Hyperspectral Band Selection based on Generalized 3DTV and Tensor CUR Decomposition

arXiv:2405.00951v1 Announce Type: new
Abstract: Hyperspectral Imaging (HSI) serves as an important technique in remote sensing. However, high dimensionality and data volume typically pose significant computational challenges. Band selection is essential for reducing spectral redundancy in hyperspectral imagery while retaining intrinsic critical information. In this work, we propose a novel hyperspectral band selection model by decomposing the data into a low-rank and smooth component and a sparse one. In particular, we develop a generalized 3D total variation (G3DTV) by applying …

abstract arxiv challenges computational cs.cv cs.na data dimensionality generalized however imaging information intrinsic math.na math.oc redundancy sensing tensor type while work