基于 CUR 分解(CUR-HU)的高效盲高光谱解混框架

Remote. Sens. Pub Date : 2024-02-22 DOI:10.3390/rs16050766
Muhammad A. A. Abdelgawad, Ray C. C. Cheung, Hong Yan
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引用次数: 0

摘要

高光谱成像可捕捉到用于遥感的详细光谱数据。然而,由于高光谱传感器的空间分辨率有限,高光谱图像(HSI)的每个像素可能包含来自多种材料的信息。虽然高光谱非混合(HU)过程包括估算内含物、识别纯光谱成分和估算像素丰度,但现有的算法大多只关注其中的一项或两项任务。基于非负矩阵因式分解(NMF)算法的盲源分离(BSS)可同时识别 HSI 每个像素的内含成分及其丰度。虽然这些算法性能良好,但因式分解结果不稳定,计算成本高,而且难以从原始 HSI 中解读。CUR 矩阵分解从数据集中选择特定的列和行,将其表示为三个小的子矩阵的乘积,从而得到可解释的低秩因式分解。在本文中,我们提出了一种基于 CUR 因式分解的全新盲 HU 框架,称为 CUR-HU,它通过利用给定 HSI 的低秩结构来执行整个 HU 流程。CUR-HU 融合了多种技术来执行 HU 过程,其性能与最先进的方法相当,但计算效率更高。我们采用确定性采样方法来选择 HSI 中信息量最大的像素和频谱成分。我们使用增量 QR 分解法来降低计算复杂度和估算内含物的数量。为了评估 CUR-HU 的性能,我们在合成和真实的 HSI 上进行了各种实验。CUR-HU 在估计内含物数量和丰度图方面的表现与最先进的方法相当,但在估计内含物和计算效率方面优于其他方法。对于不同的实际恒星指数,它的速度比不同的方法快 9.4 到 249.5 倍。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Efficient Blind Hyperspectral Unmixing Framework Based on CUR Decomposition (CUR-HU)
Hyperspectral imaging captures detailed spectral data for remote sensing. However, due to the limited spatial resolution of hyperspectral sensors, each pixel of a hyperspectral image (HSI) may contain information from multiple materials. Although the hyperspectral unmixing (HU) process involves estimating endmembers, identifying pure spectral components, and estimating pixel abundances, existing algorithms mostly focus on just one or two tasks. Blind source separation (BSS) based on nonnegative matrix factorization (NMF) algorithms identify endmembers and their abundances at each pixel of HSI simultaneously. Although they perform well, the factorization results are unstable, require high computational costs, and are difficult to interpret from the original HSI. CUR matrix decomposition selects specific columns and rows from a dataset to represent it as a product of three small submatrices, resulting in interpretable low-rank factorization. In this paper, we propose a new blind HU framework based on CUR factorization called CUR-HU that performs the entire HU process by exploiting the low-rank structure of given HSIs. CUR-HU incorporates several techniques to perform the HU process with a performance comparable to state-of-the-art methods but with higher computational efficiency. We adopt a deterministic sampling method to select the most informative pixels and spectrum components in HSIs. We use an incremental QR decomposition method to reduce computation complexity and estimate the number of endmembers. Various experiments on synthetic and real HSIs are conducted to evaluate the performance of CUR-HU. CUR-HU performs comparably to state-of-the-art methods for estimating the number of endmembers and abundance maps, but it outperforms other methods for estimating the endmembers and the computational efficiency. It has a 9.4 to 249.5 times speedup over different methods for different real HSIs.
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