Abstract
Abstract. In this paper, a multiscale Markov framework is proposed in order to address the problem of the classification of multiresolution and multisensor remotely sensed data. The proposed framework makes use of a quadtree to model the interactions across different spatial resolutions and a Markov model with respect to a generic total order relation to deal with contextual information at each scale in order to favor applicability to very high resolution imagery. The methodological properties of the proposed hierarchical framework are investigated. Firstly, we prove the causality of the overall proposed model, a particularly advantageous property in terms of computational cost of the inference. Secondly, we prove the expression of the marginal posterior mode criterion for inference on the proposed framework. Within this framework, a specific algorithm is formulated by defining, within each layer of the quadtree, a Markov chain model with respect to a pixel scan that combines both a zig-zag trajectory and a Hilbert space-filling curve. Data collected by distinct sensors at the same spatial resolution are fused through gradient boosted regression trees. The developed algorithm was experimentally validated with two very high resolution datasets including multispectral, panchromatic and radar satellite images. The experimental results confirm the effectiveness of the proposed algorithm as compared to previous techniques based on alternate approaches to multiresolution fusion.
Highlights
Thanks to the potential offered by current and forthcoming space missions, very high spatial resolution (VHR) optical (e.g., Pleiades, WorldView-3, SPOT-6/7) and synthetic aperture radar (SAR; e.g., COSMO-SkyMed Seconda Generazione, TerraSAR-X, RADARSAT-2) instruments are available to serve many applications
A major challenge in this joint multisensor-multiresolution scenario is the combination of heterogeneous statistics of the input images and of the need to characterize spatial information associated with different resolutions (Hedhli et al, 2017)
We extend the approach developed in (Montaldo et al, 2019a, Montaldo et al, 2019b), in which hierarchical Markov random fields (MRFs) on quadtrees have been combined with planar Markov meshes and with various pixelwise probabilistic models, to a broader framework in which each layer of the quadtree is associated with a Markov model with respect to an arbitrary total order relation
Summary
Thanks to the potential offered by current and forthcoming space missions, very high spatial resolution (VHR) optical (e.g., Pleiades, WorldView-3, SPOT-6/7) and synthetic aperture radar (SAR; e.g., COSMO-SkyMed Seconda Generazione, TerraSAR-X, RADARSAT-2) instruments are available to serve many applications. From the viewpoint of image analysis, the joint exploitation of the resulting data requires novel methods that operate with images collected by multiple sensors on the same area at multiple spatial resolutions. This is highly promising because it allows to benefit from data associated with different physical natures, frequencies, polarizations, etc., and from the tradeoff between a synoptic view at coarser resolutions and the spatial detail of finer resolutions. A major challenge in this joint multisensor-multiresolution scenario is the combination of heterogeneous statistics of the input images and of the need to characterize spatial information associated with different resolutions (Hedhli et al, 2017). Trivial well-known solutions mostly use resampling procedures and do not attempt to capture the multiresolution structure of the data explicitly
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