H.264 sensor aided video encoder for UAV BLOS missions

This paper presents a new low-complexity H.264 encoder, based on x264 implementation, for Unmanned Aerial Vehicles (UAV) applications. The encoder employs a new motion estimation scheme which make use of the global motion information provided by the onboard navigation system. The results are relevant in low frame rate video coding, which is a typical scenario in UAV behind line-of-sight (BLOS) missions

Restauration d'image fondée sur la théorie de l'information

This thesis addresses informational formulation of image processing problems. This formulation expresses the solution through a minimization of an information-based energy. These energies belong to the nonparametric class in that they do not make any parametric assumption on the underlying data distribution. Energies are expressed directly as a function of the data considered as random variables. However, classical nonparametric estimation relies on fixed-size kernels which becomes less reliable when dealing with high dimensional data. Actually, recent trends in image processing rely on patch-based approaches which deal with vectors describing local patterns of natural images, e. g., local pixel neighbor- hoods. The k-Nearest Neighbors framework solves these difficulties by locally adapting the data distribution in such high dimensional spaces. Based on these premises, we develop new algorithms tackling mainly two problems of image processing: deconvolution and denoising. The problem of denoising is developed in the additive white Gaussian noise (AWGN) hypothesis and successively adapted to no AWGN realm such as digital photography and SAR despeckling. The denoising scheme is also modified to propose an inpainting algorithm.Cette thèse aborde la formulation par la théorie de l’information des problèmes de traitement d’image. Cette formulation exprime la solution au travers de la minimisation d’une énergie. Ces énergies appartiennent à la classe non paramétrique au sens où elles ne font aucune hypothèse paramétrique sur la distribution des données. Les énergies sont exprimées directement en fonction des données considérées comme des variables aléatoires. Toutefois, l’estimation non paramétrique classique repose sur des noyaux de taille fixe moins fiables lorsqu’il s’agit de données de grande dimension. En particulier, des méthodes récentes dans le traitement de l’image dépendent des données de type ”patch” correspondant à des vecteurs de description de modèles locaux des images naturelles, par exemple, les voisinages de pixels. Le cadre des k-plus proches voisins résout ces difficultés en s’adaptant localement à la distribution des données dans ces espaces de grande dimension. Sur la base de ces prémisses, nous développons de nouveaux algorithmes qui s’attaquent principalement à deux problèmes du traitement de l’image : la déconvolution et le débruitage. Le problème de la restauration est développé dans les hypothèses d’un bruit blanc gaussien additif puis successivement adaptés à domaines tels que la photographie numérique et le débruitage d’image radar (SAR). Le schéma du débruitage est également modifié pour définir un algorithme d’inpainting

A MINIMUM ENTROPY IMAGE DENOISING ALGORITHM Minimizing Conditional Entropy in a New Adaptive Weighted K-th Nearest Neighbor Framework for Image Denoising

In this paper we address the image restoration problem in the variational framework. The focus is set on denoising applications. Natural image statistics are consistent with a Markov random field (MRF) model for the image structure. Thus in a restoration process attention must be paid to the spatial correlation between adjacent pixels.The proposed approach minimizes the conditional entropy of a pixel knowing its neighborhood. The estimation procedure of statistical properties of the image is carried out in a new adaptive weighted k-th nearest neighbor (AWkNN) framework. Experimental results show the interest of such an approach. Restoration quality is evaluated by means of the RMSE measure and the SSIM index, more adapted to the human visual system.

Remote sensing in the fight against environmental crimes: The case study of the cattle-breeding facilities in southern Italy

Enforcement of environmental regulation is a persistent challenge and timely detection of the violations is key to holding the violators accountable. The use of remote sensing data is becoming an effective practice in the fight against environmental crimes. In this work, a novel and effective approach for the detection of potentially hazardous cattle-breeding facilities, exploiting both synthetic aperture radar and optical multispectral data together with geospatial analyses in the geographic information system (GIS) environment, is proposed. Experiments on data available for the area of Caserta (Southern Italy), show that the proposed technique provides very high detection capability, up to 90%, with a acceptable false alarm rate, becoming a useful tool in the hand of agencies engaged in the protection of territory

A nonlocal approach for SAR image denoising

Speckle reduction is a key step in several SAR image processing procedures. In this paper, a new despeckling technique based on the “nonlocal” denoising filter BM3D is presented. The filter has been modified in order to take into account SAR image characteristics. The experimental results, conducted on both synthetic and real SAR images, confirm the potential of the proposed approach

Application of DInSAR Technique to High Coherence Sentinel-1 Images for Dam Monitoring and Result Validation Through in Situ Measurements

Dam monitoring represents a crucial issue in order to avoid catastrophic failures due to infrastructure aging or earthquake damages. Differential SAR Interferometry (DInSAR) is a technique suitable for critical infrastructure monitoring, also for the availability of free data and tools, that can be used by experts in SAR remote sensing and also by geologists and civil engineers, after having acquired the right confidence and experience in these data processing and tool use. In order to apply the DInSAR technique, in its basic and simple version, to critical infrastructure monitoring, it is very important to assess its performance. Nevertheless, validation results are not largely available in literature, because heterogeneous technical competencies are required to this aim and in situ measurements must be collected and made available. In this paper, we propose a highly reproducible DInSAR workflow that can be effectively used for dam monitoring, by validating its results with in situ measurements on some significant case studies in Italy