4.7 Article

Hyperspectral Classification via Global-Local Hierarchical Weighting Fusion Network

出版社

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/JSTARS.2021.3133009

关键词

Feature extraction; Logic gates; Hyperspectral imaging; Task analysis; Convolutional neural networks; Training; Earth; Band grouping; deep learning (DL); features fusion; global-local; hyperspectral image (HSI)

资金

  1. National Natural Science Foundation of China [61977022]
  2. Science Foundation for Distinguished Young Scholars of Hunan Province [2020JJ2017]
  3. Key Research and Development Program of Hunan Province [2019SK2012]
  4. Foundation of Department of Water Resources of Hunan Province [XSKJ2021000-12, XSKJ2021000-13]
  5. Natural Science Foundation of Hunan Province [2020JJ4340, 2021JJ40226]
  6. Foundation of Education Bureau of Hunan Province [20B257, 20B266]
  7. Scientific Research Fund of Education Department of Hunan Province [19A200]
  8. Open Fund of Education Department of Hunan Province [20K062]
  9. Postgraduate Scientific Research Innovation Project of Hunan Province [QL20210254]

向作者/读者索取更多资源

This article proposes a global-local hierarchical weighted fusion end-to-end classification architecture that enhances the discrimination of spectral-spatial features. Experimental results demonstrate its competitiveness in terms of accuracy and generalization.
The fusion of spectral-spatial features based on deep learning has become the focus of research in hyperspectral image (HSI) classification. However, previous deep frameworks based on spectral-spatial fusion usually performed feature aggregation only at the branch ends. Furthermore, only first-order statistical features are considered in the fusion process, which is not conducive to improving the discrimination of spectral-spatial features. This article proposes a global-local hierarchical weighted fusion end-to-end classification architecture. The architecture includes two subnetworks for spectral classification and spatial classification. For the spectral subnetwork, two band-grouping strategies are designed, and bidirectional long short-term memory is used to capture spectral context information from global to local perspectives. For the spatial subnetwork, a pooling strategy based on local attention is combined to construct a global-local pooling fusion module to enhance the discriminability of spatial features learned by a convolutional neural network. For the fusion stage, a hierarchical weighting fusion mechanism is developed to obtain the nonlinear relationship between both spectral and spatial features. The experimental results on four real HSI datasets and a GF-5 satellite dataset demonstrate that the method proposed is more competitive in terms of accuracy and generalization.

作者

我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。

评论

主要评分

4.7
评分不足

次要评分

新颖性
-
重要性
-
科学严谨性
-
评价这篇论文

推荐

暂无数据
暂无数据