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

Luca Galli; Davide Passaro; Serena Avolio

doi:10.1117/12.737741

24 October 2007 A multiscale joint segmentation technique for multitemporal and multisource remote sensing images

Luca Galli, Davide Passaro, Serena Avolio

Proceedings Volume 6748, Image and Signal Processing for Remote Sensing XIII; 674804 (2007) https://doi.org/10.1117/12.737741
Event: SPIE Remote Sensing, 2007, Florence, Italy

Abstract

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.

Citation Download Citation

Luca Galli, Davide Passaro, and Serena Avolio "A multiscale joint segmentation technique for multitemporal and multisource remote sensing images", Proc. SPIE 6748, Image and Signal Processing for Remote Sensing XIII, 674804 (24 October 2007); https://doi.org/10.1117/12.737741

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