ジョンソン ブライアン

Data set for monitoring Japanese forest health using satellite imagery and machine learning techniques. Published in the UCI Machine Learning Repository (http://archive.ics.uci.edu/ml/datasets/Wilt), the largest machine learning database worldwide. Researchers can use the data set to learn, develop, and improve methods for forest monitoring using...

A new pansharpening method was developed and applied to Landsat 8’s multispectral image bands. It involves adding two new terms to the commonly-used Fast Intensity-Saturation-Hue (FIHS) algorithm: a trend-based modulation factor, and a band modulation factor. The proposed method was tested on an urban study area in Yokohama, Japan and an...

This study proposed a method to remove clouds from MODIS 250 meter, 8-day composite imagery for global land studies.

In this study, a multi-scale approach was used for mapping land cover in a high resolution image of an urban area. Pixels and image segments were assigned the spectral, texture, size, and shape information of their super-objects (i.e. the segments that they are located within) from coarser segmentations of the same scene, and this set of super...

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...