Data fusion of hyperspectral and SAR images

Yang-Lang Chang; Chin-Chuan Han; Hsuan Ren; Chia-Tang Chen; Kun-Shan Chen; Kuo-Chin Fan

doi:10.1117/1.1768535

1 August 2004 Data fusion of hyperspectral and SAR images

Yang-Lang Chang, Chin-Chuan Han, Hsuan Ren, Chia-Tang Chen, Kun-Shan Chen, Kuo-Chin Fan

Author Affiliations +

Optical Engineering, Vol. 43, Issue 8, (August 2004). https://doi.org/10.1117/1.1768535

Abstract

A novel technique is proposed for data fusion of earth remote sensing. The method is developed for land cover classification based on fusion of remote sensing images of the same scene collected from multiple sources. It presents a framework for fusion of multisource remote sensing images, which consists of two algorithms, referred to as the greedy modular eigenspace (GME) and the feature scale uniformity transformation (FSUT). The GME method is designed to extract features by a simple and efficient GME feature module, while the FSUT is performed to fuse most correlated features from different data sources. Finally, an optimal positive Boolean function based multiclass classifier is further developed for classification. It utilizes the positive and negative sample learning ability of the minimum classification error criteria to improve classification accuracy. The performance of the proposed method is evaluated by fusing MODIS/ASTER airborne simulator (MASTER) images and the airborne synthetic aperture radar (SAR) images for land cover classification during the PacRim II campaign. Experimental results demonstrate that the proposed fusion approach is an effective method for land cover classification in earth remote sensing, and improves the precision of image classification significantly compared to conventional single source classification.

©(2004) Society of Photo-Optical Instrumentation Engineers (SPIE)

Citation Download Citation

Yang-Lang Chang, Chin-Chuan Han, Hsuan Ren, Chia-Tang Chen, Kun-Shan Chen, and Kuo-Chin Fan "Data fusion of hyperspectral and SAR images," Optical Engineering 43(8), (1 August 2004). https://doi.org/10.1117/1.1768535

Published: 1 August 2004

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