Mr. Luca Galli Profile

Luca Galli

at Advanced Computer Systems SpA

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Proceedings Article | 24 October 2007 Paper

A multiscale joint segmentation technique for multitemporal and multisource remote sensing images

Luca Galli, Davide Passaro, Serena Avolio

Proceedings Volume 6748, 674804 (2007) https://doi.org/10.1117/12.737741

KEYWORDS: Image segmentation, Synthetic aperture radar, Image fusion, Statistical modeling, Data modeling, Statistical analysis, Image classification, Multispectral imaging, Remote sensing, Image processing

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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.

Proceedings Article | 19 October 2005 Paper

Multispectral image segmentation via multiscale weighted aggregation method

Luca Galli, Damiana de Candia

Proceedings Volume 5982, 598207 (2005) https://doi.org/10.1117/12.627534

KEYWORDS: Image segmentation, Remote sensing, Image processing, Multispectral imaging, Statistical analysis, Image processing algorithms and systems, Spatial resolution, Algorithm development, Data modeling, Earth observing sensors

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We present a new hierarchical image segmentation algorithm, which extends a fast multiscale iterated weighted aggregation method originally developed by Brandt et al.(^{1, 2}) 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.

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