Land Cover Analysis for Agricultural Area in Thailand Using CNN Method
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Abstract
To support agricultural development, increased understanding and data is needed. Performing a land cover enables obtain physical landscapes. The aim of this paper is to propose a land cover for agricultural area in Thailand using CNN method. A four-class dataset is created: forest, palm, rice, and buildings. It is composed of about 1,500,000 with a size of 64x64 pixels. Each class is associated with an optimized CNN model showing an accuracy between 80 and 90 %. After post-processing of each prediction, the land cover is successfully obtained by aggregating the different class predictions. An RGB filter is used to determine the maturation state of the rice and to differentiate the type of palm field. Finally, the estimation of the production of palm and rice can be performed.
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