical model mainly comprises a Tellet-regression correction (Tellet et al, 1982), a b correction and a variable empirical coefficient method (VECA) and the like; the normalized model is more classical 2-stage correction method proposed in 1989 by Civco et al; lambertian bulk reflectance models mainly include Cosin correction (Ekstrand et al, 1996), C correction (Tokola et al, 2001), Cosin-T model, Cosin-C model, Cosin-b model, SCS model (Gu and Gillespie et al, 1998), SCS + C model (Soenen et al, 2005), and the like; non-lambertian body reflectance models include primarily minnart correction (Bishop et al, 2003), Ekstrand-e model (Ekstrand et al, 1996), and minnart-SCS model (Vincini et al, 2002), among others. The method is based on the spatial autocorrelation of geological data, the spatial autocorrelation of the pixels of the geological features in the nature is utilized to model and restore weak spectral information of a shadow area, for example, the fact that the zhangtian and the like carry out information reconstruction on the hilly area by utilizing an envelope elimination method (zhangtian and the like, 2017), the fact that the Yang Qiyong and the like adopt a Kriging method to repair the mountain shadow area of a remote sensing image of the hilly area (Yang Qiyong and the like, 2012) all obtain better effects, but the surrounding similar pixels are used for replacing the corresponding pixels of the shadow area, and the geological information of the shadow area can be distorted. Considering that most terrain correction models mainly aim at influences caused by different incident angles of pixel direct solar rays due to terrain fluctuation, the terrain correction models have obvious effect on areas irradiated by the direct solar rays, and when the terrain correction models act on shadow areas without direct solar ray irradiation, more problems of overcorrection, undercorrection and the like often exist, and the shadow area information needs to be compensated first and then corrected. The scholars such as Yueyinje and Linxi use DEM data to classify shadows of mountain regions in 2018 in detail, the 8 types are divided, different models are adopted to correct the shadows according to different types of the shadows, shadow detection-compensation is adopted for areas without direct light irradiation, and then correction is carried out by using a scheme of a terrain correction model (Yueyinje and Linxi, and the like, 2018), so that a good effect is achieved, but the model is high in complexity and large in implementation difficulty. The terrain correction model based on DEM auxiliary data has good applicability in many scenes, plays an important role in the practical application of the remote sensing technology, but has some limitations and disadvantages: the correction accuracy depends on the quality, completeness and spatial resolution of the DEM; registration error between the remote sensing image and the DEM; the high-precision DEM data acquisition cost is high, and data in many regions are lost; the phenomena of local over-correction and under-correction easily occur in the area with the solar incident angle close to 90 degrees, and the method is not suitable for complex terrain areas.
In addition, many excellent models have been developed for correcting the shadow of the high-resolution remote sensing image in the urban area, and the classical models include a histogram matching method (Dare, 2005; Sarabandi et al, 2004), a gamma correction (Nakajima et al, 2002), a mean variance transformation Method (MVT) (Chen et al, 2007; Zhan et al, 2005; Shahtahmasebi et al, 2013), and the like. The method has the advantages of simplicity, high efficiency and easy realization, obtains good application effect in a plurality of scenes, proves the practicability, and has the defects that the physical significance of parameters is unknown and the functions can be realized only under the condition that certain prior hypothesis is established. The detection and removal of cloud shadow are more common solutions such as radar microwave remote sensing, semi-physical models, wavelet transformation, hybrid models and multi-source and multi-temporal data methods, and the basic idea is to detect a mask firstly by using spectral characteristic attributes or morphological characteristics of the shadow and then remove the mask. Arellano researches on the removal of cloud shadows by using radar data in 2003, the main problem is that microwave signals are weak, so that image resolution is low, Roy and Jin research on the removal of cloud shadows of MODIS and Landsat ETM + data by using semi-physical models in 2008 and 2013 respectively, Carvalho removes the shadows by using a nonlinear wavelet regression analysis method in 2001, and has better robustness, and Richter and Muller research on the removal of the cloud shadows by using a multi-end spectral decomposition idea in 2005. The method has good effect in each scene, but still has the problems that the geometric homography of multi-source data, the calculation of the field angle of the sensor, the correction of solar spectrum and the like are difficult to solve.
Therefore, the above-mentioned defects existing in the existing remote sensing image shadow correction are a technical problem to be solved urgently.
Disclosure of Invention
The invention provides a method and a system for reconstructing a spectrum of a generalized shadow of a remote sensing image based on statistics, and aims to solve the technical problem of the defects in the existing shadow correction of the remote sensing image.
The invention relates to a remote sensing image generalized shadow spectrum reconstruction method based on statistics, which comprises the following steps:
obtaining remote sensing image data to be subjected to spectral reconstruction, carrying out radiometric calibration on the remote sensing image data, converting the remote sensing image data into radiometric data, and simultaneously carrying out data cutting on a region to be subjected to spectral reconstruction;
performing mixed spectrum decomposition on the spectral reconstruction region after data cutting, and extracting shadow base components
；
According to a radiation transmission theory, deducing a generalized shadow consistency spectrum reconstruction equation R (lambda, x, y);
and acquiring spectral parameters based on MVT statistical equation estimation, and realizing the generalized shadow spectral reconstruction of the remote sensing image to be subjected to spectral reconstruction.
Further, the step of performing mixed spectrum decomposition on the spectral reconstruction region after data clipping and extracting shadow basis components comprises:
the shadow basis components are extracted using the SMACC algorithm.
Further, according to the radiation transmission theory, the step of deriving the generalized shadow consistency spectral reconstruction equation R (λ, x, y) includes:
establishing an MVT statistical equation under the classical definition of the remote sensing image shadow;
for the extracted shadow base component
Performing mathematical transformation and mapping to obtain shadow component map of each band
。
Further, the generalized shadow consistency spectral reconstruction equation R (λ, x, y) is derived as:
wherein the content of the first and second substances,
representing picture elements
Radiance after spectral reconstruction
Representing an initial radiance;
representing picture elements
(ii) each band shadow component;
representing the range radiation as a constant term;
shadow component of each band
Comprises the following steps:
wherein the content of the first and second substances,
representing a shadow base component
Scaling function of
Representing a transformation parameter;
representing the curved surface translation factor after transformation;
shadow base component
Mapping to band-shadow components
The mapping function used is:
wherein the content of the first and second substances,
representing a shadow base component
Scaling function of
Representing a transformation parameter;
a classic shaded area is shown and is,
a classical non-shaded region is indicated.
Further, the step of obtaining the spectrum parameters based on the MVT statistical equation estimation and realizing the generalized shadow spectrum reconstruction of the remote sensing image to be subjected to spectrum reconstruction comprises the following steps:
obtaining spectral parameters based on MVT statistical equation estimation;
and substituting the acquired spectral parameters into a generalized shadow consistency spectral reconstruction equation R (lambda, x, y) to obtain a generalized shadow spectral reconstruction result of the remote sensing image to be subjected to spectral reconstruction.
Another aspect of the present invention relates to a system for reconstructing a spectrum of a generalized shadow of a remote sensing image based on statistics, comprising:
the cutting module is used for acquiring remote sensing image data to be subjected to spectral reconstruction, carrying out radiometric calibration on the remote sensing image data, converting the remote sensing image data into radiometric data, and simultaneously carrying out data cutting on a spectral reconstruction area to be subjected to spectral reconstruction;
an extraction module for performing mixed spectrum decomposition on the spectrum reconstruction region after data clipping to extract shadow basis component
；
The derivation module is used for deriving a generalized shadow consistency spectrum reconstruction equation R (lambda, x, y) according to a radiation transmission theory;
and the reconstruction module is used for acquiring spectral parameters based on MVT statistical equation estimation and realizing the generalized shadow spectral reconstruction of the remote sensing image to be subjected to spectral reconstruction.
Further, the extraction module comprises:
and the extracting unit is used for extracting the shadow base component by adopting an SMACC algorithm.
Further, the derivation module includes:
the establishing unit is used for establishing an MVT statistical equation under the classical definition of the remote sensing image shadow;
a mapping unit for mapping the extracted shadow base component
Performing mathematical transformation and mapping to obtain shadow component map of each band
。
Further, the generalized shadow consistency spectral reconstruction equation R (λ, x, y) is derived as:
wherein the content of the first and second substances,
representing picture elements
Radiance after spectral reconstruction
Representing an initial radiance;
representing picture elements
Shadow component of each band
Representing the range radiation as a constant term;
shadow component of each band
Comprises the following steps:
wherein the content of the first and second substances,
representing a shadow base component
Scaling function of
Representing a transformation parameter;
representing the curved surface translation factor after transformation;
shadow base component
Mapping to band-shadow components
The mapping function used is:
wherein the content of the first and second substances,
representing a shadow base component
A scaling transformation function of (a);
representing a transformation parameter;
a classic shaded area is shown and is,
a classical non-shaded region is indicated.
Further, the reconstruction module includes:
the acquisition unit is used for acquiring spectral parameters based on MVT statistical equation estimation;
and the substituting unit is used for substituting the acquired spectral parameters into a generalized shadow consistency spectral reconstruction equation R (lambda, x, y) to obtain a generalized shadow spectral reconstruction result of the remote sensing image to be subjected to spectral reconstruction.
The beneficial effects obtained by the invention are as follows:
the invention provides a remote sensing image generalized shadow spectrum reconstruction method and system based on statistics.A remote sensing image data to be subjected to spectrum reconstruction is obtained, and the remote sensing image data is subjected to radiometric calibration and converted into radiometric data, and meanwhile, data cutting is carried out on a spectrum reconstruction area to be subjected to; performing mixed spectrum decomposition on the spectrum reconstruction area subjected to data cutting, and extracting shadow basis components; according to a radiation transmission theory, deducing a generalized shadow consistency spectrum reconstruction equation; and acquiring spectral parameters based on statistical equation estimation to realize the generalized shadow spectral reconstruction of the remote sensing image to be subjected to spectral reconstruction. The remote sensing image generalized shadow spectrum reconstruction method and system based on statistics break through the classical definition of the traditional remote sensing image shadow, derive a new generalized shadow consistency spectrum reconstruction equation, have natural color transition of the reconstruction result, have more prominent texture and no edge effect, and have very obvious effect; for a small amount of ground information missing areas in the image, complete automatic filtering is realized, error correction of pixels in the information missing areas is eliminated, and the fidelity of the recorded information of the image data is maintained to the maximum extent.
Drawings
FIG. 1 is a schematic flow chart of an embodiment of a method for reconstructing a spectrum of a generalized shadow of a remote sensing image based on statistics according to the present invention;
FIG. 2 is a schematic view of a detailed flowchart of an embodiment of the step of deriving the generalized shadow coherence spectral reconstruction equation shown in FIG. 1 according to the radiation transmission theory;
FIG. 3 is a schematic view of a detailed flow chart of an embodiment of the step of obtaining spectral parameters based on statistical equation estimation and implementing the spectral reconstruction of the generalized shadow of the remote sensing image to be spectrally reconstructed shown in FIG. 1;
FIG. 4 is an initial image of the ASTER image generalized shadow spectral reconstruction of the present invention;
FIG. 5 is a diagram of experimental theoretical results of the ASTER image generalized shadow spectral reconstruction of the present invention;
FIG. 6 is a functional block diagram of an embodiment of a system for reconstructing a generalized shadow spectrum of a remote sensing image based on statistics according to the present invention;
FIG. 7 is a functional block diagram of an embodiment of the derivation module shown in FIG. 6;
fig. 8 is a functional block diagram of an embodiment of the reconstruction module shown in fig. 6.
The reference numbers illustrate:
10. a cutting module; 20. an extraction module; 30. a derivation module; 40. a reconstruction module; 31. a building unit; 32. a mapping unit; 41. an acquisition unit; 42. and substituting the unit.
Detailed Description
In order to better understand the technical solution, the technical solution will be described in detail with reference to the drawings and the specific embodiments.
As shown in fig. 1, a first embodiment of the present invention provides a method for reconstructing a spectrum of a generalized shadow of a remote sensing image based on statistics, which includes the following steps:
and S100, obtaining remote sensing image data to be subjected to spectral reconstruction, carrying out radiometric calibration on the remote sensing image data, converting the remote sensing image data into radiometric data, and simultaneously carrying out data cutting on a spectral reconstruction region to be subjected to spectral reconstruction.
And acquiring remote sensing image data to be subjected to spectral reconstruction, carrying out radiometric calibration on the remote sensing image data, converting the remote sensing image data to be subjected to spectral reconstruction into radiometric data, and simultaneously carrying out data cutting on a region to be subjected to spectral reconstruction, and recording the data as I.
And S200, performing mixed spectrum decomposition on the spectrum reconstruction area subjected to data cutting, and extracting shadow basis components.
Performing mixed spectrum decomposition on the I by adopting an SMACC (sequential MaximumAngle ConvexCone) algorithm, extracting shadow basis components, and recording the shadow basis components as
。
And S300, deriving a generalized shadow consistency spectrum reconstruction equation according to a radiation transmission theory.
According to the radiation transmission theory, a generalized shadow consistency spectrum reconstruction equation is deduced
。
And S400, acquiring spectral parameters based on statistical equation estimation, and realizing the generalized shadow spectral reconstruction of the remote sensing image to be subjected to spectral reconstruction.
And acquiring spectral parameters based on MVT statistical equation estimation, and realizing the generalized shadow spectral reconstruction of the remote sensing image to be subjected to spectral reconstruction.
The remote sensing image generalized shadow spectrum reconstruction method based on statistics provided by the embodiment has the following working principle:
most of the traditional remote sensing image shadow correction methods are based on the classical definition of traditional shadows, firstly, various characteristics caused by the shadows are used for detection, then different models are used for partition processing according to different scenes, and as the shadows have transition regions, the problems of block edge effect, local overcorrection and undercorrection are inevitably caused. In fact, the nature does not have an absolutely ideal mirror image ground, and on the contrary, the ground is rough and uneven, so that shading effects of different degrees are necessarily existed, therefore, the generalized shadow should exist in any one pixel in the remote sensing image, and only the components of the shadow of different pixels are different. Consider such a scenario: in the non-shadow region of the low-resolution remote sensing image, a corresponding high-resolution image may have a classical shadow pixel, so that, in a broad sense, a corresponding pixel in the low-resolution remote sensing image cannot be defined as the non-shadow region, but the shadow component is small, and the caused effect is not enough to define the image as the classical shadow region, which is caused by the scale effect. According to the embodiment, a mathematical basis component M (x, y) for uniformly and quantitatively describing generalized shadows in the remote sensing images is extracted from a single-scene remote sensing image by adopting an SMACC algorithm according to a mixed spectrum decomposition principle, then a segmented mapping function is established to map the mathematical basis component M (x, y) into shadow components S (lambda, x, y) with different wave bands, finally a generalized shadow consistency spectrum reconstruction equation R (lambda, x, y) is deduced according to a radiation transmission theory, equation parameters of R (lambda, x, y) are estimated based on an MVT statistical equation, and the generalized shadow spectrum reconstruction of the multi/hyperspectral remote sensing image is realized.
Compared with the prior art, the remote sensing image generalized shadow spectrum reconstruction method based on statistics is characterized in that remote sensing image data to be subjected to spectrum reconstruction are obtained, radiometric calibration is carried out on the remote sensing image data, the remote sensing image data are converted into radiometric data, and meanwhile data cutting is carried out on a spectral reconstruction area to be subjected; performing mixed spectrum decomposition on the spectrum reconstruction area subjected to data cutting, and extracting shadow basis components; according to a radiation transmission theory, deducing a generalized shadow consistency spectrum reconstruction equation; and acquiring spectral parameters based on statistical equation estimation to realize the generalized shadow spectral reconstruction of the remote sensing image to be subjected to spectral reconstruction. The remote sensing image generalized shadow spectrum reconstruction method based on statistics breaks through the classical definition of the traditional remote sensing image shadow, deduces a new generalized shadow consistency spectrum reconstruction equation, and has the advantages of natural color transition, more prominent texture, no edge effect and very obvious effect of the reconstruction result; for a small amount of ground information missing areas in the image, complete automatic filtering is realized, error correction of pixels in the information missing areas is eliminated, and the fidelity of the recorded information of the image data is maintained to the maximum extent.
Further, please refer to fig. 2, fig. 2 is a schematic detailed flowchart of an embodiment of step S300 shown in fig. 1, in this embodiment, step S300 includes:
and S310, establishing a statistical equation under the classical definition of the remote sensing image shadow.
And establishing an MVT statistical equation under the classical definition of the remote sensing image shadow, wherein the parameters of the MVT statistical equation are given according to the remote sensing image shadow detection method of energy integration.
Step S320, performing mathematical transformation on the extracted shadow basis component, and mapping the shadow basis component into a shadow component map of each band.
For the extracted shadow base component
Performing mathematical transformation and mapping to obtain shadow component map of each band
. And deducing a generalized shadow consistency spectrum reconstruction equation R (lambda, x, y) according to a radiation transmission theory.
The mathematical principle of deriving the generalized shadow consistency spectral reconstruction equation R (λ, x, y) according to the radiation transmission theory is as follows:
a. in this embodiment, a generalized shadow consistency spectral reconstruction equation is constructed under the assumption of Proy:
proy assumes that: the earth surface is a lambertian body, and the total radiation of the pixels received by the sensor is divided into direct solar radiation reflected by the ground objects, downward scattered radiation reflected by the atmosphere, light radiation reflected by the surrounding environment and upward scattered path radiation reflected by the atmosphere.
Under completely ideal conditions, the sun is irradiated by all direct rays, and the reflectivity of the ground object X is as follows:
in the formula (1), the first and second groups,
represents the surface feature X reflectivity;
representing apparent brightness of corresponding pixel of sensor
Representing atmospheric range radiation;
represents the atmospheric radiation transmittance along the observation direction of the sensor;
representing the direct solar radiation received by the ground object X;
representing the atmospheric downlink scattered optical radiation received by X,
representing the ambient radiation received by X.
Under natural conditions, only part of the direct sunlight is irradiated due to the mutual shielding effect of the ground objects, and the reflectivity of the ground object X is
In the formula (2), the first and second groups,
represents the surface feature X reflectivity;
representing the apparent brightness of the corresponding pixel of the sensor;
representing atmospheric range radiation;
represents the atmospheric radiation transmittance along the observation direction of the sensor;
representing the direct solar radiation received by the ground object X,
；
representing atmospheric scattered light radiation received by the terrain X,
representing the ambient radiation received by X.
The feature reflectivity is a material property of the feature, and is constant, and formula (1) = formula (2).
Is a constant term, within a certain spatial range, instantaneous
、
Approximately constant, we derive:
order to
Then equation (3) transforms to:
as can be seen from the formula (4), the apparent radiance of the shadow area pixel can be restored by linear transformation,
the mean variance transformation method assumes that pixels in a shadow area and pixels in a non-shadow area have the same mean and variance, and the core equation is as follows:
in the formula (5)
Is a shadow regionThe radiation brightness after pixel correction;
、
、
、
statistical mean and standard deviation of the shaded and unshaded regions, respectively.
The MVT method does not take into account the spatial variability of the shadow components, assuming that k is constant. Under the thought framework of generalized shadow, k is no longer constant, shadow components exist in any pixel, and the formula (4) needs to be further extended. Considering picture elements
In the wavelength band of the wavelength λ, its shadow component is assumed to be
Then equation (4) is transformed into
In the formula (5), the first and second groups,
representing picture elements
Radiance after spectral reconstruction
Representing an initial radiance;
to representPixel element
A shadow component;
representing the range radiation as a constant term.
Equation (5) is named as the generalized shadow consistency spectral reconstruction equation, where,
for the shadow component function, the key to solving equation (5) is
Is estimated.
Two, pair the extracted shadow base component
Performing mathematical transformation and mapping to obtain shadow component map of each band
The principle of (1) is as follows:
a. the embodiment adopts the mapping of shadow base components extracted by the mixed spectrum decomposition technology to the shadow components of each wave band
In the process of decomposing the remote sensing image mixed spectrum, the shadow can be treated as a separate 'spectrum end member' due to the unique attribute of the shadow. In the continuous maximum Convex pyramid (SMACC) model, the continuous maximum Convex pyramid (SMACC) is extracted as a 'Null Vector' connecting the Convex pyramid (A Convex Cone) model and the Convex bag model (A Convex Hull), representing the total shadow components, and in addition, a Bayesian method, a linear spectral decomposition model (MESMA) and the like can be used for obtaining a component map of the shadow 'spectral end members'. Obviously, in the remote sensing image, the size of the shadow component changes with different wavelengths, and the shadow component cannot be directly used as the shadow component of the consistent spectral reconstruction equation. In the embodiment, the SMACC algorithm is adopted to extract the shadow basis component from the remote sensing image, and the shadow basis component is recorded as
Then the shadow component is mapped into a shadow component graph of each wave band through mathematical transformation and is recorded as
。
The present embodiment considers a simple mathematical mapping model:
the value interval of the element is (0, 1), the geometric form is an uneven curved surface between a 0 plane and a 1 plane in a three-dimensional rectangular coordinate system, the mapping process is a process of stretching and translating the curved surface in the interval (0, 1) in the z-axis direction, and different functions can be considered in the stretching process, such as stretching
、
Etc., as follows.
In the formula (6), the first and second groups,
representing a shadow base component
A scaling transformation function of (a);
representing transformation parameters
Representing the transformed surface translation factor.
Substituting the formula (6) into the formula (5), and solvingIs most preferred
、
And
。
shadow base component
Mapping to band-shadow components
The mapping function used is:
in the formula (7), the first and second groups,
representing a shadow base component
A scaling transformation function of (a);
representing a transformation parameter;
represents a classical shadow region;
a classical non-shaded region is indicated.
Compared with the prior art, the remote sensing image generalized shadow spectrum reconstruction method based on statistics establishes a statistical equation under the classical definition of the remote sensing image shadow; and performing mathematical transformation on the extracted shadow basis components, and mapping into a shadow component map of each wave band. The remote sensing image generalized shadow spectrum reconstruction method based on statistics breaks through the classical definition of the traditional remote sensing image shadow, deduces a new generalized shadow consistency spectrum reconstruction equation, and has the advantages of natural color transition, more prominent texture, no edge effect and very obvious effect of the reconstruction result; for a small amount of ground information missing areas in the image, complete automatic filtering is realized, error correction of pixels in the information missing areas is eliminated, and the fidelity of the recorded information of the image data is maintained to the maximum extent.
Preferably, referring to fig. 3, fig. 3 is a schematic view of a detailed flow of an embodiment of step S400 shown in fig. 1, in this embodiment, step S400 includes:
and S410, obtaining spectral parameters based on statistical equation estimation.
Equation parameter estimation based on statistics: in the data of the example, the slope vector of the MVT equation parameter
And intercept vector
The vectors are as follows:
range radiation
According to equation (4), the vector in the experiment
Calculated using the formula:
mapping function parameters
，
Satisfies the following formula:
in formula (11)
Represents a mathematical expectation;
representing base surfaces of shadow components
The classical shadow part of (a), is obviously an underdetermined equation,
、
the experimental data are as follows:
shadow base curve
After the piecewise function transformation, 5 × 5 low-pass filtering is carried out to obtain the shadow component curved surface of each wave band
Translation vector of curved surface
Solved by the following equation:
in the formula (14), the first and second groups,
representing the shadow component curved surface of each wave band;
statistical maximum of classical shadow region;
representing the translation vector of the surface.
The concrete solving results are as follows:
and step S420, substituting the acquired spectral parameters into a generalized shadow consistency spectral reconstruction equation to obtain a generalized shadow spectral reconstruction result of the remote sensing image to be subjected to spectral reconstruction.
Substituting the acquired spectral parameters into the generalized shadow consistency spectral reconstruction equation of the formula (5) to obtain a generalized shadow spectral reconstruction result of the remote sensing image to be subjected to spectral reconstruction, wherein initial image and experimental reconstruction result graphs of the ASTER image generalized shadow spectral reconstruction are respectively shown in FIG. 4 and FIG. 5.
Compared with the traditional remote sensing image data shadow correction method, as can be clearly seen from fig. 5, most of shadow region information of the image is restored to the result approximately consistent with the non-shadow region by the method provided by the embodiment, the color transition is natural, the texture is more prominent, no edge effect exists, and the effect is very obvious. Meanwhile, for a small number of 'black spot areas', because the pixel reflection radiation is very weak and is close to the '0' reflection pixel, the data recorded by the sensor is similar to the atmospheric upstroke radiation noise, which is equivalent to a ground information missing area, the data can be identified, the reconstruction of false information is eliminated, and the fidelity of the information recorded by the image data is maintained to the maximum extent.
Compared with the prior art, the remote sensing image generalized shadow spectrum reconstruction method based on statistics obtains spectrum parameters through MVT statistical equation estimation; and substituting the acquired spectral parameters into the generalized shadow consistency spectral reconstruction equation to obtain a generalized shadow spectral reconstruction result of the remote sensing image to be subjected to spectral reconstruction. The remote sensing image generalized shadow spectrum reconstruction method based on statistics breaks through the classical definition of the traditional remote sensing image shadow, deduces a new generalized shadow consistency spectrum reconstruction equation, and has the advantages of natural color transition, more prominent texture, no edge effect and very obvious effect of the reconstruction result; for a small amount of ground information missing areas in the image, complete automatic filtering is realized, error correction of pixels in the information missing areas is eliminated, and the fidelity of the recorded information of the image data is maintained to the maximum extent.
As shown in fig. 6, the invention further provides a statistics-based remote sensing image generalized shadow spectrum reconstruction system, which includes a clipping module 10, an extraction module 20, a derivation module 30 and a reconstruction module 40, wherein the clipping module 10 is configured to obtain remote sensing image data to be subjected to spectrum reconstruction, perform radiometric calibration on the remote sensing image data, convert the remote sensing image data into radiometric data, and perform data clipping on a spectral reconstruction region to be subjected to spectrum reconstruction; the extraction module 20 is configured to perform mixed spectrum decomposition on the spectrum reconstruction region after the data clipping, and extract a shadow basis component; the derivation module 30 is configured to derive a generalized shadow consistency spectrum reconstruction equation according to a radiation transmission theory; and the reconstruction module 40 is used for acquiring spectral parameters based on statistical equation estimation and realizing the generalized shadow spectral reconstruction of the remote sensing image to be subjected to spectral reconstruction.
The cutting module 10 obtains remote sensing image data to be subjected to spectral reconstruction, performs radiometric calibration on the remote sensing image data, converts the remote sensing image data to be subjected to spectral reconstruction into radiometric data, and performs data cutting on a region to be subjected to spectral reconstruction, and records the data as I.
The extracting module 20 performs mixed spectrum decomposition on the I by using an SMACC (sequential maximum angle convex cone analysis) algorithm, extracts a shadow basis component, and records the shadow basis component as
。
The derivation module 30 derives the generalized shadow consistency spectrum reconstruction equation according to the radiation transmission theory
。
The reconstruction module 40 obtains spectral parameters based on the MVT statistical equation estimation, and realizes the spectral reconstruction of the generalized shadow of the remote sensing image to be subjected to spectral reconstruction.
The remote sensing image generalized shadow spectrum reconstruction system based on statistics provided by the embodiment has the following working principle:
most of the traditional remote sensing image shadow correction methods are based on the classical definition of traditional shadows, firstly, various characteristics caused by the shadows are used for detection, then different models are used for partition processing according to different scenes, and as the shadows have transition regions, the problems of block edge effect, local overcorrection and undercorrection are inevitably caused. In fact, the nature does not have an absolutely ideal mirror image ground, and on the contrary, the ground is rough and uneven, so that shading effects of different degrees are necessarily existed, therefore, the generalized shadow should exist in any one pixel in the remote sensing image, and only the components of the shadow of different pixels are different. Consider such a scenario: in the non-shadow region of the low-resolution remote sensing image, a corresponding high-resolution image may have a classical shadow pixel, so that, in a broad sense, a corresponding pixel in the low-resolution remote sensing image cannot be defined as the non-shadow region, but the shadow component is small, and the caused effect is not enough to define the image as the classical shadow region, which is caused by the scale effect. According to the embodiment, a mathematical basis component M (x, y) for uniformly and quantitatively describing generalized shadows in the remote sensing images is extracted from a single-scene remote sensing image by adopting an SMACC algorithm according to a mixed spectrum decomposition principle, then a segmented mapping function is established to map the mathematical basis component M (x, y) into shadow components S (lambda, x, y) with different wave bands, finally a generalized shadow consistency spectrum reconstruction equation R (lambda, x, y) is deduced according to a radiation transmission theory, equation parameters of R (lambda, x, y) are estimated based on an MVT statistical equation, and the generalized shadow spectrum reconstruction of the multi/hyperspectral remote sensing image is realized.
Compared with the prior art, the system for reconstructing the spectrum of the generalized shadow of the remote sensing image based on statistics obtains the remote sensing image data to be subjected to spectrum reconstruction, performs radiometric calibration on the remote sensing image data, converts the remote sensing image data into radiometric data, and performs data cutting on the region to be subjected to spectrum reconstruction; performing mixed spectrum decomposition on the spectrum reconstruction area subjected to data cutting, and extracting shadow basis components; according to a radiation transmission theory, deducing a generalized shadow consistency spectrum reconstruction equation; and acquiring spectral parameters based on statistical equation estimation to realize the generalized shadow spectral reconstruction of the remote sensing image to be subjected to spectral reconstruction. The remote sensing image generalized shadow spectrum reconstruction system based on statistics breaks through the classical definition of the traditional remote sensing image shadow, deduces a new generalized shadow consistency spectrum reconstruction equation, and has the advantages of natural color transition, more prominent texture, no edge effect and very obvious effect of the reconstruction result; for a small amount of ground information missing areas in the image, complete automatic filtering is realized, error correction of pixels in the information missing areas is eliminated, and the fidelity of the recorded information of the image data is maintained to the maximum extent.
Further, please refer to fig. 7, fig. 7 is a schematic functional module diagram of an embodiment of the derivation module shown in fig. 6, in this embodiment, the derivation module 30 includes an establishing unit 31 and a mapping unit 32, and the establishing unit 31 is configured to establish a statistical equation under the classical definition of the remote sensing image shadow; the mapping unit 32 performs mathematical transformation on the extracted shadow basis components and maps the shadow basis components into a shadow component map of each band.
The establishing unit 31 establishes an MVT statistical equation under the classical definition of the remote sensing image shadow, wherein the parameters of the MVT statistical equation are given according to the remote sensing image shadow detection method of energy integration.
The mapping unit 32 maps the extracted shadow base component
Performing mathematical transformation and mapping to obtain shadow component map of each band
(ii) a And deducing a generalized shadow consistency spectrum reconstruction equation R (lambda, x, y) according to a radiation transmission theory.
Compared with the prior art, the remote sensing image generalized shadow spectrum reconstruction system based on statistics establishes a statistical equation under the classical definition of the remote sensing image shadow; and performing mathematical transformation on the extracted shadow basis components, and mapping into a shadow component map of each wave band. The remote sensing image generalized shadow spectrum reconstruction system based on statistics breaks through the classical definition of the traditional remote sensing image shadow, deduces a new generalized shadow consistency spectrum reconstruction equation, and has the advantages of natural color transition, more prominent texture, no edge effect and very obvious effect of the reconstruction result; for a small amount of ground information missing areas in the image, complete automatic filtering is realized, error correction of pixels in the information missing areas is eliminated, and the fidelity of the recorded information of the image data is maintained to the maximum extent.
Preferably, referring to fig. 8, fig. 8 is a functional module schematic diagram of an embodiment of the reconstruction module shown in fig. 6, in this embodiment, the reconstruction module 40 includes an obtaining unit 41 and a substituting unit 42, where the obtaining unit 41 is configured to obtain the spectral parameters based on the MVT statistical equation estimation; and a substituting unit 42, configured to substitute the acquired spectral parameters into a generalized shadow consistency spectral reconstruction equation R (λ, x, y), so as to obtain a generalized shadow spectral reconstruction result of the remote sensing image to be subjected to spectral reconstruction.
The obtaining unit 41 obtains the spectral parameters based on the MVT statistical equation estimation, and the spectral parameters may be slope vectors of the MVT equation parameters
And intercept vector
Vectors, and the like.
The substituting unit 42 substitutes the acquired spectral parameters into the generalized shadow consistency spectral reconstruction equation of equation (5) to obtain a generalized shadow spectral reconstruction result of the remote sensing image to be subjected to spectral reconstruction, wherein initial image and experimental reconstruction result graphs of the ASTER image generalized shadow spectral reconstruction are shown in fig. 4 and fig. 5, respectively.
Compared with the traditional remote sensing image data shadow correction method, as can be clearly seen from fig. 5, the system provided by the embodiment restores most shadow region information of the image to a result approximately consistent with a non-shadow region, and has the advantages of natural color transition, more prominent texture, no edge effect and very obvious effect. Meanwhile, for a small number of 'black spot areas', because the pixel reflection radiation is very weak and is close to the '0' reflection pixel, the data recorded by the sensor is similar to the atmospheric upstroke radiation noise, which is equivalent to a ground information missing area, the data can be identified, the reconstruction of false information is eliminated, and the fidelity of the information recorded by the image data is maintained to the maximum extent.
Compared with the prior art, the remote sensing image generalized shadow spectrum reconstruction system based on statistics obtains spectrum parameters through MVT statistical equation estimation; and substituting the acquired spectral parameters into the generalized shadow consistency spectral reconstruction equation to obtain a generalized shadow spectral reconstruction result of the remote sensing image to be subjected to spectral reconstruction. The remote sensing image generalized shadow spectrum reconstruction system based on statistics breaks through the classical definition of the traditional remote sensing image shadow, deduces a new generalized shadow consistency spectrum reconstruction equation, and has the advantages of natural color transition, more prominent texture, no edge effect and very obvious effect of the reconstruction result; for a small amount of ground information missing areas in the image, complete automatic filtering is realized, error correction of pixels in the information missing areas is eliminated, and the fidelity of the recorded information of the image data is maintained to the maximum extent.
While preferred embodiments of the present invention have been described, additional variations and modifications in those embodiments may occur to those skilled in the art once they learn of the basic inventive concepts. Therefore, it is intended that the appended claims be interpreted as including preferred embodiments and all such alterations and modifications as fall within the scope of the invention. It will be apparent to those skilled in the art that various changes and modifications may be made in the present invention without departing from the spirit and scope of the invention. Thus, if such modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include such modifications and variations.
Claims (8)
1. A remote sensing image generalized shadow spectrum reconstruction method based on statistics is characterized by comprising the following steps:
obtaining remote sensing image data to be subjected to spectral reconstruction, carrying out radiometric calibration on the remote sensing image data, converting the remote sensing image data into radiometric data, and simultaneously carrying out data cutting on a region to be subjected to spectral reconstruction;
performing mixed spectrum decomposition on the spectrum reconstruction area subjected to data cutting, and extracting shadow basis components;
according to a radiation transmission theory, deducing a generalized shadow consistency spectrum reconstruction equation;
acquiring spectral parameters based on statistical equation estimation, and realizing the generalized shadow spectral reconstruction of the remote sensing image to be subjected to spectral reconstruction;
the step of deriving the generalized shadow consistency spectral reconstruction equation according to the radiation transmission theory comprises:
establishing a statistical equation under the classical definition of the remote sensing image shadow;
and performing mathematical transformation on the extracted shadow basis components, and mapping into a shadow component map of each wave band.
2. The method for reconstructing the spectrum of generalized shadows of remote sensing images based on statistics of claim 1,
the step of performing mixed spectrum decomposition on the spectral reconstruction region after data clipping and extracting shadow basis components comprises the following steps:
the shadow basis components are extracted using the SMACC algorithm.
3. The method for reconstructing the spectrum of generalized shadows of remote sensing images based on statistics of claim 1,
the generalized shadow consistency spectral reconstruction equation R (λ, x, y) derived is:
wherein the content of the first and second substances,
representing picture elements
Radiance after spectral reconstruction;
representing an initial radiance;
representing picture elements
(ii) each band shadow component;
representing the range radiation as a constant term;
shadow component of each band
Comprises the following steps:
wherein the content of the first and second substances,
representing a shadow base component
A scaling transformation function of (a);
representing a transformation parameter;
representing the curved surface translation factor after transformation;
the shadow base component
Mapping to band-shadow components
The mapping function used is:
wherein the content of the first and second substances,
representing a shadow base component
A scaling transformation function of (a);
representing a transformation parameter;
represents a classical shadow region;
a classical non-shaded region is indicated.
4. The method for reconstructing the spectrum of generalized shadows of remote sensing images based on statistics of claim 1,
the method for achieving the spectral reconstruction of the generalized shadow of the remote sensing image to be subjected to spectral reconstruction based on the estimation of the statistical equation comprises the following steps of:
obtaining spectral parameters based on statistical equation estimation;
and substituting the acquired spectral parameters into the generalized shadow consistency spectral reconstruction equation to obtain a generalized shadow spectral reconstruction result of the remote sensing image to be subjected to spectral reconstruction.
5. A remote sensing image generalized shadow spectral reconstruction system based on statistics is characterized by comprising:
the cutting module (10) is used for acquiring remote sensing image data to be subjected to spectral reconstruction, carrying out radiometric calibration on the remote sensing image data, converting the remote sensing image data into radiometric data, and simultaneously carrying out data cutting on a spectral reconstruction area to be subjected to spectral reconstruction;
the extraction module (20) is used for carrying out mixed spectrum decomposition on the spectrum reconstruction area subjected to data cutting and extracting shadow basis components;
the derivation module (30) is used for deriving a generalized shadow consistency spectrum reconstruction equation according to a radiation transmission theory;
the reconstruction module (40) is used for obtaining spectral parameters based on statistical equation estimation and realizing the generalized shadow spectral reconstruction of the remote sensing image to be subjected to spectral reconstruction;
the derivation module (30) comprises:
the establishing unit (31) is used for establishing a statistical equation under the classical definition of the remote sensing image shadow;
and a mapping unit (32) for performing mathematical transformation on the extracted shadow base component and mapping the shadow base component into a shadow component map of each band.
6. The system for the generalized shadow spectral reconstruction of remote sensing images based on statistics of claim 5, wherein,
the extraction module (20) comprises:
the extracting unit is used for extracting the shadow base component by adopting an SMACC algorithm.
7. The system for the generalized shadow spectral reconstruction of remote sensing images based on statistics of claim 5, wherein,
the generalized shadow consistency spectral reconstruction equation R (λ, x, y) derived is:
wherein the content of the first and second substances,
representing picture elements
After spectral reconstructionThe brightness of the radiation;
representing an initial radiance;
representing picture elements
(ii) each band shadow component;
representing the range radiation as a constant term;
shadow component of each band
Comprises the following steps:
wherein the content of the first and second substances,
representing a shadow base component
A scaling transformation function of (a);
representing a transformation parameter;
representing the curved surface translation factor after transformation;
the shadow base component
Mapping to band-shadow components
The mapping function used is:
wherein the content of the first and second substances,
representing a shadow base component
A scaling transformation function of (a);
representing a transformation parameter;
represents a classical shadow region;
a classical non-shaded region is indicated.
8. The system for the generalized shadow spectral reconstruction of remote sensing images based on statistics of claim 5, wherein,
the reconstruction module (40) comprises:
an acquisition unit (41) for acquiring spectral parameters based on statistical equation estimation;
and the substituting unit (42) is used for substituting the acquired spectral parameters into the generalized shadow consistency spectral reconstruction equation to obtain a generalized shadow spectral reconstruction result of the remote sensing image to be subjected to spectral reconstruction.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010846356.2A CN111738916B (en) | 2020-08-21 | 2020-08-21 | Remote sensing image generalized shadow spectrum reconstruction method and system based on statistics |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010846356.2A CN111738916B (en) | 2020-08-21 | 2020-08-21 | Remote sensing image generalized shadow spectrum reconstruction method and system based on statistics |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN111738916A true CN111738916A (en) | 2020-10-02 |
| CN111738916B CN111738916B (en) | 2020-11-13 |
Family
ID=72658661
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202010846356.2A Active CN111738916B (en) | 2020-08-21 | 2020-08-21 | Remote sensing image generalized shadow spectrum reconstruction method and system based on statistics |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN111738916B (en) |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112927150A (en) * | 2021-02-20 | 2021-06-08 | 河北先河环保科技股份有限公司 | Water reflection area spectrum recovery method of hyperspectral image and terminal device |
| CN112964643A (en) * | 2021-02-03 | 2021-06-15 | 福州大学 | Method for correcting landform falling shadow of visible light wave band of remote sensing image |
| CN113096048A (en) * | 2021-04-25 | 2021-07-09 | 华中师范大学 | Method and system for radiation correction of generalized cloud-driven and geometric collaborative remote sensing image |
| CN117252789A (en) * | 2023-11-10 | 2023-12-19 | 中国科学院空天信息创新研究院 | Shadow reconstruction method and device for high-resolution remote sensing image and electronic equipment |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JPH11304591A (en) * | 1998-04-22 | 1999-11-05 | Mitsubishi Electric Corp | Device for acquiring spectral image |
| CN102279393A (en) * | 2011-07-15 | 2011-12-14 | 北京航空航天大学 | Cross radiometric calibration method of hyper-spectral sensor based on multi-spectral sensor |
| CN103020649A (en) * | 2013-01-16 | 2013-04-03 | 东北林业大学 | Forest type identification method based on texture information |
| CN108760658A (en) * | 2018-06-06 | 2018-11-06 | 湖南省有色地质勘查研究院 | A kind of remote sensing image vegetation inhibits and Rocks, Soils information restoring method |
| CN109035150A (en) * | 2018-04-24 | 2018-12-18 | 湖南省有色地质勘查研究院 | A kind of remote sensing image data defogging method based on middle-low alloy steels |
-
2020
- 2020-08-21 CN CN202010846356.2A patent/CN111738916B/en active Active
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JPH11304591A (en) * | 1998-04-22 | 1999-11-05 | Mitsubishi Electric Corp | Device for acquiring spectral image |
| CN102279393A (en) * | 2011-07-15 | 2011-12-14 | 北京航空航天大学 | Cross radiometric calibration method of hyper-spectral sensor based on multi-spectral sensor |
| CN103020649A (en) * | 2013-01-16 | 2013-04-03 | 东北林业大学 | Forest type identification method based on texture information |
| CN109035150A (en) * | 2018-04-24 | 2018-12-18 | 湖南省有色地质勘查研究院 | A kind of remote sensing image data defogging method based on middle-low alloy steels |
| CN108760658A (en) * | 2018-06-06 | 2018-11-06 | 湖南省有色地质勘查研究院 | A kind of remote sensing image vegetation inhibits and Rocks, Soils information restoring method |
Non-Patent Citations (2)
| Title |
|---|
| LI Q 等: "Research on Preservation of Evidence Based on Remote Sensing Image Supported by GIS", 《IEEE INTERNATIONAL CONFERENCE ON GEOSCIENCE & REMOTE SENSING SYMPOSIUM》 * |
| 罗素群 等: "基于统计模型的高光谱数据校正及分类研究", 《工业控制计算机》 * |
Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112964643A (en) * | 2021-02-03 | 2021-06-15 | 福州大学 | Method for correcting landform falling shadow of visible light wave band of remote sensing image |
| CN112964643B (en) * | 2021-02-03 | 2022-04-19 | 福州大学 | Method for correcting landform falling shadow of visible light wave band of remote sensing image |
| CN112927150A (en) * | 2021-02-20 | 2021-06-08 | 河北先河环保科技股份有限公司 | Water reflection area spectrum recovery method of hyperspectral image and terminal device |
| CN113096048A (en) * | 2021-04-25 | 2021-07-09 | 华中师范大学 | Method and system for radiation correction of generalized cloud-driven and geometric collaborative remote sensing image |
| CN117252789A (en) * | 2023-11-10 | 2023-12-19 | 中国科学院空天信息创新研究院 | Shadow reconstruction method and device for high-resolution remote sensing image and electronic equipment |
| CN117252789B (en) * | 2023-11-10 | 2024-02-02 | 中国科学院空天信息创新研究院 | Shadow reconstruction method and device for high-resolution remote sensing image and electronic equipment |
Also Published As
| Publication number | Publication date |
|---|---|
| CN111738916B (en) | 2020-11-13 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Kulkarni et al. | Pixel level fusion techniques for SAR and optical images: A review | |
| CN111738916B (en) | Remote sensing image generalized shadow spectrum reconstruction method and system based on statistics | |
| CN108573276B (en) | Change detection method based on high-resolution remote sensing image | |
| Shen et al. | Missing information reconstruction of remote sensing data: A technical review | |
| Shahtahmassebi et al. | Review of shadow detection and de-shadowing methods in remote sensing | |
| Amro et al. | A survey of classical methods and new trends in pansharpening of multispectral images | |
| Makarau et al. | Haze detection and removal in remotely sensed multispectral imagery | |
| Liu et al. | Hyperspectral restoration and fusion with multispectral imagery via low-rank tensor-approximation | |
| Zhai | Inversion of organic matter content in wetland soil based on Landsat 8 remote sensing image | |
| Kang et al. | Fog model-based hyperspectral image defogging | |
| Kumar et al. | Digital image processing of remotely sensed satellite images for information extraction | |
| Zhang et al. | Multi-temporal cloud detection based on robust PCA for optical remote sensing imagery | |
| Wang et al. | Poissonian blurred hyperspectral imagery denoising based on variable splitting and penalty technique | |
| Ouerghemmi et al. | Urban vegetation mapping by airborne hyperspetral imagery; feasibility and limitations | |
| Sandhan et al. | Simultaneous detection and removal of high altitude clouds from an image | |
| Borra et al. | Satellite image enhancement and analysis | |
| Luo et al. | An Evolutionary Shadow Correction Network and A Benchmark UAV Dataset for Remote Sensing Images | |
| Singh | Pattern recognition and image processing | |
| Hu et al. | Rapid dehazing algorithm based on large-scale median filtering for high-resolution visible near-infrared remote sensing images | |
| Dhore et al. | A new pan-sharpening method using joint sparse FI image fusion algorithm | |
| Ye et al. | Optical and SAR image fusion based on complementary feature decomposition and visual saliency features | |
| Pacot et al. | Cloud removal from aerial images using generative adversarial network with simple image enhancement | |
| CN111667432B (en) | Remote sensing image shadow removing method based on physical model | |
| Mishra et al. | BLUR KERNEL’S EFFECT ON PERFORMANCE OF SINGLE-FRAME SUPER-RESOLUTION ALGORITHMS FOR SPATIALLY ENHANCING HYPERION AND PRISMA DATA | |
| BAJPAI | Remotely sensed image segmentation using multiphase level-set active contour model |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant | ||
| TR01 | Transfer of patent right | ||
| TR01 | Transfer of patent right |
Effective date of registration: 20220808 Address after: 410000 no.256 Laodong West Road, Changsha City, Hunan Province Patentee after: RESEARCH INSTITUTE OF HUNAN PROVINCE NONFERROUS METALS GEOLOGICAL EXPLORATION BUREAU Patentee after: Hunan Xinxiang Geophysical Exploration Engineering Co.,Ltd. Address before: Jiangshan resources building, 256 Laodong West Road, Changsha, Hunan 410015 Patentee before: RESEARCH INSTITUTE OF HUNAN PROVINCE NONFERROUS METALS GEOLOGICAL EXPLORATION BUREAU |