Due to the enormous quantity of radar images acquired by satellites and through shuttle missions, there is an evident need for efficient automatic analysis tools. This paper describes unsupervised classification of radar images in the framework of hidden Markov models and generalized mixture estimation. Hidden Markov chain models, applied to a Hilbert–Peano scan of the image, constitute a fast ...

In this work, we propose to use the Hidden Markov Chain (HMC) model for fully automatic change detection in a temporal set of Synthetic Aperture Radar (SAR) images. First, it is shown that this model can be used as an alternative to the Hidden Markov Random Field (HMRF) model in the image differencing context. We then propose a novel approach, called joint characterization, whose principle is t...

Markov random fields provide a compact representation of joint probability distributions by representing its independence properties in an undirected graph. The well-known Hammersley-Clifford theorem uses these conditional independences to factorize a Gibbs distribution into a set of factors. However, an important issue of using a graph to represent independences is that it cannot encode some t...

In this paper, we discuss a Markov Random Field (MR) modeling for multisource and multitemporal remotely sensed image fusion and classification. Satellite images provided by individual sensor are incomplete, inconsistent or imprecise. Additional sources may provide complementary information and the fusion of multisource data can create a more consistent interpretation of the scene in which the ...

We present a data-driven approach to predict the importance of edges and construct a Markov network for image analysis based on statistical models of global and local image features. We also address the coupled problem of predicting the feature weights associated with each edge of a Markov network for evaluation of context. Experimental results indicate that this scene dependent structure const...

Wepropose a combination of shape prior models with Markov Random Fields. The model allows to integrate multiple shape priors and appearance models into MRF-models for segmentation. We discuss a recognition task and introduce a general learning scheme. Both tasks are solved in the scope of the model and verified experimentally.

We present a nonparametric Markov Random Field model for classifying texture in images. This model can capture the characteristics of a wide variety of textures, varying from the highly structured to the stochastic. The power of our modelling technique is evident in that only a small training image is required, even when the training texture contains long range characteristics. We show how this...

This paper presents Japanese morphological analysis based on conditional random fields (CRFs). Previous work in CRFs assumed that observation sequence (word) boundaries were fixed. However, word boundaries are not clear in Japanese, and hence a straightforward application of CRFs is not possible. We show how CRFs can be applied to situations where word boundary ambiguity exists. CRFs offer a so...

Droughts are one of the most damaging climate-related hazards. The late 1960s Sahel drought in Africa and the North American Dust Bowl of the 1930s are two examples of severe droughts that have an impact on society and the environment. Due to the importance of understanding droughts, we consider the problem of their detection based on gridded datasets of precipitation. We formulate the problem ...