Deep learning is now receiving widespread attention in hyperspectral image (HSI) classification. However, due to the imbalance between a huge number of weights and limited training samples, many problems and difficulties have arisen from the use of deep learning methods in HSI classification. To handle this issue, an efficient deep learning-based HSI classification method, namely, spatial-aware network (SANet) has been proposed in this paper. The main idea of SANet is to exploit discriminative spectral-spatial features by incorporating prior domain knowledge into the deep architecture, where edge-preserving side window filters are used as the convolution kernels. Thus, SANet has a small number of parameters to optimize. This makes it fit for small sample sizes. Furthermore, SANet is able not only to aware local spatial structures using side window filtering framework, but also to learn discriminative features making use of the hierarchical architecture and limited label information. The experimental results on four widely used HSI data sets demonstrate that our proposed SANet significantly outperforms many state-of-the-art approaches when only a small number of training samples are available.
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AGRIS - 国际农业科技情报系统
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Remote Sensing
卷
13
期
16
ISSN
2072-4292
出版者
Multidisciplinary Digital Publishing Institute
其它主题
Deep learning; Feature learning; Hyperspectral image classification; Spatial-aware network
语言
英语
类型
Journal Article
2025-07-18
AGRIS AP
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