Progressive Compressively Sensed Band Processing for Hyperspectral Classification
Author:
Affiliation:
1. Department of Computer Science and Electrical Engineering, Remote Sensing Signal and Image Processing Laboratory, University of Maryland, Baltimore, MD, USA
Funder
Fundamental Research Funds for Central Universities
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Subject
General Earth and Planetary Sciences,Electrical and Electronic Engineering
Link
http://xplorestaging.ieee.org/ielx7/36/9363036/09123599.pdf?arnumber=9123599
Cited by 5 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献
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2. Ant Colony Optimized AmoebaNet-A Algorithm for Hyperspectral Image Classification;2022 6th International Conference on Electronics, Communication and Aerospace Technology;2022-12-01
3. A Discriminative Spectral-Spatial-Semantic Feature Network Based on Shuffle and Frequency Attention Mechanisms for Hyperspectral Image Classification;Remote Sensing;2022-06-03
4. Spectral Distribution-Aware Estimation Network for Hyperspectral Anomaly Detection;IEEE Transactions on Geoscience and Remote Sensing;2022
5. Estimating Optimal Number of Compressively Sensed Bands for Hyperspectral Classification via Feature Selection;IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing;2021
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