Remote Sensing Applications and Decision Support

Improved collaborative representation model with multitask learning using spatial support for target detection in hyperspectral imagery

[+] Author Affiliations
Chunhui Zhao, Bing Cui

Harbin Engineering University, College of Information and Communication Engineering, Room 504, Building 21, No. 145 Nantong Street, Nangang District, Harbin City, Heilongjiang Province 150001, China

Wei Li

Harbin Engineering University, College of Information and Communication Engineering, Room 504, Building 21, No. 145 Nantong Street, Nangang District, Harbin City, Heilongjiang Province 150001, China

University of Alberta, Earth Observation Systems Laboratory, Earth and Atmospheric Sciences Department, Edmonton, Alberta T6G 2E3, Canada

G. Arturo Sanchez-Azofeifa

University of Alberta, Earth Observation Systems Laboratory, Earth and Atmospheric Sciences Department, Edmonton, Alberta T6G 2E3, Canada

Bin Qi

Harbin Engineering University, College of Underwater Acoustic Engineering, No. 145 Nantong Street, Nangang District, Harbin City, Heilongjiang Province 150001, China

J. Appl. Remote Sens. 10(1), 016009 (Feb 10, 2016). doi:10.1117/1.JRS.10.016009
History: Received August 5, 2015; Accepted January 13, 2016
Text Size: A A A

Abstract.  We propose an improved collaborative representation model with multitask learning using spatial support (ICRTD-MTL) for target detection (TD) in hyperspectral imagery. The proposed model consists of the following aspects. First, multiple features are extracted from the hyperspectral image to represent pixels from different perspectives. Next, we apply these features into the unified CRTD-MTL to acquire a collaborative vector for each feature. To adjust the contribution of each feature, a weight coefficient is included in the optimization problem. Once the collaborative vector is obtained, the class of the test sample can be determined by the characteristics of the collaborative vector on reconstruction. Finally, the spatial correlation and spectral similarity of adjacent neighboring pixels are incorporated into each feature to improve the detection accuracy. The experimental results suggest that the proposed algorithm obtains an excellent performance.

© 2016 Society of Photo-Optical Instrumentation Engineers

Citation

Chunhui Zhao ; Wei Li ; G. Arturo Sanchez-Azofeifa ; Bin Qi and Bing Cui
"Improved collaborative representation model with multitask learning using spatial support for target detection in hyperspectral imagery", J. Appl. Remote Sens. 10(1), 016009 (Feb 10, 2016). ; http://dx.doi.org/10.1117/1.JRS.10.016009


Access This Article
Sign in or Create a personal account to Buy this article ($20 for members, $25 for non-members).

Some tools below are only available to our subscribers or users with an online account.

Related Content

Customize your page view by dragging & repositioning the boxes below.

Related Book Chapters

Topic Collections

Advertisement
  • Don't have an account?
  • Subscribe to the SPIE Digital Library
  • Create a FREE account to sign up for Digital Library content alerts and gain access to institutional subscriptions remotely.
Access This Article
Sign in or Create a personal account to Buy this article ($20 for members, $25 for non-members).
Access This Proceeding
Sign in or Create a personal account to Buy this article ($15 for members, $18 for non-members).
Access This Chapter

Access to SPIE eBooks is limited to subscribing institutions and is not available as part of a personal subscription. Print or electronic versions of individual SPIE books may be purchased via SPIE.org.