Joint Sparse Representation and Multitask Learning for Hyperspectral Target Detection
- 5 December 2016
- journal article
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Transactions on Geoscience and Remote Sensing
- Vol. 55 (2), 894-906
- https://doi.org/10.1109/tgrs.2016.2616649
Abstract
With the high spectral resolution, hyperspectral images (HSIs) provide great potential for target detection, which is playing an increasingly important role in HSI processing. Many target detection methods uniformly utilize all the spectral information or employ reduced spectral information to distinguish the targets and background. Simultaneously reducing spectral redundancy and preserving the discriminative information is a challenging problem in hyperspectral target detection. The multitask learning (MTL) technique may have the potential to solve the above problem, since it can explore the redundancy knowledge to construct multiple sub-HSIs and integrate them without any information loss. This paper proposes the joint sparse representation and MTL (JSR-MTL) method for hyperspectral target detection. This approach: 1) explores the HSIs similarity by a band cross-grouping strategy to construct multiple sub-HSIs; 2) takes full advantage of the MTL technique to integrate the sparse representation models for the multiple related sub-HSIs; and 3) applies the total reconstruction error difference accumulated over all the tasks to detect the targets. Extensive experiments were carried out on three HSIs, and it was founded that JSR-MTL generally shows a better detection performance than the other target detection methods.Keywords
Funding Information
- National Natural Science Foundation of China (61471274, 41431175)
- Natural Science Foundation of Hubei Province (2014CFB193)
- Fundamental Research Funds for the Central Universities (2042014kf0239)
This publication has 41 references indexed in Scilit:
- Kernel-based regularized-angle spectral matching for target detection in hyperspectral imageryPattern Recognition Letters, 2011
- Kernel Spectral Matched Filter for Hyperspectral ImageryInternational Journal of Computer Vision, 2006
- On the Impact of PCA Dimension Reduction for Hyperspectral Detection of Difficult TargetsIEEE Geoscience and Remote Sensing Letters, 2005
- Preprocessing eo-1 hyperion hyperspectral data to support the application of agricultural indexesIEEE Transactions on Geoscience and Remote Sensing, 2003
- Hyperspectral image data analysisIEEE Signal Processing Magazine, 2002
- HyMap hyperspectral remote sensing to detect hydrocarbonsInternational Journal of Remote Sensing, 2001
- Study of crop growth parameters using Airborne Imaging Spectrometer dataInternational Journal of Remote Sensing, 2001
- Adaptive subspace detectorsIEEE Transactions on Signal Processing, 2001
- Matched subspace detectorsIEEE Transactions on Signal Processing, 1994
- Imaging Spectrometry for Earth Remote SensingScience, 1985