Toward Fully Automatic Detection of Changes in Suburban Areas From VHR SAR Images by Combining Multiple Neural-Network Models

Recent X-band SAR missions, such as COSMO-SkyMed (CSK), which is able to provide very high spatial resolution images of an area of interest with a short revisit time, are expected to be quite useful sources of information for monitoring the terrestrial environment and its changes. On the other hand, the huge amount of data involved, as well as the need to promptly act in case of emergency, requires the development of automatic change detection tools. This paper reports on a novel automatic change detection algorithm combining multilayer perceptron neural networks (NNs) and pulse coupled NNs, which has been implemented and tested on pairs of Stripmap and Spotlight CSK images acquired on the Tor Vergata University area in the southeast outskirts of Rome, Italy, where a significant and continuous urbanization process is occurring.