Hyperspectral Band Selection based on Generalized 3DTV and Tensor CUR Decomposition
This work addresses computational efficiency for remote sensing applications, but it is incremental as it builds on existing band selection methods with specific improvements.
The authors tackled the problem of high dimensionality and computational challenges in hyperspectral imaging by proposing a novel band selection model that decomposes data into low-rank and sparse components, achieving effectiveness compared to state-of-the-art techniques on two benchmark datasets.
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 the $\ell_1^p$-norm to derivatives to preserve spatial-spectral smoothness. By employing the alternating direction method of multipliers (ADMM), we derive an efficient algorithm, where the tensor low-rankness is implied by the tensor CUR decomposition. We demonstrate the effectiveness of the proposed approach through comparisons with various other state-of-the-art band selection techniques using two benchmark real-world datasets. In addition, we provide practical guidelines for parameter selection in both noise-free and noisy scenarios.