Cross-Domain Transfer with Self-Supervised Spectral-Spatial Modeling for Hyperspectral Image Classification

• URL: http://arxiv.org/abs/2601.18088v1
• Date: Mon, 26 Jan 2026 02:52:35 GMT
• Title: Cross-Domain Transfer with Self-Supervised Spectral-Spatial Modeling for Hyperspectral Image Classification
• Authors: Jianshu Chao, Tianhua Lv, Qiqiong Ma, Yunfei Qiu, Li Fang, Huifang Shen, Wei Yao,
• Abstract summary: This paper proposes a self-supervised cross-domain transfer framework.<n>It learns transferable spectral-spatial joint representations without source labels.<n> Experimental results demonstrate stable classification performance and strong cross-domain adaptability.
• Score: 5.784164305429653
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Self-supervised learning has demonstrated considerable potential in hyperspectral representation, yet its application in cross-domain transfer scenarios remains under-explored. Existing methods, however, still rely on source domain annotations and are susceptible to distribution shifts, leading to degraded generalization performance in the target domain. To address this, this paper proposes a self-supervised cross-domain transfer framework that learns transferable spectral-spatial joint representations without source labels and achieves efficient adaptation under few samples in the target domain. During the self-supervised pre-training phase, a Spatial-Spectral Transformer (S2Former) module is designed. It adopts a dual-branch spatial-spectral transformer and introduces a bidirectional cross-attention mechanism to achieve spectral-spatial collaborative modeling: the spatial branch enhances structural awareness through random masking, while the spectral branch captures fine-grained differences. Both branches mutually guide each other to improve semantic consistency. We further propose a Frequency Domain Constraint (FDC) to maintain frequency-domain consistency through real Fast Fourier Transform (rFFT) and high-frequency magnitude loss, thereby enhancing the model's capability to discern fine details and boundaries. During the fine-tuning phase, we introduce a Diffusion-Aligned Fine-tuning (DAFT) distillation mechanism. This aligns semantic evolution trajectories through a teacher-student structure, enabling robust transfer learning under low-label conditions. Experimental results demonstrate stable classification performance and strong cross-domain adaptability across four hyperspectral datasets, validating the method's effectiveness under resource-constrained conditions.

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