ABSTRACT

Recent years, convolutional neural networks (CNNs) have attracted broad attention in hyperspectral image (HSI) classification. Most CNN-based HSI classification methods use image patches as the inputs of network. As the size increases, input patches may contain inhomogeneous pixels, which will cause damage to the classification results. In general, it needs to develop hierarchical architectures for CNNs to learn high-level feature representation. However, excessively deep architectures will cause overfitting and gradient vanishing, thus degrades the generalization performance of CNN. To address the above issues, a dual attention dense residual network (DADRN) is proposed for HSI classification. First, a dual attention module (DAM) is designed to reduce the damage of inhomogeneous pixels. It consists of a spectral attention unit and a spatial attention unit, and can achieve adaptive feature refinement. Second, a dense residual subnetwork (DRN) composed of dense convolutional blocks (DCBs) is proposed to extract more discriminative features from the output feature maps of the DAM. Specifically, the DCB achieves feature reuse mechanism through dense connection and channel concatenation operation, and the DRN adopts dense residual connections to alleviate overfitting and gradient vanishing. Experimental results on three benchmark HSI datasets demonstrate that the competitive advantage of proposed method over several state-of-the-art classification methods.

摘要

近年来，卷积神经网络（CNN）在高光谱图像（HSI）分类中引起了广泛关注。大多数基于 CNN 的 HSI 分类方法使用图像块作为网络的输入。随着尺寸的增加，输入补丁可能包含不均匀的像素，这会对分类结果造成损害。一般来说，它需要为 CNN 开发分层架构来学习高级特征表示。然而，过深的架构会导致过拟合和梯度消失，从而降低 CNN 的泛化性能。为了解决上述问题，提出了一种用于 HSI 分类的双重注意力密集残差网络（DADRN）。首先，设计了一个双重注意模块（DAM）来减少不均匀像素的损坏。它由一个光谱注意力单元和一个空间注意力单元组成，可以实现自适应特征细化。其次，提出了由密集卷积块 (DCB) 组成的密集残差子网络 (DRN)，以从 DAM 的输出特征图中提取更具辨别力的特征。具体来说，DCB通过密集连接和通道级联操作实现特征重用机制，DRN采用密集残差连接来缓解过拟合和梯度消失。在三个基准 HSI 数据集上的实验结果表明，所提出的方法相对于几种最先进的分类方法具有竞争优势。提出了由密集卷积块 (DCB) 组成的密集残差子网络 (DRN)，以从 DAM 的输出特征图中提取更具辨别力的特征。具体来说，DCB通过密集连接和通道级联操作实现特征重用机制，DRN采用密集残差连接来缓解过拟合和梯度消失。在三个基准 HSI 数据集上的实验结果表明，所提出的方法相对于几种最先进的分类方法具有竞争优势。提出了由密集卷积块 (DCB) 组成的密集残差子网络 (DRN)，以从 DAM 的输出特征图中提取更具辨别力的特征。具体来说，DCB通过密集连接和通道级联操作实现特征重用机制，DRN采用密集残差连接来缓解过拟合和梯度消失。在三个基准 HSI 数据集上的实验结果表明，所提出的方法相对于几种最先进的分类方法具有竞争优势。