Abstract
Although most of deep-learning-based hyperspectral image (HSI) classification methods achieve great performance, there still remains a challenge to utilize small-size training samples to remarkably enhance the classification accuracy. To tackle this challenge, a novel two-branch spectral–spatial-feature attention network (TSSFAN) for HSI classification is proposed in this paper. Firstly, two inputs with different spectral dimensions and spatial sizes are constructed, which can not only reduce the redundancy of the original dataset but also accurately explore the spectral and spatial features. Then, we design two parallel 3DCNN branches with attention modules, in which one focuses on extracting spectral features and adaptively learning the more discriminative spectral channels, and the other focuses on exploring spatial features and adaptively learning the more discriminative spatial structures. Next, the feature attention module is constructed to automatically adjust the weights of different features based on their contributions for classification to remarkably improve the classification performance. Finally, we design the hybrid architecture of 3D–2DCNN to acquire the final classification result, which can significantly decrease the sophistication of the network. Experimental results on three HSI datasets indicate that our presented TSSFAN method outperforms several of the most advanced classification methods.
Highlights
Hyperspectral image (HSI) contains rich features of ground [4,5,6], in which spatial features and spectral features are both included for each pixel
CNN isdesigned originally for the recognition of two-dimensional so is originally fordesigned the recognition of two-dimensional images, so images, the tradithe traditional network structure is a two-dimensional convolutional neural network tional network structure is a two-dimensional convolutional neural network [51,52,53]
The superiorities of the presented two-branch spectral–spatial-feature attention network (TSSFAN) by creating two inputs with different size to, respectively, emphasize accurately extracting spectral information and spatial information, designing two-branch 3DCNN with attention modules to focus on more discriminative spectral channels and spatial structures, and constructing the feature attention module to concentrate on the feature contributing more to the classification tasks are completely verifiable
Summary
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. Various novel classification methods have been introduced in the past several years, which try to improve the classification performance by incorporating spatial information One category in these methods attempts to design diverse feature extraction approaches, including the local binary pattern (LBP) histogram feature extraction [20] and extended morphological profiles (EMP) extraction [21], etc. Compared with the methods that only use spectral information, these methods can effectively enhance the classification performance All these classification methods mentioned above design and extract features based on specific data with different structures. They have no universality for diverse hyperspectral datasets and cannot simultaneously achieve good results for data with different structures.
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