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Synthesis of remote sensing label fields using a tree-structured hierarchical model

TitleSynthesis of remote sensing label fields using a tree-structured hierarchical model
Publication TypeJournal Article
Year of Publication2011
AuthorsLiu, Y., A. Wong, and P. Fieguth
JournalIEEE Transactions on Geoscience and Remote Sensing
Volume49
Pagination2060-2070
ISSN0196-2892
Keywordscomplex nonstationary scale structures, geographic information systems, geophysical image processing, image classification, image segmentation, Markov processes, operational RADARSAT SAR sea ice data, pseudoground truth data, region-oriented binary tree structure, remote sensing by radar, remote sensing label fields, resolution-oriented hierarchical method, SAR data analysis tools, sea ice, SIR-C/X-SAR land mass data, synthetic aperture radar, tree data structures, tree-structured hierarchical model
Abstract

The systematic evaluation of synthetic aperture radar (SAR) data analysis tools, such as segmentation and classification algorithms for geographic information systems, is difficult given the unavailability of ground-truth data in most cases. Therefore, testing is typically limited to small sets of pseudoground-truth data collected manually by trained experts, or primitive synthetic sets composed of simple geometries. To address this issue, we investigate the potential of employing an alternative approach, which involves the synthesis of SAR data and corresponding label fields from real SAR data for use as a reliable evaluation testbed. Given the scale-dependent nonstationary nature of SAR data, a new modeling approach that combines a resolution-oriented hierarchical method with a region-oriented binary tree structure is introduced to synthesize such complex data in a realistic manner. Experimental results using operational RADARSAT SAR sea-ice data and SIR-C/X-SAR land-mass data show that the proposed hierarchical approach can better model complex nonstationary scale structures than local MRF approaches and existing nonparametric methods, thus making it well suited for synthesizing SAR data and the corresponding label fields for potential use in the systematic evaluation of SAR data analysis tools.

DOI10.1109/TGRS.2010.2102765