GPU Accelerated Processing Method for Feature Point Extraction and Matching in Satellite SAR Images
This paper addresses the challenge of extracting feature points and image matching in Synthetic Aperture Radar (SAR) satellite images, particularly focusing on large-scale embedding. The widely used Scale Invariant Transform (SIFT) algorithm, successful in computer vision and optical satellite image...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-02-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/14/4/1528 |
| _version_ | 1797299037067542528 |
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| author | Lei Dong Niangang Jiao Tingtao Zhang Fangjian Liu Hongjian You |
| author_facet | Lei Dong Niangang Jiao Tingtao Zhang Fangjian Liu Hongjian You |
| author_sort | Lei Dong |
| collection | DOAJ |
| description | This paper addresses the challenge of extracting feature points and image matching in Synthetic Aperture Radar (SAR) satellite images, particularly focusing on large-scale embedding. The widely used Scale Invariant Transform (SIFT) algorithm, successful in computer vision and optical satellite image matching, faces challenges when applied to satellite SAR images due to the presence of speckle noise, leading to increased matching errors. The SAR–SIFT method is explored and analyzed in-depth, considering the unique characteristics of satellite SAR images. To enhance the efficiency of matching identical feature points in two satellite SAR images, the paper proposes a Graphics Processing Unit (GPU) mapping implementation based on the SAR–SIFT algorithm. The paper introduces a multi-GPU collaborative acceleration strategy for SAR image matching. This strategy addresses the challenge of matching feature points in the region and embedding multiple SAR images in large areas. The goal is to achieve efficient matching processing of multiple SAR images in extensive geographical regions. The proposed multi-GPU collaborative acceleration algorithm is validated through experiments involving feature point extraction and matching using 21 GF-3 SAR images. The results demonstrate the feasibility and efficiency of the algorithm in enhancing the processing speed of matching feature points in large-scale satellite SAR images. Overall, the paper contributes to the advancement of SAR image processing techniques, specifically in feature point extraction and matching in large-scale applications. |
| first_indexed | 2024-03-07T22:43:42Z |
| format | Article |
| id | doaj.art-97da2f43abd145e3b8ad974def431336 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-07T22:43:42Z |
| publishDate | 2024-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-97da2f43abd145e3b8ad974def4313362024-02-23T15:06:18ZengMDPI AGApplied Sciences2076-34172024-02-01144152810.3390/app14041528GPU Accelerated Processing Method for Feature Point Extraction and Matching in Satellite SAR ImagesLei Dong0Niangang Jiao1Tingtao Zhang2Fangjian Liu3Hongjian You4Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, ChinaAerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, ChinaAerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, ChinaAerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, ChinaAerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, ChinaThis paper addresses the challenge of extracting feature points and image matching in Synthetic Aperture Radar (SAR) satellite images, particularly focusing on large-scale embedding. The widely used Scale Invariant Transform (SIFT) algorithm, successful in computer vision and optical satellite image matching, faces challenges when applied to satellite SAR images due to the presence of speckle noise, leading to increased matching errors. The SAR–SIFT method is explored and analyzed in-depth, considering the unique characteristics of satellite SAR images. To enhance the efficiency of matching identical feature points in two satellite SAR images, the paper proposes a Graphics Processing Unit (GPU) mapping implementation based on the SAR–SIFT algorithm. The paper introduces a multi-GPU collaborative acceleration strategy for SAR image matching. This strategy addresses the challenge of matching feature points in the region and embedding multiple SAR images in large areas. The goal is to achieve efficient matching processing of multiple SAR images in extensive geographical regions. The proposed multi-GPU collaborative acceleration algorithm is validated through experiments involving feature point extraction and matching using 21 GF-3 SAR images. The results demonstrate the feasibility and efficiency of the algorithm in enhancing the processing speed of matching feature points in large-scale satellite SAR images. Overall, the paper contributes to the advancement of SAR image processing techniques, specifically in feature point extraction and matching in large-scale applications.https://www.mdpi.com/2076-3417/14/4/1528image matchingSAR-SIFTGPU mappingmulti-GPU collaborative acceleration |
| spellingShingle | Lei Dong Niangang Jiao Tingtao Zhang Fangjian Liu Hongjian You GPU Accelerated Processing Method for Feature Point Extraction and Matching in Satellite SAR Images Applied Sciences image matching SAR-SIFT GPU mapping multi-GPU collaborative acceleration |
| title | GPU Accelerated Processing Method for Feature Point Extraction and Matching in Satellite SAR Images |
| title_full | GPU Accelerated Processing Method for Feature Point Extraction and Matching in Satellite SAR Images |
| title_fullStr | GPU Accelerated Processing Method for Feature Point Extraction and Matching in Satellite SAR Images |
| title_full_unstemmed | GPU Accelerated Processing Method for Feature Point Extraction and Matching in Satellite SAR Images |
| title_short | GPU Accelerated Processing Method for Feature Point Extraction and Matching in Satellite SAR Images |
| title_sort | gpu accelerated processing method for feature point extraction and matching in satellite sar images |
| topic | image matching SAR-SIFT GPU mapping multi-GPU collaborative acceleration |
| url | https://www.mdpi.com/2076-3417/14/4/1528 |
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