A New Cascade Model for the Hierarchical Joint Classification of Multitemporal and Multiresolution Remote Sensing Data

Abstract : In this paper, we propose a novel method for the joint classification of both multidate and multiresolution remote sensing imagery, which represents an important and relatively unexplored classification problem. The proposed classifier is based on an explicit hierarchical graph-based model that is sufficiently flexible to address co-registered time series of images collected at different spatial resolutions. Within this framework, a novel element of the proposed approach is the use of multiple quad-trees in cascade, each associated with the images available at each observation date in the considered time series. For each date, the input images are inserted in a hierarchical structure on the basis of their resolutions, whereas missing levels are filled in with wavelet transforms of the images embedded in finer-resolution levels. This approach is aimed at both exploiting multiscale information, which is known to play a crucial role in high resolution image analysis, and supporting input images acquired at different resolutions in the input time series. The experimental results are shown for multitemporal and multiresolution optical data. Index Terms— Satellite image time series, multitemporal classification, hierarchical multiresolution Markov random fields.
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IEEE Transactions on Geoscience and Remote Sensing, Institute of Electrical and Electronics Engineers, 2016, 54 (11), pp.6333 - 6348
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Dernière modification le : jeudi 30 novembre 2017 - 09:20:39
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  • HAL Id : hal-01308039, version 1

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Ihsen Hedhli, Gabriele Moser, Josiane Zerubia, Sebastiano Serpico. A New Cascade Model for the Hierarchical Joint Classification of Multitemporal and Multiresolution Remote Sensing Data . IEEE Transactions on Geoscience and Remote Sensing, Institute of Electrical and Electronics Engineers, 2016, 54 (11), pp.6333 - 6348. 〈hal-01308039〉

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