  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.
