A hybrid pansharpening approach and multiscale object-based image analysis for mapping diseased pine and oak trees |
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| Authors: | Brian Alan Johnson Ryutaro Tateishi Nguyen Thanh Hoan |
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| Affiliation: | 1. Centre for Environmental Remote Sensing (CEReS) , Chiba University, 1-33 Yayo-icho , Inage , Chiba , 263-8522 , Japan;2. Institute for Global Environmental Strategies, 2108-11 Kamiyamaguchi , Hayama , Kanagawa , 240-0115 , Japan bjohns53@fau.edu;4. Centre for Environmental Remote Sensing (CEReS) , Chiba University, 1-33 Yayo-icho , Inage , Chiba , 263-8522 , Japan;5. Institute of Geography, Vietnamese Academy of Science and Technology (VAST) , 18-Hoang Quoc Viet , Hanoi , Vietnam |
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| Abstract: | We developed a multiscale object-based classification method for detecting diseased trees (Japanese Oak Wilt and Japanese Pine Wilt) in high-resolution multispectral satellite imagery. The proposed method involved (1) a hybrid intensity–hue–saturation smoothing filter-based intensity modulation (IHS-SFIM) pansharpening approach to obtain more spatially and spectrally accurate image segments; (2) synthetically oversampling the training data of the ‘Diseased tree’ class using the Synthetic Minority Over-sampling Technique (SMOTE); and (3) using a multiscale object-based image classification approach. Using the proposed method, we were able to map diseased trees in the study area with a user's accuracy of 96.6% and a producer's accuracy of 92.5%. For comparison, the diseased trees were mapped at a user's accuracy of 84.0% and a producer's accuracy of 70.1% when IHS pansharpening was used alone and a single-scale classification approach was implemented without oversampling the ‘Diseased tree’ class. |
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