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Koyo Sato

4 accepted papers

2024

Enhancing Hyperspectral Anomaly Detection by Difference-of-Convex Sparse Anomaly Modeling

ICASSP 2024accepted

We propose a hyperspectral (HS) anomaly detection method using a novel characterization of anomalies. Among HS anomaly detection approaches, decomposition-based methods, which simultaneously estimate a background part and an anomaly part from an HS image, have attracted much attention. In these meth…

Cited by 0SourceScholar
2023

Robust Hyperspectral Anomaly Detection with Simultaneous Mixed Noise Removal via Constrained Convex Optimization

ICASSP 2023accepted

Hyperspectral (HS) anomaly detection is the task of identifying pixels with spectral signatures that differ significantly from surrounding pixels. Most existing anomaly detection methods do not take into account the effect of noise in HS images, or if they do, it is only Gaussian noise. In practice,…

Cited by 0SourceScholar