Model-Informed Multistage Unsupervised Network for Hyperspectral Image Super-Resolution

Li, Jiaxin; Zheng, Ke; Gao, Lianru; Ni, Li; Huang, Min; Chanussot, Jocelyn

doi:10.1109/tgrs.2024.3391014

Cited by 20 publications

(2 citation statements)

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“…(1) Number of dimensions in the transformation space (r): To get the optimal setting, the value of r is set to the range of [1][2][3][4][5]10,20,30,40,60]. The influence of detection rates in six hyperspectral datasets is displayed in Figure 3a.…”

Section: Parameter Analysismentioning

confidence: 99%

“…Based on the differences in spectral curves, different feature types are easy to discriminate. Therefore, hyperspectral images (HSIs) [7] have been employed in various fields [8][9][10][11][12] in recent years. Hyperspectral technology contains many image processing tasks, such as change detection [13], classification [14,15], anomaly detection [16][17][18], fusion [5,19,20], band selection [21], and so on [22][23][24][25].…”

Section: Introductionmentioning

confidence: 99%

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Hyperspectral Anomaly Detection via Low-Rank Representation with Dual Graph Regularizations and Adaptive Dictionary

Cheng,

Mu,

Lin

et al. 2024

Remote Sensing

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In a hyperspectral image, there is a close correlation between spectra and a certain degree of correlation in the pixel space. However, most existing low-rank representation (LRR) methods struggle to utilize these two characteristics simultaneously to detect anomalies. To address this challenge, a novel low-rank representation with dual graph regularization and an adaptive dictionary (DGRAD-LRR) is proposed for hyperspectral anomaly detection. To be specific, dual graph regularization, which combines spectral and spatial regularization, provides a new paradigm for LRR, and it can effectively preserve the local geometrical structure in the spectral and spatial information. To obtain a robust background dictionary, a novel adaptive dictionary strategy is utilized for the LRR model. In addition, extensive comparative experiments and an ablation study were conducted to demonstrate the superiority and practicality of the proposed DGRAD-LRR method.

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