most citedUnsupervised Feature Learning by Autoencoder and Prototypical Contrastive Learning for Hyperspectral Classification

63 citations · 63 across the 3 of their papers we have counts for

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cs.CV2026

Utilizing the Score of Data Distribution for Hyperspectral Anomaly Detection

Jiahui Sheng, Yidan Shi, Shu Xiang +2

Hyperspectral images (HSIs) are a type of image that contains abundant spectral information. As a type of real-world data, the high-dimensional spectra in hyperspectral images are…

cs.CV2026

Turbo-GoDec: Exploiting the Cluster Sparsity Prior for Hyperspectral Anomaly Detection

Jiahui Sheng, Xiaorun Li, Shuhan Chen

As a key task in hyperspectral image processing, hyperspectral anomaly detection has garnered significant attention and undergone extensive research. Existing methods primarily rel…

cs.CV2020★ 63 cited

Unsupervised Feature Learning by Autoencoder and Prototypical Contrastive Learning for Hyperspectral Classification

Zeyu Cao, Xiaorun Li, Liaoying Zhao

Unsupervised learning methods for feature extraction are becoming more and more popular. We combine the popular contrastive learning method (prototypical contrastive learning) and…