数字农科院2.0

基于卷积神经网络与注意力机制的高光谱图像分类

文献类型： 中文期刊

作者： 高玉鹏;闫伟红;潘新

作者机构：

关键词： 高光谱图像分类(HSI);卷积神经网络(CNN);coordinate attention;Transformer

期刊名称：光电子·激光

ISSN： 1005-0086

年卷期： 2024 年 35 卷 05 期

页码：

收录情况： 北大核心（2023版） ; ; CSCD（2023-2024年度）

摘要： 由于浅层卷积神经网络(convolutional neural network, CNN)模型感受野的限制，无法捕获远距离特征，在高光谱图像(hyperspectral image, HSI)分类问题中无法充分利用图像空间-光谱信息，很难获得较高精度的分类结果。针对上述问题，本文提出了一种基于卷积神经网络与注意力机制的模型(model based on convolutional neural network and attention mechanism, CNNAM),该模型利用CA (coordinate attention)对图像通道数据进行位置编码，并利用以自注意力机制为核心架构的Transformer模块对其进行远距离特征提取以解决CNN感受野的限制问题。CNNAM在Indian Pines和Salinas两个数据集上得到的总体分类精度分别为97.63%和99.34%,对比于其他模型，本文提出的模型表现出更好的分类性能。另外，本文以是否结合CA为参考进行了消融实验，并证明了CA在CNNAM中发挥重要作用。实验证明将传统CNN与注意力机制相结合可以在HSI分类问题中获得更高的分类精度。

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