CN110309694B - Method and device for determining main direction of remote sensing image - Google Patents
Method and device for determining main direction of remote sensing image Download PDFInfo
- Publication number
- CN110309694B CN110309694B CN201810903797.4A CN201810903797A CN110309694B CN 110309694 B CN110309694 B CN 110309694B CN 201810903797 A CN201810903797 A CN 201810903797A CN 110309694 B CN110309694 B CN 110309694B
- Authority
- CN
- China
- Prior art keywords
- sensing image
- remote sensing
- main direction
- block
- areas
- 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.)
- Expired - Fee Related
Links
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/24—Classification techniques
- G06F18/241—Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V20/00—Scenes; Scene-specific elements
- G06V20/10—Terrestrial scenes
- G06V20/13—Satellite images
Landscapes
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Data Mining & Analysis (AREA)
- Bioinformatics & Cheminformatics (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Evolutionary Biology (AREA)
- Evolutionary Computation (AREA)
- Bioinformatics & Computational Biology (AREA)
- General Engineering & Computer Science (AREA)
- Artificial Intelligence (AREA)
- Life Sciences & Earth Sciences (AREA)
- Astronomy & Astrophysics (AREA)
- Remote Sensing (AREA)
- Multimedia (AREA)
- Image Analysis (AREA)
Abstract
The invention relates to a method and a device for determining a main direction of a remote sensing image, and belongs to the field of remote sensing image processing application. The method comprises the steps of firstly dividing a remote sensing image into a plurality of remote sensing image areas, then classifying the remote sensing image areas into a plurality of remote sensing image blocks according to the consistency among the remote sensing image areas, then calculating the main direction of each remote sensing image block, and taking a set formed by the main directions of the remote sensing image blocks as the main direction of the remote sensing image. The invention does not need to manually frame the approximate range of each target in the remote sensing image, and does not need to calculate the main direction of each target one by one, thereby reducing manual intervention, reducing calculated amount and improving practicability.
Description
Technical Field
The invention relates to a method and a device for determining a main direction of a remote sensing image, and belongs to the field of remote sensing image processing application.
Background
At present, the description of the features of the remote sensing image features at home and abroad mainly focuses on 3 aspects such as spectrum, texture and geometric shape information. Along with the improvement of the resolution ratio of the remote sensing image, the shape diversity and the spectrum complexity of the ground features tend to be obvious, the phenomena of 'same-spectrum foreign matter' and 'foreign matter same-spectrum' are more obvious, the mode separability of different ground feature elements in a spectrum domain is greatly weakened by higher intra-class variation and lower inter-class difference, and the identification and extraction of the ground features cannot be well realized by simply depending on the spectrum information; the geometric characteristics mainly utilize information such as shape, size and the like, the algorithm is relatively simple, and the geometric characteristics are often used as auxiliary characteristics for ground feature identification and extraction post-processing; the texture features are one of basic visual features, can give consideration to macroscopic features and microscopic details, have stronger stability and have more and more obvious advantages in the processing and analysis of the remote sensing images.
In the text of technical research on extraction of residential areas of remote sensing images based on textural features (doctor's academic paper of the university of liberty military information engineering), a method for extracting residential areas of remote sensing images based on textural features is disclosed, a region growing method based on seed points is adopted in the text for extracting residential areas, a search mode of 8 neighborhoods is adopted to measure similarity distances of textural features between the seed points and candidate points, whether the candidate points and the seed points are classified into one class or not is judged according to a certain criterion, and the process is repeated until the searching of pixel points meeting the conditions is completed. Among them, the authors specifically use a plurality of different methods (including fourier transform, wavelet transform, Gabor transform, etc.) to perform corresponding texture feature extraction, so as to illustrate the superiority and inferiority among the methods. The algorithms for extracting the texture features need to calculate the main direction of the remote sensing image.
However, the above solution requires the calculation of a specific texture direction accurate to a certain degree for each residential area, and has the following disadvantages: the manual intervention links are too many, a seed point needs to be manually selected, the calculated amount is large, and the method is not practical enough. In the field, administrative villages (residential areas) governed by a grade city are generally hundreds, and in some densely populated areas, thousands of administrative villages can be reached at most, while the residential area extraction method disclosed in the prior art requires that each administrative village is manually framed in the whole remote sensing image, so that corresponding operations are required to be performed by a specially-assigned person hundreds of times, and meanwhile, calculation of a main direction is required to be performed specially for each administrative village, so that the calculation amount is too large, and the practicability is not high; in practical application, the size of each residential area is random and uncertain, so the outline of each residential area is uncertain, the image range which needs to be framed based on the seed points to calculate the main direction cannot be determined, the framed range is small, so the residential area (such as administrative village) is not framed, if the framed range is large, other villages or ground objects are framed, and the calculated main direction is inaccurate.
Disclosure of Invention
The invention aims to provide a method and a device for determining the main direction of a remote sensing image so as to solve the problem that the conventional method for determining the main direction of the remote sensing image depends too much on manpower, so that the calculated amount is too large.
In order to achieve the above object, the present invention provides a first solution: a method for determining the main direction of a remote sensing image comprises the following steps:
A. dividing the remote sensing image I into N blocks of regions (N is an integer not less than 1), and respectively recording the N blocks of regions as I1，…，IN；
B. Calculating main directions of different remote sensing image areas of the remote sensing image I, classifying the remote sensing image areas according to the consistency of the main directions, and classifying the remote sensing image areas with consistency deviation smaller than a first set threshold value into a remote sensing image block;
C. and solving a corresponding main direction of each remote sensing image block, wherein the set of the main directions of each remote sensing image block is the main direction of the remote sensing image I.
According to the method, the areas with strong consistency are classified into the blocks, the main direction of the remote sensing image is formed by the main direction of the blocks, manual intervention is reduced, meanwhile, the calculation amount of the main direction of the remote sensing image is greatly reduced, and the efficiency and the practicability of determining the main direction of the remote sensing image are improved.
Scheme II: on the basis of the first scheme, the method for performing consistency judgment and classification in the step B specifically comprises the following steps:
alpha) calculating each remote sensing image area I1，…，INDetermining the position of a peak value in an angular distribution curve by corresponding angular distribution of the Fourier transform magnitude spectrum, and taking an angle corresponding to the position as a main direction of each remote sensing image area to obtain each remote sensing image area I1，…，INCorresponding main direction theta1，…，θN；
Beta) classifying all the remote sensing image areas according to the criterion that the consistency deviation between the corresponding main directions of all the remote sensing image areas is smaller than a first set threshold value, and classifying the remote sensing image areas meeting the criterion condition into a remote sensing image block.
The third scheme is as follows: on the basis of the second scheme, the method for solving the corresponding main direction of the remote sensing image block in the step C specifically comprises the following steps:
1) calculating an average value of main directions corresponding to remote sensing image areas forming the remote sensing image block;
2) and taking the average value as the main direction of the remote sensing image block.
And the scheme is as follows: on the basis of the scheme I or II or III, if a certain remote sensing image block only comprises a remote sensing image area ImThen, the method for calculating the main direction of the remote sensing image block comprises the following steps:
i) remote sensing image area ImRandomly dividing the block into N 'sub-areas (N' is an integer not less than 1), and respectively recording the sub-areas
ii) for remote sensing image area ImCalculating main directions of different remote sensing image subregions, classifying the remote sensing image subregions according to the consistency of the main directions, and classifying the remote sensing image subregions with consistency deviation smaller than a second set threshold value into a remote sensing image sub-region;
iii) solving the corresponding main direction of each remote sensing image sub-block, wherein the set of the main directions of each remote sensing image sub-block is the remote sensing image area ImCorresponding to the main direction of the remote sensing image block.
According to the scheme, the remote sensing image areas with large differences are subjected to reclassification calculation, and the remote sensing image areas are subdivided, so that the accuracy of determining the main direction of the final remote sensing image is improved.
And a fifth scheme: on the basis of the fourth scheme, the method for classifying by consistency judgment in the step ii) specifically comprises the following steps:
a) calculating each remote sensing image subregion
Corresponding angular distribution of the Fourier transform magnitude spectrum, determining the position of a peak value in an angular distribution curve, and taking an angle corresponding to the position as a main direction of each remote sensing image subregion to obtain each remote sensing image subregion
Corresponding main direction
b) And classifying all the remote sensing image sub-areas according to the criterion that the consistency deviation between the corresponding main directions of all the remote sensing image sub-areas is smaller than a second set threshold value, and classifying the remote sensing image sub-areas meeting the criterion condition into a remote sensing image sub-area block.
Scheme six: on the basis of the fifth scheme, the method for solving the corresponding main direction of the remote sensing image sub-block in the step iii) specifically comprises the following steps:
(1) calculating an average value of main directions corresponding to the remote sensing image sub-areas forming the remote sensing image sub-block;
(2) and taking the average value as the main direction of the remote sensing image sub-block.
In addition, the invention also provides a seventh scheme: a device for determining the main direction of a remote sensing image comprises a memory, a processor and a computer program which is stored in the memory and can run on the processor, wherein when the processor executes the program, any one of a scheme I to a scheme VI can be realized.
The device for calculating the main direction of the remote sensing image through the classification idea is provided, the device classifies the regions with strong consistency into blocks by running related programs, the main direction of the blocks forms the main direction of the remote sensing image, manual intervention is reduced, meanwhile, the calculation amount of the main direction of the remote sensing image is greatly reduced, and the efficiency and the practicability of determining the main direction of the remote sensing image are improved.
Drawings
Fig. 1 is a flowchart of a method for determining a main direction of a remote sensing image according to the present invention.
Detailed Description
Fig. 1 is a flowchart illustrating a method for determining a main direction of a remote sensing image according to the present invention, wherein M is an integer less than or equal to N. The following further describes embodiments of the present invention with reference to the drawings.
Example 1
Determining the main direction of the remote sensing image according to the following steps:
1. dividing the remote sensing image I into 10 areas at random, and respectively recording the areas as I1，…，I10；
2. Calculating each remote sensing image area I1，…，I10Determining the specific position of a peak value in an angular distribution curve by corresponding angular distribution of the Fourier transform magnitude spectrum, and taking an angle corresponding to the specific position as a main direction of each remote sensing image area to obtain each remote sensing image area I1，…，I10Corresponding main direction theta1，…，θ10；
3. Classifying all remote sensing image areas according to a criterion that consistency deviation between corresponding main directions of all the remote sensing image areas is smaller than a second set threshold, dividing the remote sensing image areas meeting the criterion condition into a remote sensing image block, in other words, when variance of the corresponding main directions of a plurality of remote sensing image areas is smaller than a set value (for example, 10 degrees), indicating that the consistency of the plurality of remote sensing image areas is better, and performing similarity comparison by using the same filter, so that similar areas are found one by one from ten remote sensing image areas and classified;
4. calculating an average value of main directions corresponding to remote sensing image areas forming a certain remote sensing image block, and taking the average value as the main direction of the remote sensing image block;
5. and taking the set of main directions corresponding to the remote sensing image blocks as the main directions of the remote sensing image I.
If a remote-sensing image block only comprises a remote-sensing image area ImIf so, the remote sensing image block needs to be subdivided, and the main direction of the remote sensing image block is recalculated, which specifically comprises the following steps:
i) remote sensing image area ImRandomly divided into 4 sub-areas, respectively marked as
ii) calculating each remote sensing image subregion
Determining the specific position of a peak value in an angular distribution curve by corresponding angular distribution of the Fourier transform magnitude spectrum, and taking an angle corresponding to the specific position as a main direction of each remote sensing image subregion to obtain each remote sensing image subregion
Corresponding main direction
iii) classifying all the remote sensing image subregions according to a criterion that the consistency deviation between the main directions corresponding to all the remote sensing image subregions is smaller than a second set threshold, dividing the remote sensing image subregions meeting the criterion condition into remote sensing image subregions, in other words, when the variance of the main directions corresponding to a plurality of remote sensing image subregions is smaller than a set value (for example, 10 degrees), indicating that the consistency of the plurality of remote sensing image subregions is better, and performing similarity comparison by using the same filter, thereby finding similar subregions from 4 remote sensing image regions one by one and classifying the subregions into categories;
iv) calculating an average value of main directions corresponding to remote sensing image areas forming a remote sensing image sub-block, and taking the average value as the main direction of the remote sensing image sub-block;
v) taking the set of main directions corresponding to the remote sensing image sub-blocks as the remote sensing image area ImAnd the main direction of the corresponding remote sensing image block.
The main direction corresponding to the remote sensing image is obtained, and as to how to use the main direction specifically, specific contents of "extraction of residences based on fourier transform" in section 3.1 of "research on residences extraction technology of remote sensing image based on texture features" (doctor's academic paper of information engineering university of the golden fly, liberty) "and" extraction of residences based on Gabor transform "in section 3.2 of the background art can be referred to.
Of course, the method for determining the main direction of the remote sensing image is not limited to fourier transform and Gabor transform, and the method for determining the main direction of the remote sensing image can be used in any algorithm as long as the main direction of the remote sensing image needs to be used, and the above-mentioned scheme should still fall into the scope of the present invention.
Example 2
The present embodiment is a device for determining a main direction of a remote sensing image, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the method for determining a main direction of a remote sensing image according to embodiment 1 when executing the program, and when performing a specific programming, since knowledge of a programming language such as syntax is common knowledge in the art, a skilled person is fully capable of performing a corresponding programming according to the specific method for determining a main direction of a remote sensing image of the present invention using an existing programming language (e.g., C language, JAVA, assembly language, C #, C + +, etc.), which is not described herein again.
In the above embodiments, the remote sensing image is randomly segmented to form a remote sensing image region, and in practical applications, a specific segmentation scheme, such as uniform segmentation, proportional segmentation or template segmentation, may also be set for segmentation, and the above scheme should still fall within the scope of the present invention.
In the above embodiment, the variance between the angles is used as the standard for consistency judgment and classification, in practical application, the consistency judgment may be performed by averaging different angles and calculating the deviation between each angle and the average, and the judgment may also be performed by using a standard variance or a least square method or other common consistency classification methods, and the above solutions should still fall within the protection scope of the present invention.
In the above embodiment, when calculating the main direction of the remote-sensing image block or the sub-block, a way of averaging the main direction corresponding to the area constituting the block or the main direction corresponding to the sub-area constituting the sub-block is adopted, and in practical applications, other calculation ways such as arithmetic mean, minimum error, and the like may also be used for calculation, and in addition, the existing way of using the angular distribution of the fourier transform magnitude spectrum may also be used for calculation, and the above scheme should still fall within the protection scope of the present invention.
The specific embodiments are given above, but the present invention is not limited to the described embodiments. The basic idea of the present invention is to provide the basic solution described above, and variations, modifications, replacements, and variations of the embodiments can be made without departing from the principle and spirit of the present invention, and still fall within the protection scope of the present invention.
Claims (6)
1. A method for determining the main direction of a remote sensing image is characterized by comprising the following steps:
A. dividing the remote sensing image I into N blocks of regions (N is an integer not less than 1), and respectively recording the N blocks of regions as I1，…，IN；
B. Calculating a main direction of different remote sensing image areas of the remote sensing image I, classifying the remote sensing image areas according to the consistency of the main direction, and classifying the remote sensing image areas with consistency deviation smaller than a first set threshold value into a remote sensing image block;
C. solving a corresponding main direction of each remote sensing image block, wherein the set of the main directions of each remote sensing image block is the main direction of the remote sensing image I;
if a remote-sensing image block only comprises a remote-sensing image area ImThen the remote sensing image blockThe method for calculating the main direction of (1) comprises the steps of:
i) remote sensing image area ImRandomly dividing the block into N 'sub-areas (N' is an integer not less than 1), and respectively recording the sub-areas
ii) for remote sensing image area ImCalculating the main direction of different remote sensing image subregions, classifying the remote sensing image subregions according to the consistency of the main direction, and classifying the remote sensing image subregions with consistency deviation smaller than a second set threshold value into a remote sensing image sub-region;
iii) solving the corresponding main direction of each remote sensing image sub-block, wherein the set of the main directions of each remote sensing image sub-block is the remote sensing image area ImCorresponding to the main direction of the remote sensing image block.
2. The method for determining the principal direction of remote sensing images according to claim 1, wherein the method for consistency classification in the step B comprises the following steps:
alpha) calculating each remote sensing image area I1，…，INDetermining the position of a peak value in an angular distribution curve by corresponding angular distribution of the Fourier transform magnitude spectrum, and taking an angle corresponding to the position as a main direction of each remote sensing image area to obtain each remote sensing image area I1，…，INCorresponding main direction theta1，…，θN；
Beta) classifying all the remote sensing image areas according to the criterion that the consistency deviation between the corresponding main directions of all the remote sensing image areas is smaller than a first set threshold value, and classifying the remote sensing image areas meeting the criterion condition into a remote sensing image block.
3. The method for determining the main direction of the remote sensing image according to claim 2, wherein the method for obtaining the corresponding main direction of the remote sensing image block in the step C comprises the following steps:
1) calculating an average value of main directions corresponding to remote sensing image areas forming the remote sensing image block;
2) and taking the average value as the main direction of the remote sensing image block.
4. The method for determining the principal direction of remote sensing images according to claim 1, wherein the method for consistency classification in step ii) specifically comprises the following steps:
a) calculating each remote sensing image subregion
Corresponding angular distribution of the Fourier transform magnitude spectrum, determining the position of a peak value in an angular distribution curve, and taking an angle corresponding to the position as a main direction of each remote sensing image subregion to obtain each remote sensing image subregion
Corresponding main direction
b) And classifying all the remote sensing image sub-areas according to the criterion that the consistency deviation between the corresponding main directions of all the remote sensing image sub-areas is smaller than a second set threshold value, and classifying the remote sensing image sub-areas meeting the criterion condition into a remote sensing image sub-area block.
5. The method for determining the main direction of the remote sensing image according to claim 4, wherein the method for obtaining the corresponding main direction of the remote sensing image sub-block in the step iii) specifically comprises the following steps:
(1) calculating an average value of main directions corresponding to the remote sensing image sub-areas forming the remote sensing image sub-block;
(2) and taking the average value as the main direction of the remote sensing image sub-block.
6. A device for determining the principal direction of a remote-sensing image, comprising a memory, a processor and a computer program stored in the memory and capable of running on the processor, wherein the processor implements the method for determining the principal direction of a remote-sensing image according to any one of claims 1 to 5 when executing the program.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201810903797.4A CN110309694B (en) | 2018-08-09 | 2018-08-09 | Method and device for determining main direction of remote sensing image |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201810903797.4A CN110309694B (en) | 2018-08-09 | 2018-08-09 | Method and device for determining main direction of remote sensing image |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN110309694A CN110309694A (en) | 2019-10-08 |
| CN110309694B true CN110309694B (en) | 2021-03-26 |
Family
ID=68074096
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201810903797.4A Expired - Fee Related CN110309694B (en) | 2018-08-09 | 2018-08-09 | Method and device for determining main direction of remote sensing image |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN110309694B (en) |
Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102542295A (en) * | 2012-01-08 | 2012-07-04 | 西北工业大学 | Method for detecting landslip from remotely sensed image by adopting image classification technology |
| CN102968634A (en) * | 2012-11-23 | 2013-03-13 | 南京大学 | Method for extracting parking lot structure under main direction restriction |
| CN103347187A (en) * | 2013-07-23 | 2013-10-09 | 北京师范大学 | Remote-sensing image compression method for discrete wavelet transform based on self-adaptation direction prediction |
| CN104657741A (en) * | 2015-01-09 | 2015-05-27 | 北京环境特性研究所 | Target classification method based on video images |
| CN105261014A (en) * | 2015-09-30 | 2016-01-20 | 西南交通大学 | Multi-sensor remote sensing image matching method |
| CN105825169A (en) * | 2016-03-10 | 2016-08-03 | 辽宁工程技术大学 | Road-image-based pavement crack identification method |
| CN105844337A (en) * | 2016-04-14 | 2016-08-10 | 吴本刚 | Intelligent garbage classification device |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20080146302A1 (en) * | 2006-12-14 | 2008-06-19 | Arlen Lynn Olsen | Massive Multiplayer Event Using Physical Skills |
-
2018
- 2018-08-09 CN CN201810903797.4A patent/CN110309694B/en not_active Expired - Fee Related
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102542295A (en) * | 2012-01-08 | 2012-07-04 | 西北工业大学 | Method for detecting landslip from remotely sensed image by adopting image classification technology |
| CN102968634A (en) * | 2012-11-23 | 2013-03-13 | 南京大学 | Method for extracting parking lot structure under main direction restriction |
| CN103347187A (en) * | 2013-07-23 | 2013-10-09 | 北京师范大学 | Remote-sensing image compression method for discrete wavelet transform based on self-adaptation direction prediction |
| CN104657741A (en) * | 2015-01-09 | 2015-05-27 | 北京环境特性研究所 | Target classification method based on video images |
| CN105261014A (en) * | 2015-09-30 | 2016-01-20 | 西南交通大学 | Multi-sensor remote sensing image matching method |
| CN105825169A (en) * | 2016-03-10 | 2016-08-03 | 辽宁工程技术大学 | Road-image-based pavement crack identification method |
| CN105844337A (en) * | 2016-04-14 | 2016-08-10 | 吴本刚 | Intelligent garbage classification device |
Also Published As
| Publication number | Publication date |
|---|---|
| CN110309694A (en) | 2019-10-08 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN103400151B (en) | The optical remote sensing image of integration and GIS autoregistration and Clean water withdraw method | |
| Kavzoglu et al. | A comparative study of segmentation quality for multi-resolution segmentation and watershed transform | |
| WO2018107939A1 (en) | Edge completeness-based optimal identification method for image segmentation | |
| CN103034863B (en) | The remote sensing image road acquisition methods of a kind of syncaryon Fisher and multiple dimensioned extraction | |
| CN107092871B (en) | Remote sensing image building detection method based on multiple dimensioned multiple features fusion | |
| CN107085708B (en) | High-resolution remote sensing image change detection method based on multi-scale segmentation and fusion | |
| WO2022141145A1 (en) | Object-oriented high-resolution remote sensing image multi-scale segmentation method and system | |
| CN110363053B (en) | Method and device for extracting residential area of remote sensing image | |
| CN101714254A (en) | Registering control point extracting method combining multi-scale SIFT and area invariant moment features | |
| CN104636706A (en) | Complicated background bar code image automatic partitioning method based on gradient direction consistency | |
| CN102629380B (en) | Remote sensing image change detection method based on multi-group filtering and dimension reduction | |
| CN102122359B (en) | Image registration method and device | |
| CN104200521A (en) | Three-dimensional reconstruction method of building objects based on model prior high-resolution SAR images | |
| CN112396612B (en) | Vector information assisted remote sensing image road information automatic extraction method | |
| Yuan et al. | Learning to count buildings in diverse aerial scenes | |
| CN102903109B (en) | A kind of optical image and SAR image integration segmentation method for registering | |
| CN102446356A (en) | Parallel and adaptive matching method for acquiring remote sensing images with homogeneously-distributed matched points | |
| CN109635722B (en) | Automatic identification method for high-resolution remote sensing image intersection | |
| CN103295014A (en) | Image local feature description method based on pixel location arrangement column diagrams | |
| CN113129323A (en) | Remote sensing ridge boundary detection method and system based on artificial intelligence, computer equipment and storage medium | |
| CN109410233A (en) | A kind of accurate extracting method of high-definition picture road of edge feature constraint | |
| Prerna et al. | Evaluation of LiDAR and image segmentation based classification techniques for automatic building footprint extraction for a segment of Atlantic County, New Jersey | |
| Kushwaha et al. | Automatic Bright Circular Type Oil Tank Detection Using Remote Sensing Images. | |
| CN110309694B (en) | Method and device for determining main direction of remote sensing image | |
| Buck et al. | Enhanced ship detection from overhead imagery |
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 | ||
| CF01 | Termination of patent right due to non-payment of annual fee |
Granted publication date: 20210326 Termination date: 20210809 |
|
| CF01 | Termination of patent right due to non-payment of annual fee |