A hybrid pansharpening approach and multiscale object-based image analysis for mapping diseased pine and oak trees

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 (IHS)/Smoothing Filter-based Intensity Modulation (SFIM) pansharpening approach 10 (IHS-SFIM) 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 15 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.