Sparse and Low-Rank Matrix Decomposition for Automatic Target Detection in Hyperspectral Imagery
- 29 March 2019
- journal article
- research article
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Transactions on Geoscience and Remote Sensing
- Vol. 57 (8), 5239-5251
- https://doi.org/10.1109/tgrs.2019.2897635
Abstract
Given a target prior information, our goal is to propose a method for automatically separating targets of interests from the background in hyperspectral imagery. More precisely, we regard the given hyperspectral image (HSI) as being made up of the sum of low-rank background HSI and a sparse target HSI that contains the targets based on a prelearned target dictionary constructed from some online spectral libraries. Based on the proposed method, two strategies are briefly outlined and evaluated to realize the target detection on both synthetic and real experiments.Keywords
This publication has 52 references indexed in Scilit:
- Robust principal component analysis?Journal of the ACM, 2011
- Honey, I Shrunk the Sample Covariance MatrixThe Journal of Portfolio Management, 2004
- Effects of spectrometer band pass, sampling, and signal‐to‐noise ratio on spectral identification using the Tetracorder algorithmJournal of Geophysical Research, 2003
- Preprocessing eo-1 hyperion hyperspectral data to support the application of agricultural indexesIEEE Transactions on Geoscience and Remote Sensing, 2003
- Detection algorithms for hyperspectral imaging applicationsIEEE Signal Processing Magazine, 2002
- Signal processing for hyperspectral image exploitationIEEE Signal Processing Magazine, 2002
- Anomaly detection from hyperspectral imageryIEEE Signal Processing Magazine, 2002
- Study of crop growth parameters using Airborne Imaging Spectrometer dataInternational Journal of Remote Sensing, 2001
- HyMap hyperspectral remote sensing to detect hydrocarbonsInternational Journal of Remote Sensing, 2001
- The CFAR adaptive subspace detector is a scale-invariant GLRTIEEE Transactions on Signal Processing, 1999