An adaptive thresholding approach to multiple-change detection in multispectral images

In this paper an unsupervised approach to multiple change detection based on adaptive thresholding is proposed. The method is developed in the polar framework for change vector analysis in multispectral images recently presented in the literature. According to the properties of spectral change vectors in the polar framework a procedure is presented based on the following steps: i) identification of unchanged and changed pixels along the magnitude variable (different kinds of change are treated as being a single class); ii) isolation of different kinds of changes along the direction variable; iii) refinement of the decision threshold along the magnitude variable according to the properties of each kind of detected change considered independently. The proposed method is validated on a pair of multitemporal images acquired by the Landsat-5 satellite including 3 kinds of changes.