作者：骆继明, 朱彤珺, 黄明明, 黄全振, 张洋, 赵俊皓, 杨镰朴

作者单位：河南工程学院电气信息工程学院, 河南 郑州 451191

关键词：图像分类;高光谱图像;神经网络;空间-光谱注意力;多尺度

摘要：

针对高光谱图像数据高维的特点，为进一步提高图像分类准确率，设计一种融合注意力机制的三维空洞卷积神经网络模型用于高光谱分类问题。该方法以3D卷积为基础，使用多尺寸卷积核策略，从不同尺度提取高光谱图像的特征信息；使用空洞结构卷积核，可有效提取特征信息，同时增加网络的感受野。提出一种空间-光谱注意力模块，自适应聚焦信息，增加高光谱图像空间、光谱的特征表达能力。提出的方法在University of Pavia 和 Indian Pines等公开数据集上测试，分别取得99.61%、99.58%的总体分类准确率。与SVM、2D-CNN、3D-CNN、RES-3D-CNN算法进行比较，该文提出的算法在准确率和分类性能上优于其他算法。

Fusion attention hole residual networks for hyperspectral image classification
LUO Jiming, ZHU Tongjun, HUANG Mingming, HUANG Quanzhen, ZHANG Yang, ZHAO Junhao, YANG Lianpu
School of Electrical Information Engineering, Henan Institute of Engineering, Zhengzhou 451191, China
Abstract: Aiming at the high-dimensional characteristics of hyperspectral image data, in order to further improve the accuracy of image classification, a three-dimensional convolutional neural network model is designed for the hyperspectral classification problem. This method is based on 3D convolution and uses a multi-size convolution kernel strategy to extract the characteristic information of hyperspectral images from different scales. The use of an dilated convolution kernel effectively extracts feature information and expands the receptive field of the network. We propose a spatial-spectral attention model block that adaptively focuses on pertinent information, thereby augmenting the feature representation capability of the hyperspectral image space and spectrum. The proposed method was tested on public data sets such as University of Pavia and Indian Pines, and achieved overall classification accuracy of 99.61% and 99.58%, respectively. Compared with the results of SVM, 2D-CNN, 3D-CNN, RES-3D-CNN and other algorithms, the algorithm proposed in this paper is superior to other algorithms in accuracy and classification performance.
Keywords: image classification;hyperspectral image;neural networks;spatial spectral attention;multiscale
2023, 49(9):111-119  收稿日期: 2022-12-7;收到修改稿日期: 2023-2-3
基金项目: 河南省科技攻关项目（212102210022）；河南省自然科学基金青年科学基金项目（212300410127）；河南省科技攻关项目（212102210014）
作者简介: 骆继明（1975-），男，河南光山县人，副教授，硕士，主要从事图像处理、发电机控制设计方面的研究。
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