In the realm of artificial intelligence, the emergence of foundation models, backed by high computing capabilities and extensive data, has been revolutionary. Segment Anything Model (SAM), built on the Vision Transformer (ViT) model with millions of parameters and vast training dataset SA-1B, excels in various segmentation scenarios relying on its significance of semantic information and generalization ability. Such achievement of visual foundation model stimulates continuous researches on specific downstream tasks in computer vision. The ClassWise-SAM-Adapter (CWSAM) is designed to adapt the high-performing SAM for landcover classification on space-borne Synthetic Aperture Radar (SAR) images. The proposed CWSAM freezes most of SAM's parameters and incorporates lightweight adapters for parameter efficient fine-tuning, and a classwise mask decoder is designed to achieve semantic segmentation task. This adapt-tuning method allows for efficient landcover classification of SAR images, balancing the accuracy with computational demand. In addition, the task specific input module injects low frequency information of SAR images by MLP-based layers to improve the model performance. Compared to conventional state-of-the-art semantic segmentation algorithms by extensive experiments, CWSAM showcases enhanced performance with fewer computing resources, highlighting the potential of leveraging foundational models like SAM for specific downstream tasks in the SAR domain. The source code is available at: https://github.com/xypu98/CWSAM.

通过使用视觉转换器模型（ViT）和大量训练数据SA-1B构建的段落任意模型（SAM），可以在各种分割场景中实现优秀的语义信息和泛化能力，从而为计算机视觉中的特定下游任务提供了高性能，其中包括用于合成孔径雷达（SAR）图像的土地覆盖分类的CWSAM模型。CWSAM通过轻量级的适配器进行参数高效微调，采用类别掩码解码器实现语义分割任务，在充分实验证明了CWSAM相比于传统的语义分割算法不仅在性能上有所提升，而且在计算资源消耗方面也更为优越，突显了SAM等基础模型在SAR领域特定下游任务中的潜力。

ClassWise-SAM-Adapter：参数高效的微调方法，将任何分割任务适应于SAR领域的语义分割