Deep learning (DL) in remote sensing has nowadays become an effective operative tool: it is largely used applications such as change detection, image restoration, segmentation, detection and classification. With reference to synthetic aperture radar (SAR) domain the application of DL techniques not straightforward due non trivial interpretation SAR images, specially caused by presence speckle. Several deep solutions for despeckling have been proposed last few years. Most these focus on...

Historically, the extraction of coastal line has been performed exploiting optical images, but in last two decades, some approaches working with synthetic aperture radar (SAR) data have proposed. Recently, these gaining interest due to availability high-resolution SAR images. In this letter, a technique for retrieval from multichannel images is presented. The detection problem faced statistical estimation framework, particular, Bayesian theory. proposed able detect sea boundaries at full...

Synthetic-aperture radar (SAR) systems are widely used for monitoring vegetation and forested environments. Those that offer large-scale coverage, short revisiting times, an under-foliage wave-penetration capability have been broadly employed to extract information about the forest structure. Similarly, 3D structures serve as important indicators of productivity biomass levels can be efficiently estimated using SAR tomography (TomoSAR) processing technique through multibaseline (MB) image...

It has been suggested that the practice of meditation is associated to neuroplasticity phenomena, reducing age-related brain degeneration and improving cognitive functions. Neuroimaging studies have shown connectivity changes in meditators. In present work, we aim describe possible long-term effects on networks. To this aim, used magnetoencephalography study functional resting-state networks Vipassana We observed topological modifications network meditators compared controls. More...

In the framework of deep learning for synthetic aperture radar (SAR) speckle reduction, methods presented in literature mainly focus on definition new architectures and cost functions better catching preserving properties a real SAR image. The achieved results are interesting promising but with many left open issues. main critical problem, shared by all methods, is construction training dataset. This due to lack noise-free reference. this work, comparison among different approaches...

Synthetic Aperture Radar (SAR) images are impaired by the presence of speckle. Despite deep interest scholars in last decades, SAR image despeckling is still an open issue. Among different approaches, recently, many Deep Learning (DL) methods have been proposed following both supervised and unsupervised training approaches. There two main challenges within framework: data, cost functions. Our approach builds datasets which varied realistic using a multi-category Generalized Gaussian Coherent...

Interferometric synthetic aperture radar (InSAR) systems are capable of providing an estimate the digital elevation model (DEM) imaged ground scene. This is usually done by means a phase unwrapping (PU) operation. In absence additional regularity constraints, PU ill-posed problem, because solution not unique. Multichannel (MCh) techniques, using stacks images same scene, can be used for restoring uniqueness and reducing effect noise. Moreover, statistical techniques exploiting contextual...

Deep neural networks have demonstrated outstanding performances in agriculture production. Agriculture production is one of the most important sectors because it has a direct impact on economy and social life any society. Plant disease identification big challenge for production, which we need fast accurate technique to identify plant disease. With recent advancement deep learning, can develop robust system. This research investigated use learning tomato identification. In this research,...

Agricultural production is a critical sector that directly impacts the economy and social life of any society. The identification plant disease in real-time environment significant challenge for agriculture production. For accurate detection, precise detection leaves meaningful challenging task developing smart agricultural systems. Most researchers train test models on synthetic images. So, when using model scenario, it does not give satisfactory result because trained images fed with image...

In this paper, the problem of building edge detection in synthetic aperture radar images is addressed. A new stochastic approach based on local Gaussian Markov random field (LGMRF) proposed. The algorithm finds edges buildings starting from estimation hyperparameters LGMRF model. are seen as an indicator spatial correlation between adjacent pixels. procedure applied interferometric data, using single-channel and multichannel configurations. has been tested simulated real providing good...

Markovian approaches have proven to be effective for solving the multichannel phase-unwrapping (PU) problem, particularly when dealing with noisy data and big discontinuities. This letter presents a approach solve PU problem based on new <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">a</i> xmlns:xlink="http://www.w3.org/1999/xlink">priori</i> model, total variation, graph-cut-based optimization algorithms. The proposed method turns out fast,...

Interferometric synthetic aperture radar (SAR) (InSAR) systems allow 3-D reconstruction of observed scene. In this paper, an innovative approach for phase unwrapping and digital elevation model (DEM) generation using multichannel InSAR data is presented. The proposed algorithm, exploiting both the amplitude available complex data, able to unwrap simultaneously regularize data. particular, exploitation within chain helps in preserving sharp discontinuities typical urban areas. As a result,...

The 3-D SAR tomographic technique based on compressive sampling (CS) has been proven very performing in recovering the reflectivity function and hence estimating multiple scatterers lying same range-azimuth resolution cell, but at different elevations. In this paper, a detection method for scatterers, assuming number of to be known or preliminarily estimated, investigated. performance CS processing identifying locating analyzed measurements reciprocal distances between presence off-grid...

In SAR domain many application like classification, detection and segmentation are impaired by speckle. Hence, despeckling of images is the key for scene understanding. Usually filters face trade-off speckle suppression information preservation. last years deep learning solutions reduction have been proposed. One biggest issue these methods how to train a network given lack reference. this work we proposed convolutional neural based solution trained on simulated data. We propose use cost...

In this letter, an innovative technique for change detection in urban areas using very high resolution synthetic aperture radar multichannel stacks is proposed. Instead of the amplitude image, as classical approaches, proposed uses full complex image a Markovian framework. The data are modeled Markov random field hyperparameters, which particular local parameters that take into account spatial correlation between pixels. Starting from two sets, pre- and postevent ones, algorithm, first,...

Within this manuscript a noise filtering technique for magnetic resonance image stack is presented. Magnetic images are usually affected by artifacts and due to several reasons. Several denoising approaches have been proposed in literature, with different trade-off between computational complexity, regularization reduction. Most of them supervised, i.e. requires the set up parameters. A completely unsupervised approach could positive impact on community. The method exploits Markov random...

In recent years, several efforts have been done for producing Magnetic Resonance Image scanner with higher magnetic field strength mainly increasing the Signal to Noise Ratio and Contrast of acquired images. However, denoising methodologies still play an important role achieving images neatness. Several algorithms presented in literature. Some them exploit statistical characteristics involved noise, some others project image a transformed domain, look geometrical properties image. common...

Height reconstruction in urban areas using multibaseline synthetic aperture radar (SAR) images is still a challenging task. Due to the superimposition of scatterers from different elevations same resolution cell, generation digital elevation model not straightforward. Classical SAR interferometry cannot be adopted, while standard tomography (TomoSAR) can fail well identify and separate case that relative contribution scatterer others insignificant. Moreover, irregular low number sampling...

Although the first filtering algorithms have been proposed more than 30 years ago, despeckling of synthetic aperture radar images is still an open issue. A new boost has provided by nonlocal (NL) means filters. The idea NL filters to move from exploitation spatial neighboring pixels similar found across image. difference between mainly related definition similarity and how are exploited in restoration process. Generally, define similarity, patches adopted. In this paper, a criterion for...

Interferometric Synthetic Aperture Radar is an effective and widely adopted tool for earth observation. Based on interferograms it possible to infer several information about the observed area. Two main problems affecting interferogram can limit its application: phase noise wrapping. In this paper attention focused first issue. Several algorithms have been developed restoration. Given wide spread of Deep Learning (DL) in framework image processing, DL based proposed denoising. Most efforts...