Classification of multi-scene high-spatial resolution images by using information obtained from temporal low-spatial resolution images

Elaborate jobs are required for land cover classification using multi-scene 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 time-series low-spatial resolution images. It can automatically produce training data set on each scene, optimized considering land-cover features to the scene. Moreover, it prevents from deteriorating into low accuracy classification result, by referring to the class candidate information derived from time-series low-spatial resolution images. Experiments were conducted that used Landsat TM and NOAA AVHRR images as high-spatial and low-spatial resolution images respectively. Validation results by using three visually interpreted TM images demonstrate the optimization of training data set improved the classification accuracy from 56.0% to 66.2%, and the class candidate in formation did from 61.9% to 66.2%.