Spectral change detection

Abstract

Digital change detection is the computerized process of identifying changes in the state of an object, or other earth-surface features, between different dates. During the last years, a large number of change detection methods have evolved that differ widely in refinement, robustness and complexity. This study aims to develop a program to detect and delineate landscape changes automatically over multiple scales using a formulation of spectral classifiers. The procedure calculates, for each pixel, SAM, SCM or Euclidian distance value between the spectra at time t1 and t2. By considering a high threshold value, it is possible to define points with the same spectral behavior and probably without alteration during the period. In particular, this method allows for the automatic identification of invariant points to calibrate remote-sensing images, without visual interpretation data. Users' program establishes the spectral change detection (SCD) method (SAM-SCD, SCM- SCD and Euclidian Distance-SCD). Thus, the program allows to work simultaneously with the collection of temporal images. This realization is an important task in landscape analysis and remote sensing.