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Abstract-This paper presents a novel approach to change detection in multitemporal synthetic aperture radar (SAR) images. The proposed approach exploits a wavelet-based multiscale decomposition of the log-ratio image (obtained by a comparison of the original multitemporal data) aimed at achieving different scales (levels) of representation of the change signal. Each scale is characterized by a different tradeoff between speckle reduction and preservation of geometrical details. For each pixel, a subset of reliable scales is identified on the basis of a local statistic measure applied to scale-dependent log-ratio images. The final changedetection result is obtained according to an adaptive scale-driven fusion algorithm. Experimental results obtained on multitemporal SAR images acquired by the ERS-1 satellite confirm the effectiveness of the proposed approach.Index Terms-Change detection, image analysis, multiscale image decomposition, remote sensing, synthetic aperture radar (SAR).

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The final version of the paper can be found in IEEE Geoscience and Remote Sensing Magazine. The sharp and recent increase in the availability of data captured by different sensors combined with their considerably heterogeneous natures poses a serious challenge for the effective and efficient processing of remotely sensed data. Such an increase in remote sensing and ancillary datasets, however, opens up the possibility of utilizing multimodal datasets in a joint manner to further improve the performance of the processing approaches with respect to the application at hand. Multisource data fusion has, therefore, received enormous attention from researchers worldwide for a wide variety of applications. Moreover, thanks to the revisit capability of several spaceborne sensors, the integration of the temporal information with the spatial and/or spectral/backscattering information of the remotely sensed data is possible and helps to move from a representation of 2D/3D data to 4D data structures, where the time variable adds new information as well as challenges for the information extraction algorithms. There are a huge number of research works dedicated to multisource and multitemporal data fusion, but the methods for the fusion of different modalities have expanded in different paths according to each research community. This paper brings together the advances of multisource and multitemporal data fusion approaches with respect to different research communities and provides a thorough and discipline-specific starting point for researchers at different levels (i.e., students, researchers, and The work of P. Ghamisi is supported by the "High Potential Program" of Helmholtz-Zentrum Dresden-Rossendorf.

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This paper presents an overview on the image fusion concept in the context of multitemporal remote sensing image processing. In the remote sensing literature, multitemporal image analysis mainly deals with the detection of changes and land-cover transitions. Thus the paper presents and analyses the most relevant literature contributions on these topics. From the perspective of change detection and detection of land-cover transitions, multitemporal image analysis techniques can be divided into two main groups: i) those based on the fusion of the multitemporal information at feature level, and ii) those based on the fusion of the multitemporal information at decision level. The former mainly exploit multitemporal image comparison techniques, which aim at highlighting the presence/ absence of changes by generating change indices. These indices are then analyzed by unsupervised algorithms for extracting the change information. The latter rely mainly on classification and include both supervised and semi/ partially-supervised/unsupervised methods. The paper focuses the attention on both standard (and largely used) methods and techniques proposed in the recent literature. image licensed by ingram publishing september 2015 ieee Geoscience and remote sensinG maGazine 9The analysis is conducted by considering images acquired by optical and SAR systems at medium, high and very high spatial resolution.