A hyperspectral unmixing and few-shot classification method based on 3DCAE network

doi:10.6046/zrzyyg.2023260

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Abstract

The rapid development of hyperspectral remote sensing technology in China fully ensures the effective application of large-scale surface feature classification. However, achieving high-precision classification under few-spot conditions while fully leveraging hyperspectral spatial-spectral information remains challenging. This study developed a 3D convolutional autoencoder (3D-CAE) network guided by physical constraints from mixed pixel decomposition. This approach enables accurate estimation of endmember abundance while effectively expressing regularized spatial-spectral features of hyperspectral data. In combination with a support vector machine (SVM) classifier, the method achieves hyperspectral classification under few-spot conditions. The classification performance of various models was evaluated at different sampling rates. To validate the proposed method, this study conducted experiments including comparisons with traditional hyperspectral feature extraction and classification methods, such as supervised classification approaches. The classification performance of various models was also evaluated at different sampling rates. The experimental results demonstrate that the proposed hyperspectral classification method has a significant advantage of accuracy, achieving a mean intersection over union (mIoU) of 0.829, which was close to 0.8 even at a low sampling rate of 1/200, surpassing its counterparts. These results confirm that the proposed method exhibits robustness under few-spot conditions. This study provides a valuable technical reference for addressing hyperspectral classification challenges under few-spot conditions.

Keywords deep learning hyperspectral imagery classification convolutional neural network unmixing

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Issue Date: 17 February 2025

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	Chuan HUANG
	Yaqin LI
	Yueran QI
	Xiaoyan WEI
	Yuanzheng SHAO

Cite this article:

Chuan HUANG,Yaqin LI,Yueran QI, et al. A hyperspectral unmixing and few-shot classification method based on 3DCAE network[J]. Remote Sensing for Natural Resources, 2025, 37(1): 8-14.

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https://www.gtzyyg.com/EN/10.6046/zrzyyg.2023260 OR https://www.gtzyyg.com/EN/Y2025/V37/I1/8

Fig.1 Hyperspectral image and endmember spectra

Fig.2 The hyperspectral classification workflow based on 3D-CAE

Tab.1 Network composition and related parameter settings

Tab.2 Model prediction of the abundance from different endmembers compared to the true values

Fig.3 Comparison chart of classification results predicted by different models versus ground truth

Tab.3 Comparison table of prediction accuracy for different models

Fig.4 Trend chart of classification accuracy variation for various models at different sampling rates

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