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14 May 2019 Semantic segmentation based large-scale oil palm plantation detection using high-resolution satellite images
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Abstract
Detecting oil palm plantation from high-resolution satellite images can provide the necessary information for palm oil production estimation and oil palm plantation layout planning, etc. In this paper, we proposed a novel semantic segmentation based approach for large-scale oil palm plantation detection using QuickBird images and Google Earth Images (in 0.6-m spatial resolution) in Malaysia. We manually labeled a dataset for pixel-wise semantic segmentation into four categories: oil palm plantation, other vegetation, impervious/cloud, and the others (e.g. water and uncertain pixels). We presented an end-to-end deep convolutional neural network (DCNN) for semantic segmentation followed by fully connected conditional random fields (CRF) and applied an ensemble learning method to improve the localization of boundaries. The overall accuracy and mean IoU of our proposed approach in test regions are 95.27% and 88.46%, which are greatly better than the results of the other three common semantic segmentation methods and patch-based CNN method.
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Runmin Dong, Weijia Li, Haohuan Fu, Maocai Xia, Juepeng Zheng, and Le Yu "Semantic segmentation based large-scale oil palm plantation detection using high-resolution satellite images", Proc. SPIE 10988, Automatic Target Recognition XXIX, 109880D (14 May 2019); https://doi.org/10.1117/12.2514438
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