Hierarchical Probabilistic Graphical Models and Deep Convolutional Neural Networks for Remote Sensing Image Classification

International audienceThe method presented in this paper for semantic segmentation of multiresolution remote sensing images involves convolutional neural networks (CNNs), in particular fully convolutional networks (FCNs), and hierarchical probabilistic graphical models (PGMs). These approaches are combined to overcome the limitations in classification accuracy of CNNs for small or non-exhaustive ground truth (GT) datasets. Hierarchical PGMs, e.g., hierarchical Markov random fields (MRFs), are structured output learning models that exploit information contained at different image scales. This perfectly matches the intrinsically multiscale behavior of the processes of a CNN (e.g., pooling layers). The framework consists of a hierarchical MRF on a quadtree and a planar Markov model on each layer, modeling the interactions among pixels and accounting for both the multiscale and the spatial-contextual information. The marginal posterior mode criterion is used for inference. The adopted FCN is the U-Net and the experimental validation is conducted on the ISPRS 2D Semantic Labeling Challenge Vaihingen dataset, with some modifications to approach the case of scarce GTs and to assess the classification accuracy of the proposed technique. The proposed framework attains a higher recall compared to the considered FCNs, progressively more relevant as the training set is further from the ideal case of exhaustive GTs

Semantic Segmentation of Remote Sensing Images through Fully Convolutional Neural Networks and Hierarchical Probabilistic Graphical Models

International audienceDeep learning (DL) is currently the dominant approach to image classification and segmentation, but the performances of DL methods are remarkably influenced by the quantity and quality of the ground truth (GT) used for training. In this article, a DL method is presented to deal with the semantic segmentation of very-high-resolution (VHR) remote-sensing data in the case of scarce GT. The main idea is to combine a specific type of deep convolutional neural networks (CNNs), namely fully convolutional networks (FCNs), with probabilistic graphical models (PGMs). Our method takes advantage of the intrinsic multiscale behavior of FCNs to deal with multiscale data representations and to connect them to a hierarchical Markov model (e.g., making use of a quadtree). As a consequence, the spatial information present in the data is better exploited, allowing a reduced sensitivity to GT incompleteness to be obtained. The marginal posterior mode (MPM) criterion is used for inference in the proposed framework. To assess the capabilities of the proposed method, the experimental validation is conducted with the ISPRS 2D Semantic Labeling Challenge datasets on the cities of Vaihingen and Potsdam, with some modifications to simulate the spatially sparse GTs that are common in real remote-sensing applications. The results are quite significant, as the proposed approach exhibits a higher producer accuracy than the standard FCNs considered and especially mitigates the impact of scarce GTs on minority classes and small spatial details

Fully convolutional and feedforward networks for the semantic segmentation of remotely sensed images

International audienceThis paper presents a novel semantic segmentation method of very high resolution remotely sensed images based on fully convolutional networks (FCNs) and feedforward neural networks (FFNNs). The proposed model aims to exploit the intrinsic multiscale information extracted at different convolutional blocks in an FCN by the integration of FFNNs, thus incorporating information at different scales. The purpose is to obtain accurate classification results with realistic data sets characterized by sparse ground truth (GT) data by taking benefit from multiscale and long-range spatial information. The final loss function is computed as a linear combination of the weighted cross-entropy losses of the FFNNs and of the FCN. The modeling of spatial-contextual information is further addressed by the introduction of an additional loss term which allows to integrate spatial information between neighboring pixels. The experimental validation is conducted with the ISPRS 2D Semantic Labeling Challenge data set over the city of Vaihingen, Germany. The results are promising, as the proposed approach obtains higher average classification results than the state-of-the-art techniques considered, especially in the case of scarce, suboptimal GTs

Semantic segmentation of remote sensing images combining hierarchical probabilistic graphical models and deep convolutional neural networks

International audienceIn this paper, a novel method to deal with the semantic segmentation of very high resolution remote sensing data is presented. Recent advances in deep learning (DL), especially convolutional neural networks (CNNs) and fully convolutional networks (FCNs), have shown outstanding performances in this task. However, the map accuracy depends on the quantity and quality of ground truth (GT) used to train them. At the same time, probabilistic graphical models (PGMs) have sparked even more interest in the past few years, because of the ever-growing need for structured predictions. The novel method proposed in this paper combines DL and PGMs to perform remote sensing image classification. FCNs can be exploited to deal with multiscale data through the integration with a hierarchical Markov model. The marginal posterior mode (MPM) criterion for inference is used in the proposed framework. Experimental validation is conducted on the ISPRS 2D Semantic Labeling Challenge Vaihingen dataset. The results are significant, as the proposed method has a higher recall than the standard FCNs considered and allows mitigating the impact of incomplete or suboptimal GT, especially with regard to the discrimination of minoritary classes

Semantic Segmentation of Sar Images Through Fully Convolutional Networks and Hierarchical Probabilistic Graphical Models

International audienceThis paper addresses the semantic segmentation of synthetic aperture radar (SAR) images through the combination of fully convolutional networks (FCNs), hierarchical probabilistic graphical models (PGMs), and decision tree ensembles. The idea is to incorporate long-range spatial information together with the multiresolution information extracted by FCNs, through the multiresolution graph topology on which hierarchical PGMs can be efficiently formulated. The objective is to obtain accurate classification results with small datasets and reduce problems of spatial inconsistency. The experimental validation is conducted with several COSMO-SkyMed satellite images over Northern Italy. The results are significant, as the proposed method obtains more accurate classification results than the standard FCNs considered

Acid sensing ion channel 2: A new potential player in the pathophysiology of multiple sclerosis

Acid-sensing ion channels (ASICs) are proton-gated channels involved in multiple biological functions such as: pain modulation, mechanosensation, neurotransmission, and neurodegeneration. Earlier, we described the genetic association, within the Nuoro population, between Multiple Sclerosis (MS) and rs28936, located in ASIC2 3′UTR. Here we investigated the potential involvement of ASIC2 in MS inflammatory process. We induced experimental autoimmune encephalomyelitis (EAE) in wild-type (WT), knockout Asic1 −/− and Asic2 −/− mice and observed a significant reduction of clinical score in Asic1 −/− mice and a significant reduction in the clinical score in Asic2 −/− mice in a limited time window (i.e., at days 20–23 after immunization). Immunohistochemistry confirmed the reduction in adaptive immune cell infiltrates in the spinal cord of EAE Asic1 −/− mice. Analysis of mechanical allodynia, showed a significant higher pain threshold in Asic2 −/− mice under physiological conditions, before immunization, as compared to WT mice and Asic1 −/−. A significant reduction in pain threshold was observed in all three strains of mice after immunization. More importantly, analysis of human autoptic brain tissue in MS and control samples showed an increase of ASIC2 mRNA in MS samples. Subsequently, in vitro luciferase reporter gene assays, showed that ASIC2 expression is under possible miRNA regulation, in a rs28936 allele-specific manner. Taken together, these findings suggest a potential role of ASIC2 in the pathophysiology of MS

Psychopathological Impact in Patients with History of Rheumatic Fever with or without Sydenham's Chorea: A Multicenter Prospective Study

Sydenham's chorea (SC) is a post-streptococcal autoimmune disorder of the central nervous system, and it is a major criterium for the diagnosis of acute rheumatic fever (ARF). SC typically improves in 12-15 weeks, but patients can be affected for years by persistence and recurrencies of both neurological and neuropsychiatric symptoms. We enrolled 48 patients with a previous diagnosis of ARF, with or without SC, in a national multicenter prospective study, to evaluate the presence of neuropsychiatric symptoms several years after SC's onset. Our population was divided in a SC group (n = 21), consisting of patients who had SC, and a nSC group (n = 27), consisting of patients who had ARF without SC. Both groups were evaluated by the administration of 8 different neuropsychiatric tests. The Work and Social Adjustment Scale (WSAS) showed significantly (p = 0.021) higher alterations in the SC group than in the nSC group. Furthermore, 60.4% (n = 29) of the overall population experienced neuropsychiatric symptoms other than choreic movements at diagnosis and this finding was significantly more common (p = 0.00) in SC patients (95.2%) than in nSC patients (33.3%). The other neuropsychiatric tests also produced significant results, indicating that SC can exert a strong psychopathological impact on patients even years after its onset

Modulation of Antioxidant Defense in Farmed Rainbow Trout (Oncorhynchus mykiss) Fed with a Diet Supplemented by the Waste Derived from the Supercritical Fluid Extraction of Basil (Ocimum basilicum)

Phytotherapy is based on the use of plants to prevent or treat human and animal diseases. Recently, the use of essential oils and polyphenol-enriched extracts is also rapidly increasing in the aquaculture sector as a means of greater industrial and environmental sustainability. Previous studies assessed the antibacterial and antiparasitic effects of these bioactive compounds on fish. However, studies on the modulation of oxidative stress biomarkers are still scant to date. Thus, in this study, the modulation of antioxidant defense against oxidative stress exerted by fish diets supplemented with a basil supercritical extract (F1-BEO) was assessed in rainbow trout Oncorhynchus mykiss. The F1-BEO extracted with supercritical fluid extraction was added to the commercial feed flour (0.5, 1, 2, 3% w/w) and mixed with fish oil to obtain a suitable compound for pellet preparation. Fish were fed for 30 days. The levels of stress biomarkers such as superoxide dismutase, catalase, glutathione peroxidase, glutathione S-transferase, glutathione reductase, glyoxalase I, glyoxalase II, lactate dehydrogenase, glutathione and malondialdehyde showed a boost in the antioxidant pathway in fish fed with a 0.5% F1-BEO-supplemented diet. Higher F1-BEO supplementation led to a failure of activity of several enzymes and the depletion of glutathione levels. Malondialdehyde concentration suggests a sufficient oxidative stress defense against lipid peroxidation in all experimental groups, except for a 3% F1-BEO-supplemented diet (liver 168.87 ± 38.79 nmol/mg prot; kidney 146.86 ± 23.28 nmol/mg prot), compared to control (liver 127.76 ± 18.15 nmol/mg prot; kidney 98.68 ± 15.65 nmol/mg prot). Our results suggest supplementing F1-BEO in fish diets up to 0.5% to avoid potential oxidative pressure in farmed trout.This research was funded by Italian Ministry of Health, Ricerca Finalizzata, grant number GR-2013-02355796.Peer reviewe