抄録
Elaborate processes are required for land cover classification using multiscene high spatial resolution satellite images, like selecting training area on each scene with sufficient a priori knowledge. The classification method proposed in this paper is assumed to use both high spatial resolution images and temporal low spatial resolution images. It can automatically produce training data set on each scene, optimized considering land cover characteristics to the scene. Moreover, it prevents from deteriorating into low classification accuracy, by referring to and checking consistency to the class candidate information derived from temporal low spatial resolution images. Experiments were conducted that used twenty-eight scenes of Landsat TM and NOAA AVHRR images as high and low spatial resolution images, respectively. Validation results by using three visually interpreted images demonstrate the optimization of training data set improved the classification accuracy from 59.0% to 66.2%, and the class candidate information improved from 61.9% to 66.2%.
