Object-oriented fusion of RADARSAT-2 polarimetric synthetic aperture radar and HJ-1A multispectral data for land-cover classification

Yan Xiao; Qigang Jiang; Bin Wang; Yuanhua Li; Shu Liu; Can Cui

doi:10.1117/1.JRS.10.026021

18 May 2016 Object-oriented fusion of RADARSAT-2 polarimetric synthetic aperture radar and HJ-1A multispectral data for land-cover classification

Yan Xiao, Qigang Jiang, Bin Wang, Yuanhua Li, Shu Liu, Can Cui

Author Affiliations +

Journal of Applied Remote Sensing, Vol. 10, Issue 2, 026021 (May 2016). https://doi.org/10.1117/1.JRS.10.026021

Abstract

The contribution of the integration of optical and polarimetric synthetic aperture radar (PolSAR) data to accurate land-cover classification was investigated. For this purpose, an object-oriented classification methodology that consisted of polarimetric decomposition, hybrid feature selection, and a support vector machine (SVM) was proposed. A RADARSAT-2 Fine Quad-Pol image and an HJ-1A CCD2 multispectral image were used as data sources. First, polarimetric decomposition was implemented for the RADARSAT-2 image. Sixty-one polarimetric parameters were extracted using different polarimetric decomposition methods and then merged with the main diagonal elements (T₁₁, T₂₂, T₃₃) of the coherency matrix to form a multichannel image with 64 layers. Second, the HJ-1A and the multichannel images were divided into numerous image objects by implementing multiresolution segmentation. Third, 1104 features were extracted from the HJ-1A and the multichannel images for each image object. Fourth, the hybrid feature selection method that combined the ReliefF filter approach and the genetic algorithm (GA) wrapper approach (ReliefF–GA) was used. Finally, land-cover classification was performed by an SVM classifier on the basis of the selected features. Five other classification methodologies were conducted for comparison to verify the contribution of optical and PolSAR data integration and to test the superiority of the proposed object-oriented classification methodology. Comparison results show that HJ-1A data, RADARSAT-2 data, polarimetric decomposition, ReliefF–GA, and SVM have a significant contribution by improving land-cover classification accuracy.

Citation Download Citation

Yan Xiao, Qigang Jiang, Bin Wang, Yuanhua Li, Shu Liu, and Can Cui "Object-oriented fusion of RADARSAT-2 polarimetric synthetic aperture radar and HJ-1A multispectral data for land-cover classification," Journal of Applied Remote Sensing 10(2), 026021 (18 May 2016). https://doi.org/10.1117/1.JRS.10.026021

Published: 18 May 2016

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available