MyJournals Home  

RSS FeedsRemote Sensing, Vol. 11, Pages 150: Joint Sparse and Low-Rank Multi-Task Learning with Extended Multi-Attribute Profile for Hyperspectral Target Detection (Remote Sensing)

 
 

20 january 2019 05:00:02

 
Remote Sensing, Vol. 11, Pages 150: Joint Sparse and Low-Rank Multi-Task Learning with Extended Multi-Attribute Profile for Hyperspectral Target Detection (Remote Sensing)
 


Target detection is an active area in hyperspectral imagery (HSI) processing. Many algorithms have been proposed for the past decades. However, the conventional detectors mainly benefit from the spectral information without fully exploiting the spatial structures of HSI. Besides, they primarily use all bands information and ignore the inter-band redundancy. Moreover, they do not make full use of the difference between the background and target samples. To alleviate these problems, we proposed a novel joint sparse and low-rank multi-task learning (MTL) with extended multi-attribute profile (EMAP) algorithm (MTJSLR-EMAP). Briefly, the spatial features of HSI were first extracted by morphological attribute filters. Then the MTL was exploited to reduce band redundancy and retain the discriminative information simultaneously. Considering the distribution difference between the background and target samples, the target and background pixels were separately modeled with different regularization terms. In each task, a background pixel can be low-rank represented by the background samples while a target pixel can be sparsely represented by the target samples. Finally, the proposed algorithm was compared with six detectors including constrained energy minimization (CEM), adaptive coherence estimator (ACE), hierarchical CEM (hCEM), sparsity-based detector (STD), joint sparse representation and MTL detector (JSR-MTL), independent encoding JSR-MTL (IEJSR-MTL) on three datasets. Corresponding to each competitor, it has the average detection performance improvement of about 19.94%, 22.53%, 16.92%, 14.87%, 14.73%, 4.21% respectively. Extensive experimental results demonstrated that MTJSLR-EMAP outperforms several state-of-the-art algorithms.


 
157 viewsCategory: Geology, Physics
 
Remote Sensing, Vol. 11, Pages 151: Suitability of Satellite-Based Precipitation Products for Water Balance Simulations Using Multiple Observations in a Humid Catchment (Remote Sensing)
Remote Sensing, Vol. 11, Pages 149: Rapid Relocation Method for Mobile Robot Based on Improved ORB-SLAM2 Algorithm (Remote Sensing)
 
 
blog comments powered by Disqus


MyJournals.org
The latest issues of all your favorite science journals on one page

Username:
Password:

Register | Retrieve

Search:

Physics


Copyright © 2008 - 2024 Indigonet Services B.V.. Contact: Tim Hulsen. Read here our privacy notice.
Other websites of Indigonet Services B.V.: Nieuws Vacatures News Tweets Nachrichten