Title: Multitemporal Analysis of Multisensor Data: Information Theoretical Approaches
Authors: GUEGUEN LIONELCUI ShiyongSCHWARZ GottfriedDATCU Mihai
Citation: Proceedings of 2010 IEEE International Geoscience and Remote Sensing Symposium (IGARSS) - ISBN: 978-1-4244-9566-5 p. 2559-2562
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Publication Year: 2010
JRC Publication N°: JRC56497
URI: http://publications.jrc.ec.europa.eu/repository/handle/JRC56497
DOI: 10.1109/IGARSS.2010.5651902
Type: Contributions to Conferences
Abstract: Satellite Multitemporal Observations and Satellite Image Time Series are a new type of data sets acquired with optical and Synthetic Aperture Radar (SAR) technologies. Presently, due to the large number of Earth Observation satellites, every location on Earth can be observed very often; however, the observations in practical situations are usually multisensor. As a result, new models and algorithms are needed to automatically perform the data analysis. This paper presents two approaches for the analysis of multitemporal and multisensor data analysis. The first approach studies similarity measures derived from information measures and we assess their performance for multitemporal analysis. The temporal evolution of the scene changes is measured as a variation in the common and disjoint information measures between the different sensor data models. The second approach proposes the use of specific density estimation methods to reduce approximations effects for the detection of the multisensory changes. Usually the problem of change detection can be decomposed into two steps [1]: the generation of a change map based on the comparison of the 2 images using a similarity function, and a change classification performed either in un-supervised or supervised schema. In the former situation certain kinds of prior information about the scene, such as prior distribution assumption mode, is necessary. This paper will focus on an unsupervised approach of the generation of the indicator image in order to produce the binary change map. This approach is based on the comparison of local statistical similarity measures. In the literature, there several kinds of similarity measure, among which mutual information based similarity measures, have been proved to be more efficient. In [2], have been introduced several similarity measures between multi-sensor images. These measures are based on concepts such as statistical dependence or mutual information. The use of these measures allows for the design of image registration algorithms and automatic change detection techniques. And also in [3] are presented a new approach 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 compare the performance of each similarity measures. And he concludes the CRA and the mutual information measures perform the best. Taking advantage of the Mutual Information, in (5) is proposed a pixel-based approach comparing the localized Mutual Information shared by two pixels. When the two pixels share a lot of Mutual Information, their location is considered unchanged. From this idea, in (4) it was introduced a new informational measure derived from the Mutual Information: the Mixed Information. This new informational measure quanti¿es in a unique framework the shared and different informations such that time separated pixels with different informations are set changed. The new method based on Mixed Information was also proffed to be invariant to nonlinear changes, thus we further investigate ist use for multi-sensor cahnge detectin. Both the methods of in [3] and [4] are based on distribution estimation as histograms. In [1] it is suggested that the histogram method should be avoided due to the need of a high number of samples for estimation. As alternative, a cumulant-based density estimation is used to derive the local distribution which is furtehr used to compare the similarity between two images with the Kullback-Leibler divergence. The marginal distributions of each image are used To overcome the shortcomings, this paper presents an alternative scheme which is an extension of the work in [4].
JRC Institute:Institute for the Protection and Security of the Citizen

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