An Unsupervised Technique Based on Morphological Filters for Change Detection in Very High Resolution Images

Abstract

An unsupervised technique for change detection (CD) in very high geometrical resolution images is proposed, which is based on the use of morphological filters. This technique integrates the nonlinear and adaptive properties of the morphological filters with a change vector analysis (CVA) procedure. Different morphological operators are analyzed and compared with respect to the CD problem. Alternating sequential filters by reconstruction proved to be the most effective, permitting the preservation of the geometrical information of the structures in the scene while filtering the homogeneous areas. Experimental results confirm the effectiveness of the proposed technique. It increases the accuracy of the CD process as compared with the standard CVA approach.