In remote sensing, it is often challenging to acquire or collect a large dataset that is accurately labeled. This difficulty is usually due to several issues, including but not limited to the study site's spatial area and accessibility, errors in the global positioning system (GPS), and mixed pixels caused by an image's spatial resolution. We propose an approach, with two variations, that estimates multiple target signatures from training samples with imprecise labels: Multi-Target Multiple Instance Adaptive Cosine Estimator (Multi-Target MI-ACE) and Multi-Target Multiple Instance Spectral Match Filter (Multi-Target MI-SMF). The proposed methods address the problems above by directly considering the multiple-instance, imprecisely labeled dataset. They learn a dictionary of target signatures that optimizes detection against a background using the Adaptive Cosine Estimator (ACE) and Spectral Match Filter (SMF). Experiments were conducted to test the proposed algorithms using a simulated hyperspectral dataset, the MUUFL Gulfport hyperspectral dataset collected over the University of Southern Mississippi-Gulfpark Campus, and the AVIRIS hyperspectral dataset collected over Santa Barbara County, California. Both simulated and real hyperspectral target detection experiments show the proposed algorithms are effective at learning target signatures and performing target detection.
Weighted Hierarchical Sparse Representation for Hyperspectral Target Detection
Northwestern Polytechnical University（西北工业大学）；School of Computer Science（计算机科学学院）；Center for OPTical IMagery Analysis and Learning (OPTIMAL)（光学影像分析与学习中心（OPTIMAL））
Automatic Target Detection for Sparse Hyperspectral Images
CentraleSupélec（法国中央理工-高等电力学院）；Université Paris-Saclay（巴黎萨克雷大学）；American University of Beirut（贝鲁特美国大学）；ONERA（法国航空航天研究院）；National University of Singapore（新加坡国立大学）
Cross-domain Hyperspectral Image Classification based on Bi-directional Domain Adaptation
Jockey Club STEM Laboratory of Quantitative Remote Sensing, Department of Geography, the University of Hong Kong（香港大学地理系定量遥感实验室）；School of Information and Electronics, Beijing Institute of Technology（北京理工大学信息与电子学院）；Institute of Forest Resource Information Techniques, Chinese Academy of Forestry（中国林业科学研究院森林资源信息技术研究所）
Low-Rank Representations Meets Deep Unfolding: A Generalized and Interpretable Network for Hyperspectral Anomaly Detection
Aerospace Information Research Institute, Chinese Academy of Sciences（中国科学院空天信息创新研究院）；School of Mathematics and Statistics, Southeast University（东南大学数学学院与统计学院）；College of Resources and Environment, University of Chinese Academy of Sciences（中国科学院大学资源与环境学院）；Univ. Grenoble Alpes, Inria, CNRS, Grenoble INP, LJK（格勒诺布尔阿尔卑斯大学、法国国家信息与自动化研究所、法国国家科学研究中心、格勒诺布尔理工学院、约瑟夫·傅里叶大学联合实验室）
Ensemble and Random Collaborative Representation-Based Anomaly Detector for Hyperspectral Imagery
School of Artificial Intelligence, Optics and Electronics (iOPEN), Northwestern Polytechnical University（西北工业大学光电与智能研究院）；School of Computer Science and School of Artificial Intelligence, Optics and Electronics (iOPEN), Northwestern Polytechnical University（西北工业大学计算机学院与光电与智能研究院）
A UAV-Based VNIR Hyperspectral Benchmark Dataset for Landmine and UXO Detection
Rochester Institute of Technology, Chester F. Carlson Center for Imaging Science（罗切斯特理工学院，切斯特·F·卡尔森成像科学中心）；Lamont Doherty Earth Observatory, Columbia University（拉蒙特-多赫蒂地球观测站，哥伦比亚大学）；Demining Research Community（排雷研究社区）
