An Approach to Multiple Change Detection in VHR Optical Images Based on Iterative Clustering and Adaptive Thresholding

One of the most common approaches to unsupervised change detection (CD) in multispectral images is change vector analysis (CVA). CVA computes the multispectral difference image and exploits its statistical distribution in (hyper-) spherical coordinates by means of two steps: 1) magnitude and 2) direction thresholding. The two steps require assumptions on: 1) the model of class distributions and 2) the number of changes. However, both assumptions are seldom satisfied or difficult to formulate, especially when considering VHR images. Thus, we propose an approach to multiple CD in VHR optical images based on iterative clustering and adaptive thresholding in (hyper-) spherical coordinate. The proposed approach: 1) is distribution free; 2) is unsupervised; 3) automatically identifies the number of changes; and 4) is robust to noise. Results obtained on two multitemporal single-sensor and multisensor data sets, including images from WorldView-2 and QuickBird, corroborate the effectiveness of the proposed approach.