Realistic Mixing Miniature Scene Hyperspectral Unmixing: From Benchmark Datasets to Autonomous Unmixing
Author:
Affiliation:
1. State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan, China
2. School of Remote Sensing and Information Engineering, Wuhan University, Wuhan, China
Funder
National Key Research and Development Program of China
National Natural Science Foundation of China
LIESMARS Special Research Funding
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Subject
General Earth and Planetary Sciences,Electrical and Electronic Engineering
Link
http://xplorestaging.ieee.org/ielx7/36/10006360/10016626.pdf?arnumber=10016626
Reference38 articles.
1. Saliency-Based Endmember Detection for Hyperspectral Imagery
2. Endmember Extraction From Highly Mixed Data Using Minimum Volume Constrained Nonnegative Matrix Factorization
3. Automatic spectral target recognition in hyperspectral imagery
4. Autonomous Endmember Detection via an Abundance Anomaly Guided Saliency Prior for Hyperspectral Imagery
5. CyCU-Net: Cycle-Consistency Unmixing Network by Learning Cascaded Autoencoders
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