Modality-Free Feature Detector and Descriptor for Multimodal Remote Sensing Image Registration
The nonlinear radiation distortions (NRD) among multimodal remote sensing images bring enormous challenges to image registration. The traditional feature-based registration methods commonly use the image intensity or gradient information to detect and describe the features that are sensitive to NRD....
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| Format: | Article |
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MDPI AG
2020-09-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/12/18/2937 |
| _version_ | 1797553989585207296 |
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| author | Song Cui Miaozhong Xu Ailong Ma Yanfei Zhong |
| author_facet | Song Cui Miaozhong Xu Ailong Ma Yanfei Zhong |
| author_sort | Song Cui |
| collection | DOAJ |
| description | The nonlinear radiation distortions (NRD) among multimodal remote sensing images bring enormous challenges to image registration. The traditional feature-based registration methods commonly use the image intensity or gradient information to detect and describe the features that are sensitive to NRD. However, the nonlinear mapping of the corresponding features of the multimodal images often results in failure of the feature matching, as well as the image registration. In this paper, a modality-free multimodal remote sensing image registration method (SRIFT) is proposed for the registration of multimodal remote sensing images, which is invariant to scale, radiation, and rotation. In SRIFT, the nonlinear diffusion scale (NDS) space is first established to construct a multi-scale space. A local orientation and scale phase congruency (LOSPC) algorithm are then used so that the features of the images with NRD are mapped to establish a one-to-one correspondence, to obtain sufficiently stable key points. In the feature description stage, a rotation-invariant coordinate (RIC) system is adopted to build a descriptor, without requiring estimation of the main direction. The experiments undertaken in this study included one set of simulated data experiments and nine groups of experiments with different types of real multimodal remote sensing images with rotation and scale differences (including synthetic aperture radar (SAR)/optical, digital surface model (DSM)/optical, light detection and ranging (LiDAR) intensity/optical, near-infrared (NIR)/optical, short-wave infrared (SWIR)/optical, classification/optical, and map/optical image pairs), to test the proposed algorithm from both quantitative and qualitative aspects. The experimental results showed that the proposed method has strong robustness to NRD, being invariant to scale, radiation, and rotation, and the achieved registration precision was better than that of the state-of-the-art methods. |
| first_indexed | 2024-03-10T16:25:25Z |
| format | Article |
| id | doaj.art-56514e122c9c49de835d01d3526c65b5 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-10T16:25:25Z |
| publishDate | 2020-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-56514e122c9c49de835d01d3526c65b52023-11-20T13:19:30ZengMDPI AGRemote Sensing2072-42922020-09-011218293710.3390/rs12182937Modality-Free Feature Detector and Descriptor for Multimodal Remote Sensing Image RegistrationSong Cui0Miaozhong Xu1Ailong Ma2Yanfei Zhong3The State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, ChinaThe State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, ChinaThe State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, ChinaThe State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, ChinaThe nonlinear radiation distortions (NRD) among multimodal remote sensing images bring enormous challenges to image registration. The traditional feature-based registration methods commonly use the image intensity or gradient information to detect and describe the features that are sensitive to NRD. However, the nonlinear mapping of the corresponding features of the multimodal images often results in failure of the feature matching, as well as the image registration. In this paper, a modality-free multimodal remote sensing image registration method (SRIFT) is proposed for the registration of multimodal remote sensing images, which is invariant to scale, radiation, and rotation. In SRIFT, the nonlinear diffusion scale (NDS) space is first established to construct a multi-scale space. A local orientation and scale phase congruency (LOSPC) algorithm are then used so that the features of the images with NRD are mapped to establish a one-to-one correspondence, to obtain sufficiently stable key points. In the feature description stage, a rotation-invariant coordinate (RIC) system is adopted to build a descriptor, without requiring estimation of the main direction. The experiments undertaken in this study included one set of simulated data experiments and nine groups of experiments with different types of real multimodal remote sensing images with rotation and scale differences (including synthetic aperture radar (SAR)/optical, digital surface model (DSM)/optical, light detection and ranging (LiDAR) intensity/optical, near-infrared (NIR)/optical, short-wave infrared (SWIR)/optical, classification/optical, and map/optical image pairs), to test the proposed algorithm from both quantitative and qualitative aspects. The experimental results showed that the proposed method has strong robustness to NRD, being invariant to scale, radiation, and rotation, and the achieved registration precision was better than that of the state-of-the-art methods.https://www.mdpi.com/2072-4292/12/18/2937image registrationnonlinear radiation distortionsphase congruencymultimodal remote sensing image |
| spellingShingle | Song Cui Miaozhong Xu Ailong Ma Yanfei Zhong Modality-Free Feature Detector and Descriptor for Multimodal Remote Sensing Image Registration Remote Sensing image registration nonlinear radiation distortions phase congruency multimodal remote sensing image |
| title | Modality-Free Feature Detector and Descriptor for Multimodal Remote Sensing Image Registration |
| title_full | Modality-Free Feature Detector and Descriptor for Multimodal Remote Sensing Image Registration |
| title_fullStr | Modality-Free Feature Detector and Descriptor for Multimodal Remote Sensing Image Registration |
| title_full_unstemmed | Modality-Free Feature Detector and Descriptor for Multimodal Remote Sensing Image Registration |
| title_short | Modality-Free Feature Detector and Descriptor for Multimodal Remote Sensing Image Registration |
| title_sort | modality free feature detector and descriptor for multimodal remote sensing image registration |
| topic | image registration nonlinear radiation distortions phase congruency multimodal remote sensing image |
| url | https://www.mdpi.com/2072-4292/12/18/2937 |
| work_keys_str_mv | AT songcui modalityfreefeaturedetectoranddescriptorformultimodalremotesensingimageregistration AT miaozhongxu modalityfreefeaturedetectoranddescriptorformultimodalremotesensingimageregistration AT ailongma modalityfreefeaturedetectoranddescriptorformultimodalremotesensingimageregistration AT yanfeizhong modalityfreefeaturedetectoranddescriptorformultimodalremotesensingimageregistration |