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.

In Burkitt's lymphoma (BL) cells c-myc is often translocated in proximity to the Emu enhancer of the Ig gene locus. This translocation causes c-myc hyperexpression and an increase in the cells' proliferative capacity. A peptide nucleic acid (PNA) complementary to enhancer Emu intronic sequence (PNAEmu), linked to a nuclear localization signal (NLS), selectively and specifically blocks the expression of the c-myc oncogene under Emu control in vitro, suggesting potential therapeutic use. To explore this issue further, we have determined the pharmacokinetics of (14)C-labeled PNAEmu in SCID mice where a human tumor is established by inoculation of cells from a BL cell line. The data demonstrate that the compound has a relatively long life in vivo in tissues and, in particular, in BL tumor mass. Furthermore, in this animal model, PNAEmu shows low or no toxicity. All these results are in favor of a successful preclinical application in a BL human tumor animal model of a PNA targeting a regulatory, nontranscribed DNA sequence that can selectively inhibit the hyperexpression of a translocated gene linked to neoplastic cell expansion.