Hyperspectral and SAR Imagery Data Fusion with Positive Boolean Function

Yang Lang Chang, Chia Tang Chen, Chin Chuan Han, Kuo Chin Fan, K. S. Chen, Jeng Horng Chang

研究成果: 雜誌貢獻會議論文同行評審

8 引文 斯高帕斯(Scopus)


High-dimensional spectral imageries obtained from multispectral, hyperspectral or even ultraspectral bands generally provide complementary characteristics and analyzable information. Synthesis of these data sets into a composite image containing such complementary attributes in accurate registration and congruence would provide truly connected information about land covers for the remote sensing community. In this paper, a novel feature selection algorithm applied to the greedy modular eigenspaces (GME) is proposed to explore a multi-class classification technique using data fused from data gathered by the MODIS/ASTER airborne simulator (MASTER) and the Airborne Synthetic Aperture Radar (AIRSAR) during the Pacrim II campaign. The proposed approach, based on a synergistic use of these fused data, represents an effective and flexible utility for land cover classifications in earth remote sensing. An optimal positive Boolean function (PBF) based multi-classifier is built by using the labeled samples of these data as the classifier parameters in a supervised training stage. It utilizes the positive and negative sample learning ability of minimum classification error criteria to improve the classification accuracy. It is proved that the proposed method improves the precision of image classification significantly.

頁(從 - 到)765-776
期刊Proceedings of SPIE - The International Society for Optical Engineering
出版狀態已出版 - 2003
事件Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery IX - Orlando, FL, United States
持續時間: 21 4月 200324 4月 2003


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