A Comparative Analysis of Machine Learning with WorldView-2 Pan-Sharpened Imagery for Tea …

Open AccessThis article is freely available re-usable Sensors 2016, 16(5), 594; doi:10.3390/s16050594 (registering DOI) Article Department of Urban Planning and Spatial Information, Feng Chia University, Taichung 40724, Taiwan * Author to whom correspondence should be addressed. Academic Editor: Simon X. Yang Received: 25 January 2016 / Revised: 8 April 2016 / Accepted: 19 April 2016 / Published: 26 April 2016 No Tea is an important but vulnerable economic crop in East Asia, highly impacted by climate change. This study attempts to interpret tea land use/land cover (LULC) using very high resolution WorldView-2 imagery of central Taiwan with both pixel and object-based approaches. A total of 80 variables derived from each WorldView-2 band with pan-sharpening, standardization, principal components and gray level co-occurrence matrix (GLCM) texture indices transformation, were set as the…


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