基于全局注意力信息交互的高光谱图像分类

近年来,研究者们发现基于双分支结构的高光谱图像分类方法可以更有效地提取图像的光谱特征和空间特征用于分类.但在双分支结构中,各分支只侧重于细化、提取光谱特征或空间特征,忽略了对光谱-空间跨维特征交互的研究,且两分支各自提取的部分交互不明显,因此影响了分类的性能.针对这一问题,本文提出了一种基于全局注意力信息交互的高光谱图像分类方法.首先采用密集连接网络分两个分支分别细化图像的光谱特征和空间特征,然后结合全局注意力机制(GAM)得到通道全局注意力特征和空间全局注意力特征,最后通过一个信息交互的模块实现光谱和空间信息的交互,更充分地利用光谱和空间信息实现分类.本文提出的方法分别在Pavia University(PU)和Salinas Valley (SV)两个数据集上进行了实验,相较于其他的4种方法,本文提出的方法在分类性能上取得了明显的提升.

Hyperspectral Image Classification Based on Global Attention Information Interaction

In recent years, researchers have found that the hyperspectral image classification method based on dual branch structure can more effectively extract the spectral and spatial features of the image for classification. However, in the dual branch structure, each branch only focuses on refining and extracting spectral or spatial features, with the study on cross-dimensional spectral-spatial feature interaction ignored, and the partial interaction extracted by the two branches respectively is not obvious, which affects the performance of classification. To solve this problem, this study proposes a hyperspectral image classification method based on global attention information interaction. First, the dense connection network is used to divide the image into two branches to refine the spectral and spatial features, respectively, and then the channel global attention features and spatial global attention features are obtained by combining the global attention mechanism (GAM). Finally, an information interaction module is used to realize the interaction of spectral and spatial information, which makes full use of spectral and spatial information to achieve classification. The method proposed in this study has been tested on Pavia University (PU) and Salinas Valley (SV) datasets, respectively. Compared with that of the other four methods, the classification performance of the method proposed in this study is significantly improved.