An automated flood detection framework for very high spatial resolution imagery

The quantity and the updating time of the archives of very high spatial resolution visible and near-infrared remote sensing images for commercial use improved during the last years. This led to the detection of changes on the Earth surface through remote sensing images to become a key analytical tool for many public and private organizations, which can take advantage of the information carried out to help and improve their decision making processes. This paper proposes an unsupervised method for detecting multiple changes in the application to damage assessment after a flood. It is composed of five steps, and is based on a change vector analysis approach. After a case-specific feature extraction stage, through a process called normalized difference indexing, the change detection task is carried out by modeling the classes of changed and not changed pixels with a Gaussian finite mixture model, using the expectation-maximization algorithm to estimate the statistical parameters involved. Then, the mean shift clustering algorithm is used to discriminate among different types of change. The method has been tested on a pair of images acquired by WorldView-2 and associated with the 2013 flood in Colorado.