Hyperspectral Target Detection: a Preprocessing Method Based on Tensor Principal Component Analysis

摘要

Traditional target detection (TD) methods for hyperspectral imagery (HSI) suffer from background interference. In this paper, we propose a novel preprocessing method based on tensor principal component analysis (TPCA) to separate the background and target apart. In our approach, HSI is decomposed into the sum of the principal component (PC) part and the residual part, and TD is performed on the latter. TPCA takes spatial and spectral information into account jointly, and treats spatial and spectral information differently, which is in line with HSI physical meanings. Experiments on both synthetic and real data indicate that our TPCA-based method outperforms other feature extraction preprocessing methods in terms of TD results.