Multi-resolution classification of urban areas using hierarchical symmetric Markov mesh models

Hedhli, Ihsen; Moser, Gabriele; Zerubia, Josiane

doi:10.1109/jurse.2017.7924567

Cited by 3 publications

(13 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…First, (9) is used to calculate P px s q on all sites through a top-down pass from the root to the leaves. Then, (10) and (11) Details on the initialization of these recursions and on the parametric modeling of the transition probabilities P px s |x s´q and P px s |x r q, r À s, can be found in [9]. Here, we focus on deriving (9)-(12) and on the related assumptions.…”

Section: Inference Algorithm and Mpm Criterionmentioning

confidence: 99%

“…The approach used here is based on hierarchical latent Markov modeling [8]. We focus on the framework that we recently developed in [9] and we investigate its methodological properties in terms of causality of the stochastic model and analytical formulation of the inference.…”

Section: Introductionmentioning

confidence: 99%

“…In [9], we have developed a hierarchical MMRF that integrates both modeling approaches in a unique framework. In this model, Markovianity is postulated both across the scales of a quadtree and with respect to the neighborhood system of a mesh associated with each layer of the tree.…”

Section: Introductionmentioning

confidence: 99%

“…This joint strategy benefits from the spatial information within each layer and inherently supports multiresolution fusion. The model has been combined with both Gaussian mixtures in the application to multiresolution optical data [9] and with decision tree ensembles in the application to multiresolution and mul-tisensor (optical and radar) imagery [17]. In this framework, the present paper addresses the methodological properties of this combined hierarchical Markov mesh.…”

Section: Introductionmentioning

confidence: 99%

“…Its formulation for the hierarchical MMRF explicitly relies on the causality of the model. Examples of experimental results obtained within this framework, in addition to those in [9,17], are shown in a case study associated with high resolution satellite multiresolution data of an urban and agricultural area and with the application to land cover mapping for flood risk management [18].…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Causal Markov Mesh Hierarchical Modeling for the Contextual Classification of Multiresolution Satellite Images

Montaldo

Fronda

Hedhli

et al. 2019

2019 IEEE International Conference on Image Processing (ICIP)

View full text Add to dashboard Cite

In this paper, we address the problem of the joint classification of multiple images acquired on the same scene at different spatial resolutions. From an application viewpoint, this problem is of importance in several contexts, including, most remarkably, satellite and aerial imagery. From a methodological perspective, we use a probabilistic graphical approach and adopt a hierarchical Markov mesh framework that we have recently developed and models the spatial-contextual classification of multiresolution and possibly multisensor images. Here, we focus on the methodological properties of this framework. First, we prove the causality of the model, a highly desirable property with respect to the computational cost of the inference. Then, we prove the expression of the marginal posterior mode criterion for this model and discuss the related assumptions. Experimental results with multispectral and panchromatic satellite images are also presented.

show abstract

Section: Inference Algorithm and Mpm Criterionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Causal Markov Mesh Hierarchical Modeling for the Contextual Classification of Multiresolution Satellite Images

Montaldo

Fronda

Hedhli

et al. 2019

2019 IEEE International Conference on Image Processing (ICIP)

View full text Add to dashboard Cite

show abstract

Joint Classification of Multiresolution and Multisensor Data Using a Multiscale Markov Mesh Model

Montaldo

Fronda

Hedhli

et al. 2019

IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium

Self Cite

View full text Add to dashboard Cite

In this paper, the problem of the classification of multiresolution and multisensor remotely sensed data is addressed by proposing a multiscale Markov mesh model. Multiresolution and multisensor fusion are jointly achieved through an explicitly hierarchical probabilistic graphical classifier, which uses a quadtree structure to model the interactions across different spatial resolutions, and a symmetric Markov mesh random field to deal with contextual information at each scale and favor applicability to very high resolution imagery. Differently from previous hierarchical Markovian approaches, here, data collected by distinct sensors are fused through either the graph topology itself (across its layers) or decision tree ensemble methods (within each layer). The proposed model allows taking benefit of strong analytical properties, most remarkably causality, which make it possible to apply time-efficient non-iterative inference algorithms.

show abstract

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

et al. 2021

Self Cite

View full text Add to dashboard Cite

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.

show abstract

Multi-resolution classification of urban areas using hierarchical symmetric Markov mesh models

Cited by 3 publications

References 15 publications

Causal Markov Mesh Hierarchical Modeling for the Contextual Classification of Multiresolution Satellite Images

Causal Markov Mesh Hierarchical Modeling for the Contextual Classification of Multiresolution Satellite Images

Joint Classification of Multiresolution and Multisensor Data Using a Multiscale Markov Mesh Model

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

Contact Info

Product

Resources

About