Registration of Large Optical and SAR Images with Non-Flat Terrain by Investigating Reliable Sparse Correspondences
Optical and SAR image registration is the primary procedure to exploit the complementary information from the two different image modal types. Although extensive research has been conducted to narrow down the vast radiometric and geometric gaps so as to extract homogeneous characters for feature poi...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-09-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/15/18/4458 |
| _version_ | 1797577236693385216 |
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| author | Han Zhang Lin Lei Weiping Ni Kenan Cheng Tao Tang Peizhong Wang Gangyao Kuang |
| author_facet | Han Zhang Lin Lei Weiping Ni Kenan Cheng Tao Tang Peizhong Wang Gangyao Kuang |
| author_sort | Han Zhang |
| collection | DOAJ |
| description | Optical and SAR image registration is the primary procedure to exploit the complementary information from the two different image modal types. Although extensive research has been conducted to narrow down the vast radiometric and geometric gaps so as to extract homogeneous characters for feature point matching, few works have considered the registration issue for non-flat terrains, which will bring in more difficulties for not only sparse feature point matching but also outlier removal and geometric relationship estimation. This article addresses these issues with a novel and effective optical-SAR image registration framework. Firstly, sparse feature points are detected based on the phase congruency moment map of the textureless SAR image (SAR-PC-Moment), which helps to identify salient local regions. Then a template matching process using very large local image patches is conducted, which increases the matching accuracy by a significant margin. Secondly, a mutual verification-based initial outlier removal method is proposed, which takes advantage of the different mechanisms of sparse and dense matching and requires no geometric consistency assumption within the inliers. These two procedures will produce a putative correspondence feature point (CP) set with a low outlier ratio and high reliability. In the third step, the putative CPs are used to segment the large input image of non-flat terrain into dozens of locally flat areas using a recursive random sample consensus (RANSAC) method, with each locally flat area co-registered using an affine transformation. As for the mountainous areas with sharp elevation variations, anchor CPs are first identified, and then optical flow-based pixelwise dense matching is conducted. In the experimental section, ablation studies using four precisely co-registered optical-SAR image pairs of flat terrain quantitatively verify the effectiveness of the proposed SAR-PC-Moment-based feature point detector, big template matching strategy, and mutual verification-based outlier removal method. Registration results on four 1 m-resolution non-flat image pairs prove that the proposed framework is able to produce robust and quite accurate registration results. |
| first_indexed | 2024-03-10T22:06:17Z |
| format | Article |
| id | doaj.art-5bb9e42874c2496cbad2696cadf1673f |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-10T22:06:17Z |
| publishDate | 2023-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-5bb9e42874c2496cbad2696cadf1673f2023-11-19T12:47:59ZengMDPI AGRemote Sensing2072-42922023-09-011518445810.3390/rs15184458Registration of Large Optical and SAR Images with Non-Flat Terrain by Investigating Reliable Sparse CorrespondencesHan Zhang0Lin Lei1Weiping Ni2Kenan Cheng3Tao Tang4Peizhong Wang5Gangyao Kuang6College of Electronic Science and Technology, National University of Defense Technology, Changsha 410073, ChinaCollege of Electronic Science and Technology, National University of Defense Technology, Changsha 410073, ChinaNorthwest Institute of Nuclear Technology, Xi’an 710024, ChinaNorthwest Institute of Nuclear Technology, Xi’an 710024, ChinaCollege of Electronic Science and Technology, National University of Defense Technology, Changsha 410073, ChinaNorthwest Institute of Nuclear Technology, Xi’an 710024, ChinaCollege of Electronic Science and Technology, National University of Defense Technology, Changsha 410073, ChinaOptical and SAR image registration is the primary procedure to exploit the complementary information from the two different image modal types. Although extensive research has been conducted to narrow down the vast radiometric and geometric gaps so as to extract homogeneous characters for feature point matching, few works have considered the registration issue for non-flat terrains, which will bring in more difficulties for not only sparse feature point matching but also outlier removal and geometric relationship estimation. This article addresses these issues with a novel and effective optical-SAR image registration framework. Firstly, sparse feature points are detected based on the phase congruency moment map of the textureless SAR image (SAR-PC-Moment), which helps to identify salient local regions. Then a template matching process using very large local image patches is conducted, which increases the matching accuracy by a significant margin. Secondly, a mutual verification-based initial outlier removal method is proposed, which takes advantage of the different mechanisms of sparse and dense matching and requires no geometric consistency assumption within the inliers. These two procedures will produce a putative correspondence feature point (CP) set with a low outlier ratio and high reliability. In the third step, the putative CPs are used to segment the large input image of non-flat terrain into dozens of locally flat areas using a recursive random sample consensus (RANSAC) method, with each locally flat area co-registered using an affine transformation. As for the mountainous areas with sharp elevation variations, anchor CPs are first identified, and then optical flow-based pixelwise dense matching is conducted. In the experimental section, ablation studies using four precisely co-registered optical-SAR image pairs of flat terrain quantitatively verify the effectiveness of the proposed SAR-PC-Moment-based feature point detector, big template matching strategy, and mutual verification-based outlier removal method. Registration results on four 1 m-resolution non-flat image pairs prove that the proposed framework is able to produce robust and quite accurate registration results.https://www.mdpi.com/2072-4292/15/18/4458optical and SAR imageimage registrationnon-flat terrainphase congruencytemplate matchingoutlier removal |
| spellingShingle | Han Zhang Lin Lei Weiping Ni Kenan Cheng Tao Tang Peizhong Wang Gangyao Kuang Registration of Large Optical and SAR Images with Non-Flat Terrain by Investigating Reliable Sparse Correspondences Remote Sensing optical and SAR image image registration non-flat terrain phase congruency template matching outlier removal |
| title | Registration of Large Optical and SAR Images with Non-Flat Terrain by Investigating Reliable Sparse Correspondences |
| title_full | Registration of Large Optical and SAR Images with Non-Flat Terrain by Investigating Reliable Sparse Correspondences |
| title_fullStr | Registration of Large Optical and SAR Images with Non-Flat Terrain by Investigating Reliable Sparse Correspondences |
| title_full_unstemmed | Registration of Large Optical and SAR Images with Non-Flat Terrain by Investigating Reliable Sparse Correspondences |
| title_short | Registration of Large Optical and SAR Images with Non-Flat Terrain by Investigating Reliable Sparse Correspondences |
| title_sort | registration of large optical and sar images with non flat terrain by investigating reliable sparse correspondences |
| topic | optical and SAR image image registration non-flat terrain phase congruency template matching outlier removal |
| url | https://www.mdpi.com/2072-4292/15/18/4458 |
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