Change detection tools

In this chapter a wide range of change detection tools is addressed. They are grouped into methods suitable for optical and multispectral data, synthetic aperture radar (SAR) images, and 3D data. Optical and multispectral methods include unsupervised approaches, supervised and knowledge-based approaches, pixel-based and object-oriented approaches, multivariate alteration detection, hyperspectral approaches, and approaches that deal with changes between optical images and existing vector data. Radar methods include constant false-alarm rate detection, adaptive filtering, multi-channel segmentation (an object-oriented approach), hybrid methods, and coherent change detection. 3D methods focus on tools that are able to deal with 3D information from ground based laser-ranging systems, LiDAR, and elevation models obtained from air/space borne optical and SAR data. Highlighted applications are landcover change, which is often one of the basic types of information to build analysis on, monitoring of nuclear safeguards, third-party interference close to infrastructures (or borders), and 3D analysis. What method to use is dependent on the sensor, the size of the changes in comparison with the resolution, their shape, textural properties, spectral properties, and behaviour in time, and the type of application. All these issues are discussed to be able to determine the right method, with references for further reading