Lightweight Fully-Connected Tensorial Mapping Network for Hyper-spectral Image Classification
In recent years,convolutional neural networks have demonstrated outstanding performance in HSIC(Hy-perspectral 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-Con-nected Tensorial Mapping Network).We design two convolutional units based on the mapping way of FCTN(Fully-Con-nected Tensor Network)decomposition and the structural characteristics of HSIs.By mapping the original convolution ker-nel 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 us-ing the designed units.In this module,two branches share the same weights,and a channel split operation is employed to re-duce 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 classifica-tion 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.
万方数据
维普
