We developed a new hierarchical joint segmentation technique, which provides an effective fusion of a sequence of
multitemporal single-channel SAR images of a given area with a multispectral optical image over the same target area.
The proposed segmentation method is totally unsupervised, and it allows identifying regions that are homogeneous with
respect to the whole data set (both optical and multitemporal SAR images). This is accomplished, first, by modeling the
statistic of the joint distribution of SAR and optical data, then treating the multi-channel input images as a single entity,
and performing the segmentation using information from all channels simultaneously. To this purpose, we consider two
different statistical models: 1) multivariate Gaussian model for the multiband optical images and gamma distribution for
the SAR images, 2) again multivariate Gaussian model for the multiband optical images and multivariate log-normal
distribution for the SAR images.
The proposed segmentation algorithm is based on a fast multi-scale iterated weighted aggregation method and
generalized to multispectral remote sensing data in. A quantitative analysis of the proposed joint segmentation
technique for the fusion of multitemporal SAR and multispectral optical images is carried out using real images. To this
purpose, any desired classification schema can be applied after the segmentation step on the identified homogeneous
regions, which allows the full exploitation of the spatial-temporal information available in the multitemporal and
multisource data. Results show that the proposed joint segmentation technique, combined with even simple
classification methods, greatly improves the discrimination capability of the classifier.
We present a new hierarchical image segmentation algorithm, which extends a fast multiscale iterated weighted aggregation method originally developed by Brandt et al.(<sup>1, 2</sup>) to multichannel data. This approach, combining both a region-based and an edge-based approach, results well suited for data (like remote sensing images), which present heterogeneous multiscale characteristics. The generalization of the weighted aggregation method to multispectral remote sensing data requires the choice of a suitable statistical model to describe the multichannel data, together with an appropriate spectral homogeneity criterion. To this purpose, we adopted the multivariate Gaussian model, and the Bhattacharyya distance. For multivariate Gaussian distribution the Bhattacharyya distance results in a closed expression, which is an analytical function of the mean and covariance matrix. Such statistical moments can be computed recursively during the segmentation process, preserving the overall linear complexity of the segmentation process. Our segmentation method has been successfully integrated into Knowledge Information Mining (KIM), a tool developed within an ESA research project for content-based image retrieval from remote sensing large archives. Moreover, the proposed segmentation procedure has been extensively used within the DesertWatch project, an ESA Data User Program (DUP) project for the study and the monitoring of desertification processes from multiple Earth Observation data.