数字农科院2.0

TBTA-D2Net: a novel hyperspectral image classification method based on triple-branch ternary-attention mechanism and Dense2Net

文献类型： 外文期刊

作者： Ting Tang;Shengwei Zhang;Shaopeng Liu;Weihong Yan;Xin Pan

作者机构：

关键词： attention mechanism;deep learning;Dense2Net;Hyperspectral image classification;small training set;three branch network structure

期刊名称： International Journal of Remote Sensing

ISSN： 0143-1161

年卷期： 2023 年 44 卷 22 期

页码：

收录情况： SCIE（2023版） ; ; EI（2023版）

摘要： Recently, there has been a growing interest in the hyperspectral image (HSI) classification methods that employ deep learning techniques in small sample cases. To address issues with network degradation and enhance the extraction of discriminative HSI features, this article proposes a TBTA-D2Net network utilizing a triple-branch ternary-attention mechanism and Dense2Net. Furthermore, a new deep model optimizer named Adan is introduced to improve the training speed of the network model. This article takes spatial information as a two-dimensional vector, extracting spectral features as well as spatial-X and spatial-Y features separately in three branches. Each branch includes a Dense2Net bottleneck module and an attention module. Classification is achieved by fusing the features extracted from the three branches. Experimental results on four public datasets indicate that TBTA-D2Net can achieve competitive results over state-of-the-art methods. The code is available at https://github.com/TeresaTing/TBTA-D2Net.

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