Abstract: A novel method for hyperspectral anomaly detection based on low-rank representation with manifold regularization is proposed in this paper.
A novel low-rank representation with dual graph regularization and an adaptive dictionary (DGRAD-LRR) is proposed for hyperspectral anomaly detection.
This paper proposed a novel hyperspectral anomaly detection method which consists of two interconnected components: a new anomaly detection function model
Low-rank representation (LRR) has been widely employed to detect anomalies from hyperspectral imagery (HSI) effectively while a great number of methods derived ...
Title: MANIFOLD REGULARIZED LOW-RANK REPRESENTATION FOR HYPERSPECTRAL ANOMALY DETECTION ; Authors: Tongkai Cheng; Fudan University ; Bin Wang; Fudan University ...
Feb 23, 2024 · Current hyperspectral anomaly detection (HAD) benchmark datasets suffer from low resolution, simple background, and small size of the ...
[PDF] Hyperspectral Anomaly Detection based on Low-rank ... - CEUR-WS
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paper proposed a double low-rank regularization (DLRR) model for hyperspectral anomaly detection. ... Then, the low-rank representation model aims to ...
In this article, we incorporate the graph regularization and total variation (TV) regularization into the LRR formulation and propose a novel anomaly detection ...
In a hyperspectral image, there is a close correlation between spectra and a certain degree of correlation in the pixel space.
Oct 28, 2021 · In this paper, the cluster-based graph regularized LRR with dictionary learning (CLRRDL) is proposed for HSI classification.