A new class of distributions, G arising from the multiplicative model is presented, along with their main properties and relations. Their densities are derived for complex multilook intensity amplitude data. Classical such as K, particular cases this class. special case called G/sup 0/, that has many parameters K shown able to extremely heterogeneous clutter, urban areas, cannot be properly modeled distributions. One related degree homogeneity, a limiting scaled speckle. The advantage 0/...

Accurate spatial information on Land use and land cover (LULC) plays a crucial role in city planning. A widely used method of obtaining accurate LULC maps is classification the categories, which one challenging problems. Attempts have been made considering spectral (Sp), statistical (St), index-based (Ind) features developing for However, no work has reported to automate performance modeling their robustness with machine learning (ML) algorithms. In this paper, we design seven schemes six ML...

Abstract Remote sensing data, and radar data in particular, have become an essential tool for environmental studies. Many airborne polarimetric sensors are currently operational, many more will be available the near future including spaceborne platforms. The signal‐to‐noise ratio of this kind imagery is lower than that optical information, thus requiring a careful statistical modelling. This modelling may lead to useful or useless techniques image processing analysis, according agreement...

<para xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> Images obtained with coherent illumination, as is the case of sonar, ultrasound-B, laser, and synthetic aperture radar, are affected by speckle noise which reduces ability to extract information from data. Specialized techniques required deal such imagery, has been modeled <formula formulatype="inline"><tex Notation="TeX">${\cal G}^{0}$</tex></formula> distribution and, under which, regions...

This paper presents the use of a new distribution for fully polarimetric image classification. Several classification strategies are compared in order to assess importance careful statistical modeling data and complementary nature information provided by different frequencies. Spatial context, which is relevant obtain good results with noisy data, described means multiclass Potts model, an iterated conditional modes algorithm that employs pseudolikelihood proposed. The using multivariate...

The scaled complex Wishart distribution is a widely used model for multilook full polarimetric synthetic aperture radar data whose adequacy attested in this paper. Classification, segmentation, and image analysis techniques that depend on are devised, many of them employ some type dissimilarity measure. In paper, we derive analytic expressions four stochastic distances between relaxed distributions their most general form important particular cases. Using these distances, inequalities...

A new classifier for Polarimetric SAR (PolSAR) images is proposed and assessed in this paper. Its input consists of segments, each one assigned the class which minimizes a stochastic distance. Assuming complex Wishart model, several distances are obtained from <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">h</i> - φ family divergences, they employed to derive hypothesis test statistics that also used classification process. This article presents,...

A recently proposed methodology called the Horizontal Visibility Graph (HVG) [Luque et al., Phys. Rev. E., 80, 046103 (2009)] that constitutes a geometrical simplification of well known algorithm [Lacasa Proc. Natl. Sci. U.S.A. 105, 4972 (2008)], has been used to study distinction between deterministic and stochastic components in time series [L. Lacasa R. Toral, 82, 036120 (2010)]. Specifically, authors propose node degree distribution these processes follows an exponential functional form...

SAR (Synthetic Aperture Radar) imaging plays a central role in Remote Sensing due to, among other important features, its ability to provide high-resolution, day-and-night and almost weather-independent images. images are affected from granular contamination, speckle, that can be described by multiplicative model. Many despeckling techniques have been proposed the literature, as well measures of quality results they provide. Assuming model, observed image Z is product two independent fields:...

Model-based decompositions have gained considerable attention after the initial work of Freeman and Durden. This decomposition which assumes target to be reflection symmetric was later relaxed in Yamaguchi et al. with addition helix parameter. Since then many been proposed where either scattering model modified fit data or coherency matrix representing second order statistics full polarimetric is rotated model. In this paper we propose modify four-component (Y4O) powers using concept...

In radar polarimetry, incoherent target decomposition techniques help extract scattering information from polarimetric synthetic aperture (SAR) data. This is achieved either by fitting appropriate models or optimizing the received wave intensity through diagonalization of coherency (or covariance) matrix. As such, depends on antenna configuration. Additionally, a descriptor that independent configuration might provide additional which missed individual elements implies existing...

With the rapid development of spaceborne synthetic aperture radar (SAR) technology and acquisition a large volume SAR images, image interpretation has become an urgent difficult research topic. statistical modeling is one theoretical foundations for interpretation. It great value in-depth analysis images. This article reviews major developments since its beginning, including more than 20 distributions eight models, gives their derivations expressions, which can be used as basic reference...

Crop growth monitoring using compact-pol synthetic aperture radar (CP-SAR) data is gaining attention with the rapid advancements toward operational applications. In this article, we propose a vegetation index for compact polarimetric (CP) SAR [compact-pol (CpRVI)]. The CpRVI derived concept of geodesic distance between Kennaugh matrices projected on unit sphere. This utilized to compute similarity measure observed matrix and an ideal depolarizer (a realization canopy). then modulated scaled...

Images obtained from coherent illumination processes are contaminated with speckle. A prominent example of such imagery systems is the polarimetric synthetic aperture radar (PolSAR). For a remote sensing tool, speckle interference pattern appears in form positive-definite Hermitian matrix, which requires specialized models and makes change detection hard task. The scaled complex Wishart distribution widely used model for PolSAR images. Such defined by two parameters: number looks covariance...

Polarimetric synthetic aperture radar (PolSAR) has achieved a prominent position as remote imaging method. However, PolSAR images are contaminated by speckle noise due to the coherent illumination employed during data acquisition. This provides granular aspect image, making its processing and analysis (such in edge detection) hard tasks. paper discusses seven methods for detection multilook images. In all methods, basic idea consists detecting transition points finest possible strip of which...

In this letter, we propose a novel vegetation index from polarimetric synthetic-aperture radar (PolSAR) data using the generalized volume scattering model. The geodesic distance between two Kennaugh matrices projected on unit sphere proposed by Ratha et al. is used in letter. This utilized to compute similarity measure observed matrix and models. A factor estimated corresponding ratio of minimum maximum distances set elementary targets: trihedral, cylinder, dihedral, narrow dihedral. then...

Target decomposition methods from polarimetric Synthetic Aperture Radar (PolSAR) data provides target scattering information. In this regard, several conventional model-based use power components to analyze SAR data. However, the typical hierarchical process enumerate uses various branching conditions, leading limitations. These techniques assume ad hoc models within a radar resolution cell. Therefore, of makes computation powers ambiguous. Some common issues decompositions are related...

We investigate the problem of training an oil spill detection model with small data. Most existing machine-learning-based models rely heavily on big However, amounts observation data are difficult to access in practice. To address this limitation, we developed a multiscale conditional adversarial network (MCAN) consisting series networks at multiple scales. The each scale consists generator and discriminator. aims producing map as authentically possible. discriminator tries its best...

We develop a memory graph convolutional network (MGCN) framework for sea surface temperature (SST) prediction. The MGCN consists of two layers: one layer and output layer. captures SST temporal changes via convolution units gate linear units. encodes spatial in terms characteristics derived from Laplacian. encapsulates information the previous layers produces prediction results. characterizes both changes, rendering comprehensive strategy. use daily mean data areas near Bohai Sea East China...

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...