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Domain Information Mining and State-Guided Adaptation Network for Multispectral Image Segmentation.

Boyu Zhao, Mengmeng Zhang, Wei Li

IEEE Transactions on Neural Networks and Learning Systems

|July 22, 2025

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The proposed DSAnet enhances the Segment Anything Model (SAM) for multispectral cross-domain segmentation by mining domain information and using state-guided adaptation, improving performance on diverse datasets.

Area of Science:

Computer Vision
Machine Learning
Remote Sensing

Background:

The Segment Anything Model (SAM) shows promise for cross-scene segmentation but struggles with multispectral cross-domain tasks.
Limitations include insufficient information utilization and inadequate cross-domain adaptation (DA) strategies.

Purpose of the Study:

To improve SAM's performance in multispectral cross-domain segmentation.
To address information utilization and cross-domain adaptation challenges.

Main Methods:

Proposed DSAnet (domain information mining and state-guided adaptation network) combining Masked Autoencoder (MAE) and cross-domain strategies.
Data-level: Style masking learning for feature mining and image reconstruction.
Task-level: Domain state learning and style-guided segmentation for adaptation.

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Main Results:

DSAnet demonstrated superior performance on three multitemporal multispectral image (MSI) datasets.
Outperformed state-of-the-art cross-domain strategies and SAM variants.

Conclusions:

DSAnet effectively enhances SAM for multispectral cross-domain segmentation.
The proposed method improves both data and task levels for better cross-domain adaptation.