A Self-Adaptive Thresholding Approach for Automatic Water Extraction Using Sentinel-1 SAR Imagery Based on OTSU Algorithm and Distance Block
As an indispensable material for animals, plants and human beings, obtaining accurate water body information rapidly is of great significance to maintain the balance of ecosystems and ensure normal production and the life of human beings. Due to its independence of the time of day and the weather co...
| Main Authors: | , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2023-05-01
|
| Series: | Remote Sensing |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2072-4292/15/10/2690 |
| _version_ | 1797598435601285120 |
|---|---|
| author | Jianbo Tan Yi Tang Bin Liu Guang Zhao Yu Mu Mingjiang Sun Bo Wang |
| author_facet | Jianbo Tan Yi Tang Bin Liu Guang Zhao Yu Mu Mingjiang Sun Bo Wang |
| author_sort | Jianbo Tan |
| collection | DOAJ |
| description | As an indispensable material for animals, plants and human beings, obtaining accurate water body information rapidly is of great significance to maintain the balance of ecosystems and ensure normal production and the life of human beings. Due to its independence of the time of day and the weather conditions, synthetic aperture radar (SAR) data have been increasingly applied in the extraction of water bodies. However, there is a great deal of speckle noise in SAR images, which seriously affect the extraction accuracy of water. At present, most of the processing methods are filtering methods, which will cause the loss of detailed information. Based on the characteristic of side-looking SAR, this paper proposed a self-adaptive thresholding approach for automatic water extraction based on an OTSU algorithm and distance block. In this method, the whole images were firstly divided into uniform image blocks through a distance layer which was produced by the distance to the orbit. Then, a self-adaptive processing was conducted for merging blocks. The OTSU algorithm was used to obtain a threshold for classification and the Jeffries–Matusita (JM) distance was calculated with the classification result. The merge processing continued until the separability of image blocks reached the maximum. Subsequently, we started from the next block to repeat the merger, and so on until all blocks were processed. Ten study areas around the world and the local Dongting Lake area were applied to test the feasibility of the proposed method. In comparison with five other global threshold segmentation algorithms such as the traditional OTSU, MOMENTS, MEAN, ISODATA and MINERROR, the proposed method obtains the highest overall accuracy (OA) and kappa coefficient (KC), while this approach also demonstrates greater robustness in the analysis of time series. The findings of this study offer an effective method to improve water detection accuracy as well as reducing the influence of speckle noise and retaining details in the image. |
| first_indexed | 2024-03-11T03:21:10Z |
| format | Article |
| id | doaj.art-284b2e8e20e74c83b6160c66e4933a11 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-11T03:21:10Z |
| publishDate | 2023-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-284b2e8e20e74c83b6160c66e4933a112023-11-18T03:08:47ZengMDPI AGRemote Sensing2072-42922023-05-011510269010.3390/rs15102690A Self-Adaptive Thresholding Approach for Automatic Water Extraction Using Sentinel-1 SAR Imagery Based on OTSU Algorithm and Distance BlockJianbo Tan0Yi Tang1Bin Liu2Guang Zhao3Yu Mu4Mingjiang Sun5Bo Wang6School of Traffic and Transportation Engineering, Changsha University of Science & Technology, Changsha 410114, ChinaSchool of Traffic and Transportation Engineering, Changsha University of Science & Technology, Changsha 410114, ChinaSchool of Traffic and Transportation Engineering, Changsha University of Science & Technology, Changsha 410114, ChinaInstitute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, ChinaSichuan Land Consolidation and Rehabilitation Center, Chengdu 610041, ChinaBeijing Zhongke Lantu Technology Development Co., Ltd. Chengdu Branch, Chengdu 610041, ChinaXi’an Center of Mineral Resources Survey, China Geological Survey, Xi’an 710100, ChinaAs an indispensable material for animals, plants and human beings, obtaining accurate water body information rapidly is of great significance to maintain the balance of ecosystems and ensure normal production and the life of human beings. Due to its independence of the time of day and the weather conditions, synthetic aperture radar (SAR) data have been increasingly applied in the extraction of water bodies. However, there is a great deal of speckle noise in SAR images, which seriously affect the extraction accuracy of water. At present, most of the processing methods are filtering methods, which will cause the loss of detailed information. Based on the characteristic of side-looking SAR, this paper proposed a self-adaptive thresholding approach for automatic water extraction based on an OTSU algorithm and distance block. In this method, the whole images were firstly divided into uniform image blocks through a distance layer which was produced by the distance to the orbit. Then, a self-adaptive processing was conducted for merging blocks. The OTSU algorithm was used to obtain a threshold for classification and the Jeffries–Matusita (JM) distance was calculated with the classification result. The merge processing continued until the separability of image blocks reached the maximum. Subsequently, we started from the next block to repeat the merger, and so on until all blocks were processed. Ten study areas around the world and the local Dongting Lake area were applied to test the feasibility of the proposed method. In comparison with five other global threshold segmentation algorithms such as the traditional OTSU, MOMENTS, MEAN, ISODATA and MINERROR, the proposed method obtains the highest overall accuracy (OA) and kappa coefficient (KC), while this approach also demonstrates greater robustness in the analysis of time series. The findings of this study offer an effective method to improve water detection accuracy as well as reducing the influence of speckle noise and retaining details in the image.https://www.mdpi.com/2072-4292/15/10/2690self-adaptive thresholding approachautomatic water extractionOTSUdistance blockwater extentSentinel-1 |
| spellingShingle | Jianbo Tan Yi Tang Bin Liu Guang Zhao Yu Mu Mingjiang Sun Bo Wang A Self-Adaptive Thresholding Approach for Automatic Water Extraction Using Sentinel-1 SAR Imagery Based on OTSU Algorithm and Distance Block Remote Sensing self-adaptive thresholding approach automatic water extraction OTSU distance block water extent Sentinel-1 |
| title | A Self-Adaptive Thresholding Approach for Automatic Water Extraction Using Sentinel-1 SAR Imagery Based on OTSU Algorithm and Distance Block |
| title_full | A Self-Adaptive Thresholding Approach for Automatic Water Extraction Using Sentinel-1 SAR Imagery Based on OTSU Algorithm and Distance Block |
| title_fullStr | A Self-Adaptive Thresholding Approach for Automatic Water Extraction Using Sentinel-1 SAR Imagery Based on OTSU Algorithm and Distance Block |
| title_full_unstemmed | A Self-Adaptive Thresholding Approach for Automatic Water Extraction Using Sentinel-1 SAR Imagery Based on OTSU Algorithm and Distance Block |
| title_short | A Self-Adaptive Thresholding Approach for Automatic Water Extraction Using Sentinel-1 SAR Imagery Based on OTSU Algorithm and Distance Block |
| title_sort | self adaptive thresholding approach for automatic water extraction using sentinel 1 sar imagery based on otsu algorithm and distance block |
| topic | self-adaptive thresholding approach automatic water extraction OTSU distance block water extent Sentinel-1 |
| url | https://www.mdpi.com/2072-4292/15/10/2690 |
| work_keys_str_mv | AT jianbotan aselfadaptivethresholdingapproachforautomaticwaterextractionusingsentinel1sarimagerybasedonotsualgorithmanddistanceblock AT yitang aselfadaptivethresholdingapproachforautomaticwaterextractionusingsentinel1sarimagerybasedonotsualgorithmanddistanceblock AT binliu aselfadaptivethresholdingapproachforautomaticwaterextractionusingsentinel1sarimagerybasedonotsualgorithmanddistanceblock AT guangzhao aselfadaptivethresholdingapproachforautomaticwaterextractionusingsentinel1sarimagerybasedonotsualgorithmanddistanceblock AT yumu aselfadaptivethresholdingapproachforautomaticwaterextractionusingsentinel1sarimagerybasedonotsualgorithmanddistanceblock AT mingjiangsun aselfadaptivethresholdingapproachforautomaticwaterextractionusingsentinel1sarimagerybasedonotsualgorithmanddistanceblock AT bowang aselfadaptivethresholdingapproachforautomaticwaterextractionusingsentinel1sarimagerybasedonotsualgorithmanddistanceblock AT jianbotan selfadaptivethresholdingapproachforautomaticwaterextractionusingsentinel1sarimagerybasedonotsualgorithmanddistanceblock AT yitang selfadaptivethresholdingapproachforautomaticwaterextractionusingsentinel1sarimagerybasedonotsualgorithmanddistanceblock AT binliu selfadaptivethresholdingapproachforautomaticwaterextractionusingsentinel1sarimagerybasedonotsualgorithmanddistanceblock AT guangzhao selfadaptivethresholdingapproachforautomaticwaterextractionusingsentinel1sarimagerybasedonotsualgorithmanddistanceblock AT yumu selfadaptivethresholdingapproachforautomaticwaterextractionusingsentinel1sarimagerybasedonotsualgorithmanddistanceblock AT mingjiangsun selfadaptivethresholdingapproachforautomaticwaterextractionusingsentinel1sarimagerybasedonotsualgorithmanddistanceblock AT bowang selfadaptivethresholdingapproachforautomaticwaterextractionusingsentinel1sarimagerybasedonotsualgorithmanddistanceblock |