Hierarchical attention networks for hyperspectral image classification

Zhengtao Li; Hai Xu; Yaozong Zhang; Tianxu Zhang

doi:10.1117/12.2538278

14 February 2020 Hierarchical attention networks for hyperspectral image classification

Zhengtao Li, Hai Xu, Yaozong Zhang, Tianxu Zhang

Proceedings Volume 11432, MIPPR 2019: Remote Sensing Image Processing, Geographic Information Systems, and Other Applications; 114320D (2020) https://doi.org/10.1117/12.2538278
Event: Eleventh International Symposium on Multispectral Image Processing and Pattern Recognition (MIPPR2019), 2019, Wuhan, China

Abstract

Attention can be interpreted as a method which allocates available computing power to the most informative part of the signal. In deep learning, attention mechanism also helps us to dig out the subtle information. In hyperspectral classification, the discrimination of some land cover types depends on the fine differences of hyperspectral, but most classification methods do not focus on the fine differences between hyperspectral categories. In this paper, a hierarchical group attention classification method is proposed to focus on the differences of categories from coarse to fine, therefore, the fine differences between categories can be obtained to achieve more accurate classification. For comparison and validation, we test the proposed approach with three other classification approaches on Salinas and Indian datasets, and the experiments demonstrate that our proposed approach can distinguish the spectral subtle differences of similar categories more accurately.

Citation Download Citation

Zhengtao Li, Hai Xu, Yaozong Zhang, and Tianxu Zhang "Hierarchical attention networks for hyperspectral image classification", Proc. SPIE 11432, MIPPR 2019: Remote Sensing Image Processing, Geographic Information Systems, and Other Applications, 114320D (14 February 2020); https://doi.org/10.1117/12.2538278

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KEYWORDS

Image classification

Hyperspectral imaging

Sensors

Composites

Convolution

Data processing

Feature extraction

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