Tensor Adaptive Reconstruction Cascaded With Global and Local Feature Fusion for Hyperspectral Target Detection

Hyperspectral target detection technology is becoming more and more important in remote sensing. Most of the traditional methods of target sensing using spectral information treat hyperspectral image as 2-D matrix regardless of the structure information of hyperspectral image, resulting in insufficient separation of target and background and limited detection accuracy. In order to better utilize the hyperspectral intrinsic structural information, this article proposes a hyperspectral target sensing approach based on tensor adaptive reconstruction cascaded with global and local feature fusion (TRGLF). First, the Tucker decomposition and reconstruction method is utilized to alleviate the influence of noise and other elements in the complex background hyperspectral, which can maintain the intrinsic structural features of the data and improve the background and target separation. The principal components of the factor matrix are determined by a logarithmic singular value summation strategy, and then the energy difference between the spectra to be measured and the previous spectra is calculated to fine-tune the principal components and obtain more appropriate principal component values. Second, on the basis of reconstructed data, a global and local spatial spectral fusion method is adopted to obtain the target of interest. This includes using globally constrained energy minimization to obtain the target of interest and using local differential sensing to further obtain the target boundary. The detection performed on four realistically acquired hyperspectral water surface datasets demonstrate the excellent detection performance of the proposed target detection method.