SAR-MINF: A Novel SAR Image Descriptor and Matching Method for Large-Scale Multidegree Overlapping Tie Point Automatic Extraction
The automatic extraction of large-scale tie points (TPs) for Synthetic Aperture Radar (SAR) images is crucial for generating SAR Digital Orthophoto Maps (DOMs). This task involves matching SAR images under various conditions, such as different resolutions, incidence angles, and orbital directions, w...
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MDPI AG
2024-12-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/16/24/4696 |
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| _version_ | 1846102846665129984 |
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| author | Shuo Li Xiongwen Yang Xiaolei Lv Jian Li |
| author_facet | Shuo Li Xiongwen Yang Xiaolei Lv Jian Li |
| author_sort | Shuo Li |
| collection | DOAJ |
| description | The automatic extraction of large-scale tie points (TPs) for Synthetic Aperture Radar (SAR) images is crucial for generating SAR Digital Orthophoto Maps (DOMs). This task involves matching SAR images under various conditions, such as different resolutions, incidence angles, and orbital directions, which is highly challenging. To address the feature extraction challenges of different SAR images, we propose a Gamma Modulated Phase Congruency (GMPC) model. This improved phase congruency model is defined by a Gamma Modulation Filter (GMF) and an adaptive noise model. Additionally, to reduce layover interference in SAR images, we introduce a GMPC-Harris feature point extraction method with layover perception. We also propose a matching method based on the SAR Modality Independent Neighborhood Fusion (SAR-MINF) descriptor, which fuses feature information from different neighborhoods. Furthermore, we present a graph-based overlap extraction algorithm and establish an automated workflow for large-scale TP extraction. Experiments show that the proposed SAR-MINF matching method increases the Correct Match Rate (CMR) by an average of 31.2% and the matching accuracy by an average of 57.8% compared with other prevalent SAR image matching algorithms. The proposed TP extraction algorithm can extract full-degree TPs with an accuracy of less than 0.5 pixels for more than 98% of 2-degree TPs and over 95% of multidegree TPs, meeting the requirements of DOM production. |
| format | Article |
| id | doaj-art-4847bc242e6446f18fdca5451dc54c29 |
| institution | Kabale University |
| issn | 2072-4292 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj-art-4847bc242e6446f18fdca5451dc54c292024-12-27T14:50:55ZengMDPI AGRemote Sensing2072-42922024-12-011624469610.3390/rs16244696SAR-MINF: A Novel SAR Image Descriptor and Matching Method for Large-Scale Multidegree Overlapping Tie Point Automatic ExtractionShuo Li0Xiongwen Yang1Xiaolei Lv2Jian Li3CNPC USA Corporation, Beijing 100028, ChinaCNPC USA Corporation, Beijing 100028, ChinaKey Laboratory of Technology in Geo-Spatial Information Processing and Application System, Chinese Academy of Sciences, Beijing 100190, ChinaBeijing Xingtiandi Information Technology Co., Ltd., Beijing 102200, ChinaThe automatic extraction of large-scale tie points (TPs) for Synthetic Aperture Radar (SAR) images is crucial for generating SAR Digital Orthophoto Maps (DOMs). This task involves matching SAR images under various conditions, such as different resolutions, incidence angles, and orbital directions, which is highly challenging. To address the feature extraction challenges of different SAR images, we propose a Gamma Modulated Phase Congruency (GMPC) model. This improved phase congruency model is defined by a Gamma Modulation Filter (GMF) and an adaptive noise model. Additionally, to reduce layover interference in SAR images, we introduce a GMPC-Harris feature point extraction method with layover perception. We also propose a matching method based on the SAR Modality Independent Neighborhood Fusion (SAR-MINF) descriptor, which fuses feature information from different neighborhoods. Furthermore, we present a graph-based overlap extraction algorithm and establish an automated workflow for large-scale TP extraction. Experiments show that the proposed SAR-MINF matching method increases the Correct Match Rate (CMR) by an average of 31.2% and the matching accuracy by an average of 57.8% compared with other prevalent SAR image matching algorithms. The proposed TP extraction algorithm can extract full-degree TPs with an accuracy of less than 0.5 pixels for more than 98% of 2-degree TPs and over 95% of multidegree TPs, meeting the requirements of DOM production.https://www.mdpi.com/2072-4292/16/24/4696image matchingtie pointsphase congruencySAR layoveroverlap |
| spellingShingle | Shuo Li Xiongwen Yang Xiaolei Lv Jian Li SAR-MINF: A Novel SAR Image Descriptor and Matching Method for Large-Scale Multidegree Overlapping Tie Point Automatic Extraction Remote Sensing image matching tie points phase congruency SAR layover overlap |
| title | SAR-MINF: A Novel SAR Image Descriptor and Matching Method for Large-Scale Multidegree Overlapping Tie Point Automatic Extraction |
| title_full | SAR-MINF: A Novel SAR Image Descriptor and Matching Method for Large-Scale Multidegree Overlapping Tie Point Automatic Extraction |
| title_fullStr | SAR-MINF: A Novel SAR Image Descriptor and Matching Method for Large-Scale Multidegree Overlapping Tie Point Automatic Extraction |
| title_full_unstemmed | SAR-MINF: A Novel SAR Image Descriptor and Matching Method for Large-Scale Multidegree Overlapping Tie Point Automatic Extraction |
| title_short | SAR-MINF: A Novel SAR Image Descriptor and Matching Method for Large-Scale Multidegree Overlapping Tie Point Automatic Extraction |
| title_sort | sar minf a novel sar image descriptor and matching method for large scale multidegree overlapping tie point automatic extraction |
| topic | image matching tie points phase congruency SAR layover overlap |
| url | https://www.mdpi.com/2072-4292/16/24/4696 |
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