Lightweight Fully-Connected Tensorial Mapping Network for Hyperspectral Image Classification

In recent years, convolutional neural networks have demonstrated outstanding performance in HSIC (Hyperspectral Image Classification). However, the improvement of model performance involves adopting deeper and broader network architectures, leading to an increased number of parameters and operations, thus hindering deployment in airborne or on-board devices. To this end, this paper introduces a HSIC method based on the LiteFCTMN (Lightweight Fully-Connected Tensorial Mapping Network). We design two convolutional units based on the mapping way of FCTN (Fully-Connected Tensor Network) decomposition and the structural characteristics of HSIs. By mapping the original convolution kernel to multiple small-sized convolution kernels with fully-connected structures, the complexity of the novel units is reduced while their expressiveness is improved. In addition, the RDT (Residual Double-Branch Tensorial) module is constructed using the designed units. In this module, two branches share the same weights, and a channel split operation is employed to reduce the number of feature channels, thereby reducing complexity. The proposed model strategically leverages both local spatial-spectral information from RDT and global spectral information from the new units, resulting in enhanced classification performance and reduced hardware consumption. Experimental results on three widely used HSI datasets demonstrate that the proposed model achieves superior classification performance and lower complexity compared to the state-of-the-art works. © 2024 Chinese Institute of Electronics. All rights reserved.