A New System to Perform Unsupervised and Supervised Classification of Satellite Images from Google Maps

摘要

In this paper, we describe a new system for unsupervised and supervised classification of satellite images from Google Maps. The system has been developed using the SwingX-WS library, and incorporates functionalities such as unsupervised classification of image portions selected by the user (at the maximum zoom level) using ISODATA and k-Means, and supervised classification using the Minimum Distance and Maximum Likelihood, followed by spatial post-processing based on majority voting. Selected regions in the classified portion are used to train a maximum likelihood classifier able to map larger image areas in a manner transparent to the user. The system also retrieves areas containing regions similar to those already classified. An experimental validation of the proposed system has been conducted by comparing the obtained classification results with those provided by commercial software, such as the popular Research Systems ENVI package.