Title: Image Enhancement and Feature Extraction based on Low-Resolution Satellite Data
Authors: SYRRIS VASILEIOSFERRI STEFANOEHRLICH DanielePESARESI Martino
Citation: IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING vol. 8 no. 5 p. 1986-1995
Publisher: IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Publication Year: 2015
JRC N°: JRC92756
ISSN: 1939-1404
URI: http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=7084576
http://publications.jrc.ec.europa.eu/repository/handle/JRC92756
DOI: 10.1109/JSTARS.2015.2417864
Type: Articles in periodicals and books
Abstract: The purpose of this study is to investigate the sensitivity of contrast-based textural measurements and morphological characteristics that derive from high-resolution satellite imagery (three-band SPOT-5) when diverse image enhancements techniques are piloted. The general framework of the application is the built-up/nonbuilt-up detection. In the existence of a low-resolution reference layer, we apply supervised learning that indirectly reduces the uncertainty and improves the quality of the reference layer. Based on the new class label assignments, the image histogram is adjusted suitably for the computation of contrast-based textural/morphological features. A case study is presented where we test a mixture of image enhancement operations like linear and decorrelation stretching and assess the performance through ROC analysis against available building footprints. Experimental results demonstrate that spectral band combination is the key factor that conditions the contrast of grayscale images. Contrast adjustment (before or after the band combination and merging) supports considerably the extraction of informative features from a low-contrast image; in case of a well-contrasted image, the improvement is marginal.
JRC Directorate:Space, Security and Migration

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