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With the continuing increase in the number of images collected everyday from different sensors, the automated registration of multisensor/multispectral images has become a very important issue. This is particularly true when pre- and postevent image comparison is concerned: For this particular application, the requirement of obtaining the earliest possible postevent image imposes the use of data potentially possessing significantly different characteristics with respect to the pre-event image. Strongly inhomogeneous image pairs require robust automatic registration techniques, preferably based on resolution-independent feature-based registration. In a previous paper, we proposed a mode-based feature-matching scheme mutated from the computer vision domain and adapted to pre- and postevent feature matching. Some of the weak points highlighted in that first version are addressed in this paper, where a new version of the method is proposed, which exploits a new piece of information, i.e., the adjacency between feature points, generally preserved across the disaster event. Extensive generation of synthetic cases allowed one to obtain significant feedback and, consequently, tune the algorithm. Three real cases of pre- and postevent feature matching on high-resolution satellite images are shown and discussed.