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

Abstract

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 landcover 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 information did from 61.9% to 66.2%.