Multisensor and Multiresolution Remote Sensing Image Classification through a Causal Hierarchical Markov Framework and Decision Tree Ensembles.

Pastorino, Martina; Montaldo, Alessandro; Fronda, Luca; Hedhli, Ihsen; Moser, Gabriele; Serpico, Sebastiano B.; Zerubia, Josiane; Xia, Junshi

In this paper, a hierarchical probabilistic graphical model is proposed to tackle joint classification of multiresolution and multisensor remote sensing images of the same scene. This problem is crucial in the study of satellite imagery and jointly involves multiresolution and multisensor image fusion. The proposed framework consists of a hierarchical Markov model with a quadtree structure to model information contained in different spatial scales, a planar Markov model to account for contextual spatial information at each resolution, and decision tree ensembles for pixelwise modeling. This probabilistic graphical model and its topology are especially fit for application to very high resolution (VHR) image data. The theoretical properties of the proposed model are analyzed: the causality of the whole framework is mathematically proved, granting the use of time-efficient inference algorithms such as the marginal posterior mode criterion, which is non-iterative when applied to quadtree structures. This is mostly advantageous for classification methods linked to multiresolution tasks formulated on hierarchical Markov models. Within the proposed framework, two multimodal classification algorithms are developed, that incorporate Markov mesh and spatial Markov chain concepts. The results obtained in the experimental validation conducted with two datasets containing VHR multispectral, panchromatic, and radar satellite images, verify the effectiveness of the proposed framework. The proposed approach is also compared to previous methods that are based on alternate strategies for multimodal fusion.

REMOTE sensing; DECISION trees; MARKOV processes; REMOTE-sensing images; IMAGE fusion; MULTISPECTRAL imaging; MARKOV random fields

Remote Sensing, 2021, Vol 13, Issue 5, p849

2072-4292

Academic Journal

10.3390/rs13050849