Abstract
In recent years, due to its powerful feature extraction ability, the deep learning method has been widely used in hyperspectral image classification tasks. However, the features extracted by classical deep learning methods have limited discrimination ability, resulting in unsatisfactory classification performance. In addition, due to the limited data samples of hyperspectral images (HSIs), how to achieve high classification performance under limited samples is also a research hotspot. In order to solve the above problems, this paper proposes a deep learning network framework named the three-dimensional coordination attention mechanism network (3DCAMNet). In this paper, a three-dimensional coordination attention mechanism (3DCAM) is designed. This attention mechanism can not only obtain the long-distance dependence of the spatial position of HSIs in the vertical and horizontal directions, but also obtain the difference of importance between different spectral bands. In order to extract the spectral and spatial information of HSIs more fully, a convolution module based on convolutional neural network (CNN) is adopted in this paper. In addition, the linear module is introduced after the convolution module, which can extract more fine advanced features. In order to verify the effectiveness of 3DCAMNet, a series of experiments were carried out on five datasets, namely, Indian Pines (IP), Pavia University (UP), Kennedy Space Center (KSC), Salinas Valley (SV), and University of Houston (HT). The OAs obtained by the proposed method on the five datasets were 95.81%, 97.01%, 99.01%, 97.48%, and 97.69% respectively, 3.71%, 9.56%, 0.67%, 2.89% and 0.11% higher than the most advanced A2S2K-ResNet. Experimental results show that, compared with some state-of-the-art methods, 3DCAMNet not only has higher classification performance, but also has stronger robustness.
Highlights
College of Communication and Electronic Engineering, Qiqihar University, Qiqihar 161000, China; College of Information and Communication Engineering, Dalian Nationalities University, Abstract: In recent years, due to its powerful feature extraction ability, the deep learning method has been widely used in hyperspectral image classification tasks
The proposed method 3DCAMNet is compared with other advanced classification methods, including support vector machine (SVM) [17], spatial–spectral residuals network (SSRN) [52], PyResNet [40], double-branch multi-attention (DBMA) [53], double-branch dual attention (DBDA) [55], Hybrid-SN [35], and A2 S2 K-ResNet [57]
SVM is a classification method based on the radial basis kernel function (RBF)
Summary
College of Communication and Electronic Engineering, Qiqihar University, Qiqihar 161000, China; College of Information and Communication Engineering, Dalian Nationalities University, Abstract: In recent years, due to its powerful feature extraction ability, the deep learning method has been widely used in hyperspectral image classification tasks. In order to solve the above problems, this paper proposes a deep learning network framework named the three-dimensional coordination attention mechanism network (3DCAMNet). In the early stage of hyperspectral image classification, many classical methods appeared, such as feature mining technology [15] and Markov random field [16]. These methods cannot effectively extract features with strong discrimination ability. The hyperspectral image classification method based on convolutional neural network (CNN) has attracted extensive attention because it can effectively deal with nonlinear structure data [21,22,23,24,25,26,27,28]. A two-dimensional stacked autoencoder (2DSAE) was used to attempt to extract depth features from space [32]
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