10 November 2004 Automatic partially supervised classification of multitemporal remotely sensed images
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Abstract
The use of remotely sensed imagery for environmental monitoring naturally leads to operate with multitemporal images of the geographical area of interest. In order to generate thematic maps for all acquisition dates, an unsupervised classification algorithm is not effective, due to the lack of knowledge about the thematic classes. On the other hand, a detailed analysis of all the land-cover transitions is naturally accomplished in a completely supervised context, but the ground-data requirement involved by this approach is not realistic in case of short rivisit time. An interesting trade-off is represented by the partially supervised approach, exploiting ground truth only for a subset of the acquisition dates. In this context, a multitemporal classification scheme has been proposed previously by the authors, which deals with a couple of images of the same area, assuming ground truth to be available only at the first date. In the present paper, several modifications are proposed to this system in order to automatize it and to improve the detection performances. Specifically, a preprocessing algorithm is developed, which addresses the problem of mismatches in the dynamics of images acquired at different times over the same area, by both automatically correcting strong dynamics differences and detecting cloud areas. In addition, the clustering procedures integrated in the system are fully automatized by optimizing the selection of the numbers of clusters according to Bayesian estimates of the probability of correct classification. Experimental results on multitemporal Landsat-5 TM and ERS-1 SAR data are presented.
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Gabriele Moser, Gabriele Moser, Sebastiano Bruno Serpico, Sebastiano Bruno Serpico, Michaela De Martino, Michaela De Martino, Daniele Coppolino, Daniele Coppolino, } "Automatic partially supervised classification of multitemporal remotely sensed images", Proc. SPIE 5573, Image and Signal Processing for Remote Sensing X, (10 November 2004); doi: 10.1117/12.567968; https://doi.org/10.1117/12.567968
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