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: this https URL

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

ClassWise-SAM-Adapter: Parameter Efficient Fine-tuning Adapts Segment  Anything to SAR Domain for Semantic Segmentation

We tackle the problem of class incremental learning (CIL) in the realm of
landcover classification from optical remote sensing (RS) images in this paper.
The paradigm of CIL has recently gained much prominence given the fact that
data are generally obtained in a sequential manner for real-world phenomenon.
However, CIL has not been extensively considered yet in the domain of RS
irrespective of the fact that the satellites tend to discover new classes at
different geographical locations temporally. With this motivation, we propose a
novel CIL framework inspired by the recent success of replay-memory based
approaches and tackling two of their shortcomings. In order to reduce the
effect of catastrophic forgetting of the old classes when a new stream arrives,
we learn a curriculum of the new classes based on their similarity with the old
classes. This is found to limit the degree of forgetting substantially. Next
while constructing the replay memory, instead of randomly selecting samples
from the old streams, we propose a sample selection strategy which ensures the
selection of highly confident samples so as to reduce the effects of noise. We
observe a sharp improvement in the CIL performance with the proposed
components. Experimental results on the benchmark NWPU-RESISC45, PatternNet,
and EuroSAT datasets confirm that our method offers improved
stability-plasticity trade-off than the literature.

我们在这篇论文中解决了光学遥感图像的土地覆盖分类领域中的逐类增量学习（CIL）问题。我们提出了一个新颖的 CIL 框架，通过受到重播存储器方法的启发并解决了其中的两个缺点。实验证明，我们的方法在 CIL 性能上表现出明显的改进。

基于信息子集选择的高效课程化连续学习方法在遥感场景分类中的应用

Efficient Curriculum based Continual Learning with Informative Subset  Selection for Remote Sensing Scene Classification

Deep learning based landcover classification algorithms have recently been
proposed in literature. In hyperspectral images (HSI) they face the challenges
of large dimensionality, spatial variability of spectral signatures and
scarcity of labeled data. In this article we propose an end-to-end deep
learning architecture that extracts band specific spectral-spatial features and
performs landcover classification. The architecture has fewer independent
connection weights and thus requires lesser number of training data. The method
is found to outperform the highest reported accuracies on popular hyperspectral
image data sets.

本文提出一种基于端到端的深度学习架构，在高光谱图像分类方面表现出较高的准确度，可减少独立连接权重和训练数据的数量。