SAR Image Target Segmentation Guided by the Scattering Mechanism-Based Visual Foundation Model
As a typical visual foundation model, SAM has been extensively utilized for optical image segmentation tasks. However, synthetic aperture radar (SAR) employs a unique imaging mechanism, and its images are very different from optical images. Directly transferring a pretrained SAM from optical scenes...
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| Main Authors: | , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-03-01
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| Series: | Remote Sensing |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2072-4292/17/7/1209 |
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| Summary: | As a typical visual foundation model, SAM has been extensively utilized for optical image segmentation tasks. However, synthetic aperture radar (SAR) employs a unique imaging mechanism, and its images are very different from optical images. Directly transferring a pretrained SAM from optical scenes to SAR image instance segmentation tasks can lead to a substantial decline in performance. Therefore, this paper fully integrates the SAR scattering mechanism, and proposes a SAR image target segmentation method guided by the SAR scattering mechanism-based visual foundation model. First, considering the discrete distribution features of strong scattering points in SAR imagery, we develop an edge enhancement morphological adaptor. This adaptor is designed to incorporate a limited set of trainable parameters aimed at effectively boosting the target’s edge morphology, allowing quick fine-tuning within the SAR realm. Second, an adaptive denoising module based on wavelets and soft-thresholding techniques is implemented to reduce the impact of SAR coherent speckle noise, thus improving the feature representation performance. Furthermore, an efficient automatic prompt module based on a deep object detector is built to enhance the ability of rapid target localization in wide-area scenes and improve image segmentation performance. Our approach has been shown to outperform current segmentation methods through experiments conducted on two open-source datasets, SSDD and HRSID. When the ground-truth is used as a prompt, SARSAM improves <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>m</mi><mi>I</mi><mi>O</mi><mi>U</mi></mrow></semantics></math></inline-formula> by more than 10%, and <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>A</mi><msubsup><mi>P</mi><mrow><mi>mask</mi><mspace width="4.pt"></mspace></mrow><mn>50</mn></msubsup></mrow></semantics></math></inline-formula> by more than 5% from the baseline. In addition, the computational cost is greatly reduced because the number of parameters and FLOPs of the structures that require fine-tuning are only 13.5% and 10.1% of the baseline, respectively. |
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| ISSN: | 2072-4292 |