Embedding graphs in continuous spaces is a key factor for automatic information extraction in diverse tasks (e.g., learning, inferring, predicting). The reliability of graph embeddings directly depends on how much the geometry of the manifold in continuous space matches the graph structure. State-of-the-art of manifold-based graph embedding algorithms assume that the projection on a tangential space of each point in the manifold (corresponding to a node in the graph) would locally resemble a Euclidean space. Although this condition helps in achieving efficient analytical solutions to the embedding problem, it is not an adequate set-up to work with modern real life graphs, that are characterized by weighted connections across nodes often computed over sparse datasets with missing records. In this work, we introduce a new class of manifold, named soft manifold, that can solve this situation. Soft manifolds are mathematical structures with spherical symmetry where the tangent spaces to each point are hypocycloids whose shape is defined according to the velocity of information propagation across the data points. Experimental results on reconstruction tasks on synthetic and real datasets show how the proposed approach enable more accurate and reliable characterization of graphs in continuous spaces with respect to the state-of-the-art.

Synthetic Aperture Radar (SAR) images offer crucial information for studying and monitoring sea ice in the Arctic. Sentinel-1 captures images of the area using an extremely wide swath for reduced revisit time. The backscattered signal from sea ice and open water is often very weak, making it difficult to distinguish from the sensor thermal noise floor. Thermal noise impacts the images by generating a bias and increasing the fluctuations related to speckle phenomenon. Analyzing these images requires both correcting this bias and reducing fluctuations without blurring out the image content. The acquisition of several sub-swaths in a single pass using Terrain Observation with Progressive Scans (TOPS) produces images that exhibit, after compensation for antenna gains, a non-uniform thermal noise floor and strong discontinuities between sub-swaths. Denoising techniques must take these specificities into account to restore the images. This paper introduces a joint approach to remove the thermal noise offset and suppress fluctuations due to speckle and thermal noise. Compensating at once for all these effects largely reduces artifacts at the boundary between sub-swaths. We demonstrate using both numerical simulations and actual Sentinel-1 images that debiased polarimetric reflectivities can be recovered and fluctuations strongly reduced while preserving fine spatial structures.

Image completion refers to the problem of recovering the missing, corrupted or obscured entries in image data. In this paper, we consider the problem in the remote sensing domain, where regions of an image are missing due to difficulties such as cloud cover, sensor failures or partial sensor coverage. Where previous work in this field generally falls into the category of low-rank completion methods, we propose a novel graph-based diffusion approach to the problem. The method, referred to as GraphProp, propagates observed entries around a graph-based representation of the image region in order to recover the missing entries. The graph-based diffusion approach to completion is to the best of our knowledge a novel method for remote sensing image completion. Using real-world multispectral image data acquired from the Landsat 7 platform, we validate our approach using experiments which synthetically obscure image sections. In these tests, we benchmark against alternative image completion approaches and demonstrate the superior reconstruction performance of our method versus the state of the art. Code which implements the method has been made publicly available at https://github.com/iainrolland/GraphProp.

Object Detection is a fundamental procedure in the interpretation of remote sensing images. In large-scale remote sensing images, it is common to observe that the interesting objects only occupy a small area. Such objects provide limited information gain and exhibit unclear edges, often named as small objects. The inherent characteristics of small objects significantly hinder the precise localization and accurate classification of deep object detection networks. In this paper, we introduce a significant challenge: the presence of similar objects among these small objects, which leads to dramatic misclassification and overall accuracy decrease. To assess this phenomenon, we propose a novel metric, Similar Category Angle (SCA), for classification discrimination, which serves to intuitively describe the network’s effectiveness in discriminating similar category objects in its final predictions. We also propose a one-stage object detection network named Similar Category Enhancement Network (SCENet), designed to tackle the challenges associated with discriminating similar objects in small object detection tasks. Specifically, we design SCA Loss guided by the SCA metric, which integrates SCA into the network training process, thereby enhances the network’s capability to discriminate between similar category objects. Meanwhile, we propose Laplacian Sobel Enhancement FPN, LSE-FPN, a module that incorporates dynamic edge extraction operators into the FPN to enhance the network’s ability to detect small objects by sharpening the explicit edges of objects in the feature map. Extensive experiments conducted on SODA-A, VisDrone2019 and FAIR1M-AIR datasets demonstrate the superiority of SCENet in the small object detection task, with significant improvements in detection results for both the mAP50 and SCA metrics. The code is available at https://github.com/weiziji01/SCENet.

Classic graph neural networks show some limitations in information extraction performance when applied to multimodal datasets. This is primarily due to such datasets having high volume, variety, and variability. In this paper, we propose structuring graph neural networks on a new graph representation based on fluid dynamics diffusion that allows us to incorporate the reliability of the features used to characterise each sample within the graph structure itself. This approach aims to address some of the major limitations of the classic graph-based learning structures, so to improve accuracy and robustness of the estimates. We show how this approach can help to strongly improve the quality of the analysis of classic graph neural networks. Experimental results are reported to support this point.

Present and future sensors are diversifying from traditional quad polarimetric mode of synthetic aperture radar acquisition. Thus, an approach that is interpretative in nature and applicable across polarimetric modes is required. In this context, the geodesic distance (GD)-based approach within the polarimetric synthetic aperture radar (PolSAR) literature is seen as an eigenvalue-decomposition free approach to interpret and analyze quad PolSAR data. This approach is highly adaptive toward applications due to its ability to compare the SAR observation with a known scatterer/model, or with another SAR observation in general providing a means for direct interpretation. In this work, we show that the GD (originally defined for the quad polarization mode) is generalizable across any arbitrary SAR polarimetric mode while retaining its simple form for ready computation. We show that the GD-based approach provides level ground for comparison of different polarimetric modes given a fixed application. We demonstrate it using change detection as the chosen application. In addition, we show how the behavior of the three roll-invariant GD-based parameters change under different polarimetric modes (e.g., quad, dual, and compact polarization modes). We also discuss how the GD-based approach can also be adapted to ground range detected (GRD) product data, which is presently available from Sentinel-1 and widely used in many applications. However, in this case, we show that only one of the three GD-derived parameters can be defined. We believe this work will make the GD-based approach important for present and future PolSAR applications cutting across sensors and its available polarimetric modes.

Sustainable development poses a good way for the world of Arctic, especially the temperature and vegetation has been experiencing a rising trend in the north land area. The research aims to understand the impacts of the gas flaring to the local environment through the time series analysis of vegetation changes around the gas flaring sites, and natural development sites of from 2000 to 2021 over Russia Siberia regions, the Timan-Pechora basin and West Siberia basin oil/gas rich area. The results shows that gas flaring sites has a profound to the vegetation changes at a statistical level, with the temperature analysis, obviously the flaring shows the positive contribution to the regional environment. The result indicates that the gas flaring.

Remote sensing can provide vital information in the aftermath of a disaster, and can be used by search and rescue teams to strategically deploy resources. However, there is little work on methods which address their operational requirement. This paper presents two methods: the first aims to deploy fast, robust information that can evolve its output as information becomes available; the second focuses on providing an estimation of the degree of confidence for the outcomes of a classification performed by a graph convolutional network. These methods are tested by using data collected following the Beirut blast in 2020.

Change detection (CD) aims to distinguish surface changes based on bitemporal images. Since high-resolution (HR) images cannot be typically acquired continuously over time, bitemporal images with different resolutions are often adopted for CD in practical applications. Traditional subpixel-based methods for CD using images with different resolutions may lead to substantial error accumulation when the HR images are employed, which is because of intraclass heterogeneity and interclass similarity. Therefore, it is necessary to develop a novel method for CD using images with different resolutions that are more suitable for the HR images. To this end, we propose a super-resolution-based change detection network (SRCDNet) with a stacked attention module (SAM). The SRCDNet employs a super-resolution (SR) module containing a generator and a discriminator to directly learn the SR images through adversarial learning and overcome the resolution difference between the bitemporal images. To enhance the useful information in multiscale features, a SAM consisting of five convolutional block attention modules (CBAMs) is integrated to the feature extractor. The final change map is obtained through a metric learning-based change decision module, wherein a distance map between bitemporal features is calculated. Ablation study and comparative experiments on two large datasets, building change detection dataset (BCDD) and season-varying change detection dataset (CDD), and a real-image experiment on the Google dataset fully demonstrate the superiority of the proposed method. The source code of SRCDNet is available athttps://github.com/liumency/SRCDNet.

The vast amount of spectral information provided by hyperspectral images can be useful for different applications. However, the presence of redundant bands will negatively affect application performance. Therefore, it is crucial to select a relevant subset that preserves the information of the original set. In this paper, we present an automatic and accurate band selection method based on Graph Laplacians. Unlike existing band selection methods, this method exploits two similarity measures simultaneously. Furthermore, it is performed on a superpixel level, so it allows us to preserve not only global but contemporaneously local particularities of original data. Experiments show the importance of measuring the relevance of the bands at local and global scales and the ability of the method to minimize intercorrelation among selected bands, hence improving the selection of the most informative spectral channels.

The recent advancements in machine learning techniques have opened the door for automatic large scale monitoring of the surface of the earth. For instance, they could be used in order to evaluate and assess civil infrastructures at scale, which is costly due to the fact that typically the existing methods rely on in-situ evaluation. Over the last decade Deep Learning technologies have risen as the state of the art methods for many different machine learning problems due to the fact that they can learn complex features and model complex non-linear behaviours. In this paper we will explore the possibility of using Deep Learning technologies over remote sensing data with the aim of structure health monitoring at scale. We will compare the performance of new Deep Learning technologies with regards to other traditional machine learning methods. For this purpose, we will use InSAR (Interferometry Synthetic Aperture Radar) data which allow us to measure cumulative surface displacement in the line of sight of the sensor with millimetric accuracy. We will analyse multi temporal InSAR data in order to model ground subsidence. In this paper we will discuss how deep learning technologies can learn to detect terrain subsidence over multi-temporal InSAR data automatically, providing much better results than traditional methods.

The additive system noise in synthetic aperture radar (SAR) imagery is a challenging problem for the operational use of SAR data for sea ice classification. This noise degrades the performance of the sea ice classification models. The most common way of dealing with this is to remove mean noise profiles from the backscatter intensities as a preprocessing step. In this study we investigate how including the nominal noise profiles as a feature directly into the model affects the classification. Our noise-aware approach can be used in conjunction with any other deep learning model for sea ice classification. Hence our findings pave the way for getting refined and smoother sea ice maps for ice charting. For experimentally evaluating our proposed approach, we train our noise-aware deep model using carefully labeled data consisting of both sea ice data and noise profile. We present validation results considering separate validation data. Our empirical study confirms the superior performance of the CNN model driven by noise-aware characteristics.

Although multimodal remote sensing data analysis can strongly improve the characterization of physical phenomena on Earth's surface, nonidealities and estimation imperfections between records and investigation models can limit its actual information extraction ability. In this article, we aim at predicting the maximum information extraction that can be reached when analyzing a given data set. By means of an asymptotic information theory-based approach, we investigate the reliability and accuracy that can be achieved under optimal conditions for multimodal analysis as a function of data statistics and parameters that characterize the multimodal scenario to be addressed. Our approach leads to the definition of two indices that can be easily computed before the actual processing takes place. Moreover, we report in this article how they can be used for operational use in terms of image selection in order to maximize the robustness of the multimodal analysis, as well as to properly design data collection campaigns for understanding and quantifying physical phenomena. Experimental results show the consistency of our approach.

In literature, we find many examples showing that, contrary to what one might think, multimodal remote sensing analysis might be suboptimal. Given the high computational complexity typically required by multimodal investigation in order to properly extract information from multiple sources, there is a need to assess its actual benefit during image preprocessing. This urgency becomes indeed crucial when targeting transfer learning in remote sensing, as understanding the actual relationship between diverse sensors is fundamental to accurately characterize the considered scenes. In this work, we derive a reliability metric by means of an information theory-based approach. The proposed metric is able to estimate how confident one can be of the considered datasets when characterizing each pixel in the considered region of interest. Experimental results on real datasets show how this quantity can be used to improve the understanding of the scenes, and to enhance multisensor transfer learning.

Although deep learning architectures are nowadays used in several research fields where automatized investigation of large scale datasets is required, the intrinsic mechanisms of deep learning networks are not fully understood yet. In this paper, a new approach for characterizing how information is processed within convolutional neural networks (CNNs) is introduced. Taking advantage of an analysis based on information theory, we are able to derive an index that is associated with the degree of maximum information extraction a CNN can obtain under ideal circumstances as a function of its hyperparameters setup and of the data to be explored. Experimental results on remote sensing datasets show the robustness of our approach. The outcomes of our analysis can be used to optimize the design of CNNs and maximize the information that can be obtained for the considered problem.

This article introduces the Rayleigh autoregressive moving average (RARMA) model, which is useful to interpret multiple different sets of remotely sensed data, from wind measurements to multitemporal synthetic aperture radar (SAR) sequences. The RARMA model is indeed suitable for continuous, asymmetric, and nonnegative signals observed over time. It describes the mean of Rayleigh-distributed discrete-time signals by a dynamic structure including autoregressive (AR) and moving average (MA) terms, a set of regressors, and a link function. After presenting the conditional likelihood inference for the model parameters and the detection theory, in this article, a Monte Carlo simulation is performed to evaluate the finite signal length performance of the conditional likelihood inferences. Finally, the new model is applied first to sequences of wind speed measurements, and then to a multitemporal SAR image stack for land-use classification purposes. The results in these two test cases illustrate the usefulness of this novel dynamic model for remote sensing data interpretation.

This article examines advances in interferometric synthetic aperture radar (InSAR) satellite measurement technologies to understand their relevance, utilization, and limitations for bridge monitoring. Waterloo Bridge is presented as a case study to explore how InSAR data sets can be combined with traditional measurement techniques including sensors installed on the bridge and automated total stations. A novel approach to InSAR bridge monitoring was adopted by the installation of physical reflectors at key points of structural interest on the bridge, in order to supplement the bridge's own reflection characteristics and ensure that the InSAR measurements could be directly compared and combined with in situ measurements. The interpretation and integration of InSAR data sets with civil infrastructure data are more than a trivial task, and a discussion of uncertainty of measurement data is presented. Finally, a strategy for combining and interpreting varied data from multiple sources to provide useful insights into each of these methods is presented, outlining the practical applications of this data analysis to support wider monitoring strategies.

In this paper, we introduce a new approach for investigation of polarimetric Synthetic Aperture Radar (PolSAR) images for oil slick analysis. Our method aims at enhancing discrimination of oil types by exploring the polarimetric features that can be produced by processing PolSAR scenes without dimensionality reduction. Taking advantage of a mixture description of the interactions among classes within the dataset and a characterization of their intra- and inter-class variability, our algorithm is able to quantify the areal coverage of different elements. These estimates can be used to hence improve classification. Experimental results on a PolSAR dataset acquired by unmanned aerial vehicle (UAV) on oil slicks in open water show the capacity of our method.

The data acquired by the Inference Imaging Spectrometer (IIM) sensor on board of the Chinese Chang'E-1 mission can be used to infer important information on the Moon surface composition. In this work, the multi-path and multi-reflection phenomena occurring on its rugged surface recorded at the IIM rather coarse resolution (200m) are described by means of nonlinear spectral analysis based on the p-linear mixture model (pLMM) and the p-harmonic mixture model (pHMM). The analysis by pLMM and pHMM provides details on the materials and elements on the Moon surface, and their abundance distribution and fractional cover can be properly estimated without any a priori information on its chemical composition. Mineral map extractions using pLMM and pHMM have been considered and compared with those obtained by means of the modified partial least squares regression (PLSR) methodology, assessing the reliability and accuracy of the pLMM- and pHMM-based approach.

Spectral unmixing (SU) is a useful tool for hyperspectral remote sensing image analysis. However, due to the interference of spectral variance and non‐linearity caused by photon multiple‐scattering, the result might be an inaccuracy. In addition, the unmixing performance of typically relies on the prior knowledge of endmembers. Although many classical endmember extraction algorithms have been presented, it is hard to obtain accurate endmembers in practical applications. This study presents a bilinear normal mixing model named as BNMM to tackle these issues. In fact, BNMM employs the polynomial post‐non‐linear mixing model to alleviate the effect of non‐linearity and uses a normal distribution model to reduce the influence of endmembers variability. Based on the BNMM, the authors develop a Hamiltonian Monte Carlo algorithm for SU. The experimental results demonstrate that the proposed algorithm outperforms other classical unmixing algorithms in the case of simulated and benchmark datasets.

Nonlinear unmixing algorithms are playing a key role in modern earth observation analysis thanks to their ability to characterize complex phenomena occurring in the instantaneous field of view. When unmixing hyperspectral images according to nonlinear mixture models by means of state-of-the-art methods, actual abundances of the elements in the scene can be only indirectly estimated. Thus, the reliability of the investigation can be dramatically jeopardized, hence degrading the accuracy of the characterization of the surface composition. In order to overcome this issue, we propose in this paper a nonlinear programming scheme that aims at providing direct estimation of the end members fractions. The method we introduce is based on a structural optimization approach where the abundances are directly assessed, so that no epistemic uncertainties are injected in the framework. Experimental results show that the proposed method is able to deliver accurate and reliable estimates of these quantities in hyperspectral images.

Spectral unmixing is a technique for remotely sensed image interpretation that expresses each (possibly mixed) pixel as a combination of pure spectral signatures (endmembers) and their fractional abundances. In this paper, we develop a new technique for unsupervised unmixing which is based on a deep autoencoder network (DAEN). Our newly developed DAEN consists of two parts. The first part of the network adopts stacked autoencoders (SAEs) to learn spectral signatures, so as to generate a good initialization for the unmixing process. In the second part of the network, a variational autoencoder (VAE) is employed to perform blind source separation, aimed at obtaining the endmember signatures and abundance fractions simultaneously. By taking advantage from the SAEs, the robustness of the proposed approach is remarkable as it can unmix data sets with outliers and low signal-to-noise ratio. Moreover, the multihidden layers of the VAE ensure the required constraints (nonnegativity and sum-to-one) when estimating the abundances. The effectiveness of the proposed method is evaluated using both synthetic and real hyperspectral data. When compared with other unmixing methods, the proposed approach demonstrates very competitive performance.

Accurately estimating the urbanization process is a key-factor for the actual implementation of the sustainable development goals identified by transnational institutions and agencies. In order to retrieve precise characterization of the anthropogenic extents and a sound human-environment interaction assessment, the analysis of Earth observations (EOs) plays a crucial role. Especially, the use of nonlinear spectral investigation can improve the description of geometrically and morphologically complex scenes, so that anthropogenic settlements and dynamics can be properly outlined. In this paper, we propose a novel method for directly assessing the distribution of materials and elements in hyperspectral images by means of a structural optimization approach. Experimental results show how the proposed approach is able to deliver accurate and reliable characterization of urban materials and extents.

Air quality is strongly affecting human lifestyle all over the world, and its impact is apparent on healthcare, sustainable development, welfare and public administration policies. Accurate understanding of the polluting processes requires to analyze huge volumes of records, so that significant patterns and regularities can be detected. In this paper, we introduce a framework to explore the air pollution dynamics over all Europe by means of a data driven feature extraction approach. Taking advantage of MODIS records, we are able to investigate daily trends of air quality from 2003 to 2016. By means of an automatic learning scheme based on mutual information maximization, we extract the most significant patterns in the dataset. Experimental results show that the proposed approach is able to identify relevant air pollution trends that can be associated with specific physical phenomena on ground.

In this paper, we propose a deep auto-encoder network for the unmixing for hyperspectral data with outliers and low signal to noise ratio. The proposed deep auto-encoder network composes of two parts. The first part of the network adopts stacked non-negative sparse auto-encoder to learn the spectral signatures such that to generate a good initialization for the network. In the second part of the network, a variational auto-encoder is employed to perform unmixing, aiming at the endmember signatures and abundance fractions. The effectiveness of the proposed method is verified by using a synthetic data set. In our comparison with other state-of-the-art unmixing methods, the proposed approach demonstrates highly competitive performance.

As an unsupervised learning tool, autoencoder has been widely applied in many fields. In this letter, we propose a new robust unmixing algorithm that is based on stacked nonnegative sparse autoencoders (NNSAEs) for hyperspectral data with outliers and low signal-to-noise ratio. The proposed stacked autoencoders network contains two main steps. In the first step, a series of NNSAE is used to detect the outliers in the data. In the second step, a final autoencoder is performed for unmixing to achieve the endmember signatures and abundance fractions. By taking advantage from nonnegative sparse autoencoding, the proposed approach can well tackle problems with outliers and low noise-signal ratio. The effectiveness of the proposed method is evaluated on both synthetic and real hyperspectral data. In comparison with other unmixing methods, the proposed approach demonstrates competitive performance.

In this letter, a new method for higher order nonlinear hyperspectral unmixing is introduced. The proposed scheme relies on the harmonic description of the endmembers contributions to characterize the interactions among the materials showing up in the given scenes. Moreover, it aims at directly estimating the probability of occurrence of each material in the images, so to provide an accurate quantification of the endmembers also in complex scenarios. Experimental results carried out on synthetic and real data sets show that the proposed method is able to obtain good unmixing performance when compared to other state-of-the-art architectures.

Accurately estimating the elements in Earth observations is crucial when assessing specific features such as air quality index, water pollution, or urbanization process behavior. Moreover, physical-chemical composition can be retrieved from hyperspectral images when proper spectral unmixing architectures are employed. Specifically, when linear and nonlinear combinations of endmembers (pure spectral components) are accurately characterized, hyperspectral unmixing plays a key role in understanding and quantifying phenomena occurring over the instantaneous field-of-view. Thus, reliable detection of nonlinear reflectance behavior can play a key role in enhancing hyperspectral unmixing performance. In this paper, two new methods for adaptive design of mixture models for hyperspectral unmixing are introduced. One of the methods relies on exploiting geometrical features of hyperspectral signatures in terms of nonorthogonal projections onto the space induced by the endmembers' spectra. Then, an iterative process aims at understanding the order of local nonlinearity that is displayed by each endmember over every pixel. An improved version of an artificial neural network-based approach for nonlinearity order information is also considered and compared. Experimental results show that the proposed approaches are actually able to retrieve thorough information on the nature of the nonlinear effects over the image, while providing excellent performance in reconstructing the given data sets.

Hyperspectral unmixing frameworks are ultimately designed to understand and quantify the actual distribution of endmembers in a given scene. Assessing the percentage of each material is typically cumbersome, especially in images characterized by complex combinations of spectral signatures. In this work, we present a nonlinear programming scheme that aims at providing direct estimation of the endmembers fractions. Experimental results show that the proposed method is able to deliver accurate and reliable estimates of these quantities in hyperspectral images.

Endmember extraction is a fundamental task in spectral unmixing of remotely sensed hyperspectral images. In this work, we develop a new robust algorithm for endmember extraction which is based on a nonnegative sparse autoencoder. The proposed approach is based on two main steps. First, it uses an automatic sampler approach with local outlier factor and affinity propagation to intelligently gather a set of training samples. Then, a set of endmember signatures are extracted from the selected training samples by the nonnegative sparse autoencoder. Taking advantage from both automatic sampling and nonnegative sparse autoencoding, the proposed method can tackle problems with outliers. The effectiveness of the proposed method is verified by using simulated data. In our comparison with other state-of-the-art endmember extraction methods, the proposed approach demonstrates highly competitive performance.

This paper proposes a new supervised hyperspectral nonlinear unmixing method based on normalization. The main contribution is presented by reducing the overfitting of model and taking account to spatial correlation, using the normalization. The l2-norm constraints of abundance and nonlinear coefficient are added to the P-Linear spectral mixing model. Moreover different positive parameters are given to control the trade-off between regularity and fitting. Finally, the problem can be expressed as a convex optimization problem, minimizing the cost function and the global optimum can be determined. The proposed method, abbreviated as NPLA (Normalized P-Linear Algorithm), is validated using hyperspectral synthetic and real datasets. The results indicate that the proposed method exhibits better performance on RMSE of abundance, reconstruction error and computed cost compared to other related classical hyperspectral nonlinear unmixing methods.

Information theory has recently become an interesting topic in earth observation data management and analysis, since it can provide important information on hidden interactions and correlations among the considered data records. Although several methods have been proposed and implemented to efficiently extract a proper set of features and deliver accurate image investigation, classification, and segmentation, these architectures show drawbacks when the data sets are characterized by complex interactions among the samples. In this paper, a new approach based on information theory for automatic pattern recognition is introduced for accurate classification of remotely sensed data. Experimental results carried out on real data sets show the validity of the proposed approach.

In order to provide reliable information about the instantaneous field of view considered in hyperspectral images through spectral unmixing, understanding the kind of mixture that occurs over each pixel plays a crucial role. In this paper, in order to detect nonlinear mixtures, a method for fast identification of linear mixtures is introduced. The proposed method does not need statistical information and performs an a priori test on the spectral linearity of each pixel. It uses standard least squares optimization to achieve estimates of the likelihood of occurrence of linear combinations of endmembers by taking advantage of the geometrical properties of hyperspectral signatures. Experimental results on both real and synthetic data sets show that the aforesaid algorithm is actually able to deliver a reliable and thorough assessment of the kind of mixtures present in the pixels of the scene.

In order to provide reliable information on the instantaneous field-of-view considered in hyperspectral images through spectral unmixing, understanding the kind of mixture that occurs over each pixel plays a crucial role. In this paper, a new method for fast detection of linear mixtures is introduced. The proposed method does not need statistical information and performs an a priori test on the spectral linearity of each pixel. It uses standard least squares optimization to achieve estimates of the likelihood of occurrence of linear combinations of endmembers by taking advantage of geometrical properties of hyperspectral signatures. Experimental results on synthetic datasets show how the aforesaid algorithm is actually able to deliver a reliable and thorough assessment of the kind of mix on the scene.

Recent developments in data acquisition, storage, mining and maintenance have allowed the flourishing of several multi-disciplinary research fields, which can be stated, defined and carried out according to the so-called Big Data paradigm. In this environment, the investigation and analysis of interactions between human phenomena and natural events play a key-role, as they can be fundamental for several applications, from sustainable development to community policy design and short-, medium- and long-range resource allocation planning. In this paper, we provide a study of the interplay between air pollution (as estimated by remotely sensed data processing) and clinical records, so that inferences and correlations among black particulate concentration, micro- and macro-vascular disease onsets and hospitalization tracks can be efficiently drawn. We focused on the second order administrative area of the city of Pavia, Italy, on 2013. Experimental results show how effective connections between the estimated air quality and the hospitalizations behavior can be accurately drawn and derived.

As remotely sensed Big Data applications in astrophysics research have been flourishing in the last decade, the need for a new class of techniques and methods for efficient storage, compression, retrieval and investigation of astronomical datasets has become urgent. In this paper, a novel strategy for lossless compression of large datasets composed by remote sensing records is introduced. Specifically, the new approach aims at describing each sample of the given dataset as a point living within a convex hull in a multidimensional space. Thus, the proposed framework aims at characterizing every sample as a nonlinear combination of the extremal points of the aforesaid multidimensional simplex. Therefore, efficient compression can be achieved by describing those samples by the parameters that drive the nonlinear mixture only. Experimental results show how the proposed architecture can effectively deliver great compression performance for both Earth observations and planetary records.

This paper explores protograph-based and ACE-based methods for constructing q-ary low-density parity-check (LDPC) matrices. The ACE approach maximizes approximate cycle extrinsic message degree, explicitly avoiding small q-ary stopping sets and implicitly avoiding small absorbing sets. In addition to ACE, this paper applies linear-dependent-set maximization (LDSM) to the binary image of the q-ary LDPC matrix. Performance is studied for binary and q-ary instances of erasure channels and additive white Gaussian noise channels. The combination of the ACE approach and LDSM provides dramatic error floor improvement for the binary erasure channel and both binary and q-ary AWGN channels.

Recently q-ary Low-Density Parity-Check (LDPC) codes have been used to achieve performance close to the channel capacity in different channel environments, from satellite communications to magnetic data storage systems. Design of receivers employing these codes for transmissions over channels affected by InterSymbol Interference (ISI) is still an open issue. In fact, detection-and-decoding systems have to face the trade-off between error-rate performance and complexity. In this paper we compare some receiver architectures for 16-ary LDPC codes: serial and turbo concatenated schemes and a joint Message-Passing (MP) based receiver as well. Performance of these systems are evaluated over three different Partial Response (PR) channels, using simulations. Finally, ongoing future directions for research are discussed.