Analysis and Adaptive Estimation of the Registration Noise Distribution in Multitemporal VHR Images

Abstract

This paper analyzes the problem of change detection in very high resolution (VHR) multitemporal images by studying the effects of residual misregistration [registration noise (RN)] between images acquired on the same geographical area at different times. In particular, according to an experimental analysis driven from a theoretical study, the main effects of RN on VHR images are identified and some important properties are derived and described in a polar framework for change vector analysis. In addition, a technique for an adaptive and unsupervised explicit estimation of the RN distribution in the polar domain is proposed. This technique derives the RN distribution according to both a multiscale analysis of the distribution of spectral change vectors and the Parzen windows method. Experimental results obtained on simulated and real multitemporal data sets confirm the validity of the proposed analysis, the reliability of the derived properties on RN, and the effectiveness of the proposed estimation technique. This technique represents a very promising tool for the definition of change-detection methods for VHR multitemporal images robust to RN.