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Article type: Research Article
Authors: Le Hégarat-Mascle, S. | Richard, D. | Ottlé, C.
Affiliations: CETP/CNRS, 10-12 av. de l'Europe, 78140 Vélizy, France. Tel.: +33 1 3925 49 34; Fax: +33 1 39 25 49 22; E-mail: [email protected]
Abstract: In the remote sensing domain, the combination of multi-scale satellite data appears as a new challenge for the signal processing community. This approach will lead to strong advances in Earth monitoring and continental land cover classifications by use of the complementary of the data presenting either high spatial resolution or high time repetitiveness. For the modelling of the mixed feature of the low spatial resolution pixels, and those of the partial ignorance (class confusion) when time information is not sufficient, we propose an algorithm based on the Dempster-Shafer evidence theory, which allows to model both ignorance and imprecision, and to consider compound hypotheses such as unions of classes. It has been applied on simulated data and actual data (SPOT/HRV image and NOAA/AVHRR series), and in both cases, the results show unambiguously the major improvement brought by such a data fusion, and the performance of the proposed method.
DOI: 10.3233/ICA-2003-10103
Journal: Integrated Computer-Aided Engineering, vol. 10, no. 1, pp. 9-22, 2003
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