Self-Supervised Contrastive Learning for Hyperspectral Anomaly Detection With Block-Pseudolabel Masked Data Augmentation

Hyperspectral anomaly detection (HAD) aims to identify targets deviating from normal patterns of background. However, the lack of labeled samples poses significant challenges to the task. Self-supervised deep learning methods have shown promising results in this scenario, but they often fall into pa...

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Үндсэн зохиолчид:	Meiping Song, Zhiyuan Jing, Xudong Sun, Yisha Liu, Haimo Bao, Chunyan Yu
Формат:	Artigo
Хэл сонгох:	Inglês
Хэвлэсэн:	IEEE 2026-01-01
Цуврал:	IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Нөхцлүүд:	Contrastive learning data augmentation hyperspectral anomaly detection (HAD) patches pseudolabel self-supervised
Онлайн хандалт:	https://ieeexplore.ieee.org/document/11367285/
Шошгууд:	Шошго нэмэх Шошго байхгүй, Энэхүү баримтыг шошголох эхний хүн болох!