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Land Cover Classification Using RapidEye Data to Estimate the Amount of Disaster Waste
Description
To draw reconstruction plans following great earthquakes, it is necessary to quickly estimate the amount of disaster waste, with the use of remote sensing data affecting all subsequent processing. However, the digital number (DN) of each pixel represents the average land cover conditions, i.e., the information provided by a pixel should be represented as a one-pixel mixed-class (“mixel”) instead of a one-pixel one-class. In a previous study, we proposed a method for unmixing mixels using the DNs and texture features from THEOS data. In this paper, we propose a method of land cover classification using RapidEye data, whose effectiveness was confirmed by our results.
Journal
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- Proceedings of The 3rd International Conference on Intelligent Systems and Image Processing 2015
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Proceedings of The 3rd International Conference on Intelligent Systems and Image Processing 2015 277-282, 2015-01-01
The Institute of Industrial Application Engineers