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…


Link to Full Article: A Comparative Analysis of Machine Learning with WorldView-2 Pan-Sharpened Imagery for Tea …

Pin It on Pinterest

Share This

Join Our Newsletter

Sign up to our mailing list to receive the latest news and updates about homeAI.info and the Informed.AI Network of AI related websites which includes Events.AI, Neurons.AI, Awards.AI, and Vocation.AI

You have Successfully Subscribed!