PU-KBS: A Robust Positive and Unlabeled Learning Framework With Key Band Selection for One-Class Hyperspectral Image Classification

作者：Ziying Liu, Hengwei Zhao, Xinyu Wang, Shaoyu Wang, Jingtao Li, Yanfei Zhong · 发表于：IEEE Transactions on Geoscience and Remote Sensing · 年份：2024 · DOI：10.1109/tgrs.2024.3397989 · 被引用次数：5 · 研究领域：Remote-Sensing Image Classification、Spectroscopy and Chemometric Analyses、Face and Expression Recognition

Positive and unlabeled (PU) learning is aimed at building a binary classifier to distinguish the target from the background using only the known positive samples, which is an advanced solution for the hyperspectral target detection (HTD) task. However, when PU learning (PUL) meets complex hyperspectral scenarios, there are two main challenges: 1) How to estimate the class prior accurately? The class prior, i.e., the target proportion, is an important prior for PUL to learn the discriminant boundary, but it is difficult to estimate in hyperspectral imagery, due to the interclass spectral similarity and 2) How to remove redundancy and improve the discriminative features of the target? The diagnostic spectral feature extraction is important for the weakly supervised PUL models as it can help with separating the target from the background. In this article, to tackle these challenges, a robust PUL framework with key band selection (PU-KBS) is proposed, which is modeled as an end-to-end and class prior free PUL framework, where the accurate class prior and the most discriminative key band subset are jointly initialized and iteratively updated until reaching the optimal result by evolutionary search. Meanwhile, a deep PUL detector is introduced for guiding the subsequent search direction and discriminative deep feature extraction. The proposed PU-KBS framework was verified using different hyperspectral datasets, where accurate class prior estimation, diagnostic spectral characterist...