A novel global-local block spatial-spectral fusion attention model for hyperspectral image classification

Publication Name

Remote Sensing Letters

Abstract

Deep learning brought a new method for hyperspectral image (HSI) classification, in which images are usually pre-processed by reducing their dimensions before being packaged into pieces to be input to the deep network for feature extraction. However, the learning capability of convolutional kernels of fixed dimensions is usually limited, and thus they are inclined to cause losses of feature details. In this paper, a new global-local block spatial-spectral fusion attention (GBSFA) model is proposed. An improved Inception structure is designed to extract the feature information of the global block, and the self-attention mechanism and spatial pyramid pooling (SPP) are applied to focus on the interclass edge feature information of the local block. Combined with long-short term memory (LSTM) networks, the effective information of the spectral dimension is extracted. Finally, the features extracted from the spatial dimension and the spectral dimension are conveyed in the full connection layer for classification training. Experimental results show that the classification accuracy of the proposed approach is higher than that of other comparative methods using small training sets.

Open Access Status

This publication is not available as open access

Volume

13

Issue

4

First Page

343

Last Page

351

Funding Number

61801018

Funding Sponsor

National Natural Science Foundation of China

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Link to publisher version (DOI)

http://dx.doi.org/10.1080/2150704X.2021.2022237