A robust neural network design for detecting changes from multispectral satellite imagery

Abstract

The advent of very high spatial resolution optical satellite imagery has greatly increased our ability to monitor land cover changes in urban environments where the spatial resolution plays a key role related to the detection of fine-scale objects such as a single house or small structures. At the same time, very high spatial resolution imagery presents a new challenge over other satellite systems, in that a relatively large amount of data must be analyzed and corrected for registration and classification errors to identify the land cover changes, commonly resulting in a very extensive manual work. To improve on this situation we have developed a new method for land surface change detection that greatly reduces the human effort needed to remove the errors that occur with many methods applied to very high spatial resolution imagery. This change detection algorithm is based on Neural Networks and it is able to exploit in parallel both the multi-band and the multi-temporal data to discriminate between real changes and false alarms. In general the classification errors are reduced by a factor of 2–3 using this new method over a simple Post Classification Comparison based on a neural network classification of the same images.