Few-shot hyperspectral image classification with depth feature fusion

doi:10.13474/j.cnki.11-2246.2025.1106

Abstract

Abstract: A depth feature extraction method for hyperspectral image classification is proposed to address the small sample problem.The proposed method first utilizes a pre-trained base model to extract depth maps from hyperspectral images as prior information,which is then fused with spectral information for classification.To fully exploit the rich spectral information in hyperspectral images,a sliding window approach is employed to extract multiple depth maps along the spectral dimension,which are then stacked to form depth features.The method is based on the concept of multi-source remote sensing image fusion but does not require precisely registered multi-source remote sensing images,offering a plug-and-play advantage.Extensive classification experiments on three hyperspectral image datasets validate the effectiveness of the method.

Key words: hyperspectral image, depth feature extraction, few-shot classification, foundation large model

CLC Number:

P237

QIN Jinchun, PEI Hang, LIU Bing, YU Anzhu, CHEN Junming, FAN Junyi. Few-shot hyperspectral image classification with depth feature fusion[J]. Bulletin of Surveying and Mapping, 2025, 0(11): 34-39.

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