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

In International Journal of Remote Sensing
Volume (Issue): 34 (20)
Peer-reviewed Article

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.

Hoan, Nguyen Thanh