Spatial-Aware Network for Hyperspectral Image Classification

Remote Sensing

Yicong Zhou¹  Yantao Wei² 

[1] Department of Computer and Information Science, University of Macau, Macau 999078, China;Faculty of Artificial Intelligence in Education, Central China Normal University, Wuhan 430079, China;

关键词: hyperspectral image classification;    feature learning;    spatial-aware network;    deep learning;   

DOI  :  10.3390/rs13163232

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown