Abstract
Change detection of remotely sensed images is a particularly challenging task when the time series data come from different sensors. Indeed, many change indicators are based on radiometry measurements, used to calculate differences or ratios, that are no longer meaningful when the data have been acquired by different instruments. For this reason, it is interesting to study those indicators that do not rely completely on radiometric values. In this work a new approach is proposed based on similarity measures. A series of such measures is employed for automatic change detection of optical and SAR images and a comparison of their performance is carried out to establish the limits of their applicability and their sensitivity to the occurred changes. Initial results are promising and suggest similarity measures as possiblechange detectors in multi-sensor configurations.
Highlights
Change detection analyzes a pair of images of the same subject acquired at different times to detect eventual changes occurred between the two data collections
We present here a novel approach for change detection based on the use of similarity measures
Our idea is to profit from this property and to use the correspondence between the same points in the two images not to correct the relative displacement but, already given their precise coregistration, to detect eventual changes occurred between the data acquisitions. Another reason of interest for similarity measures is that basically no example of their use for change detection has been yet reported in the literature
Summary
Change detection analyzes a pair of images of the same subject acquired at different times to detect eventual changes occurred between the two data collections. Our idea is to profit from this property and to use the correspondence between the same points in the two images not to correct the relative displacement but, already given their precise coregistration, to detect eventual changes occurred between the data acquisitions Another reason of interest for similarity measures is that basically no example of their use for change detection has been yet reported in the literature. The selection of the similarity criterion, and the definition of the function f , can vary according to the type of images under analysis, the application (e.g., image registration or change detection) and the parameters used to define it (radiometric values, features characteristics, etc.). We will report the results obtained using five measures of these two main groups, namely: 1. Measures using only the probabilities:
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