Efficient MLP-aided CNN network with local-global feature fusion for hyperspectral image classification

作者：Fei Du, Meng Li, Ju Li, Xiao‐Xin An, Xinan Xu, Cuiping Gao, Guodong Han · 年份：2024 · DOI：10.1117/12.3034144 · 被引用次数：2 · 研究领域：Remote-Sensing Image Classification、Advanced Chemical Sensor Technologies、Remote Sensing and Land Use

The utilization of long-range spatial features has significantly enhanced hyperspectral image (HSI) classification performance. However, capturing global spatial relationships among pixels comes with a high computational expense. Given that super-pixels are created adaptively from the content within hyperspectral images, leveraging them for global spatial information extraction has proven to be an efficient strategy. Nevertheless, some methodologies at the super-pixel level, like Graph Convolutional Networks (GCN), are burdened by numerous parameters and substantial computational complexity. In order to address these challenges, this paper introduces an Efficient MLP-Aided CNN Network with Localglobal Feature Fusion for Hyperspectral Image Classification (EMACN). EMACN adopts a dual-branch structure, utilizing a super-pixel-based Multilayer Perceptron (MLP) for global feature extraction with a pixel-based Convolutional Neural Network (CNN) for local feature extraction, thereby achieving an effective balance between computational efficiency and classification performance. Compared to the quadratic complexity seen in other super-pixel level deep learning structures such as GCNs, the computational complexity of our super-pixel-based MLP approach is linear with respect to both the number and dimensionality of super-pixels. Furthermore, we enhance our model by integrating a spectral transformation subnet that utilizes group convolution instead of the traditional 1x1 convolution fo...