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The global increase in wildfires due to climate change highlights the need for accurate wildfire mapping. This study performs a proof of concept on the usefulness of SuperDove imagery for wildfire mapping. To address this topic, we present an automatic methodology that combines the use of various vegetation indices with clustering algorithms (bisecting k-means and k-means) to analyze images before and after fires, with the aim of improving the precision of the burned area and severity assessments. The results demonstrate the potential of using this PlanetScope sensor, showing that the methodology effectively delineates burned areas and classifies them by severity level, in comparison with data from the Copernicus Emergency Management Service (CEMS). Thus, the potential of the SuperDove satellite sensor constellation for fire monitoring is highlighted, despite its limitations regarding radiometric distortion and the absence of Short-Wave Infrared (SWIR) bands, suggesting that the methodology could contribute to better fire management strategies. [ABSTRACT FROM AUTHOR]

Uncontrolled and unsustainable urban sprawl are altering the Earth's surface at unprecedented rates. This research explores the potential of active remote sensors for mapping urban areas, for monitoring urban expansion processes and for depicting landscape pattern dynamics in a metropolis of South America. Based on multi-temporal urban cover maps of Cordoba, Argentina, purposely derived from COSMO-SkyMed SAR data by urban extraction algorithms, we quantified urban surface increase and described urbanization processes that occurred during 2010–2021 in sectors with different degrees of soil sealing. We extracted urban extent in four time-steps using an Urban EXTent extraction (UEXT) algorithm and quantified urban expansion, identifying newly built areas on 2.5 ha cells. For these cells, we computed urban cover and a set of landscape pattern indices (PIs), and by projecting them in a composition vs. configuration Cartesian space we performed a trajectory analysis. SAR-based urban extraction and cover change proved to be very accurate. Overall accuracy and Cohen's Kappa statistic evidenced very high values, always above 91.58% and 0.82, respectively, for urban extraction, and also above 90.50% and 0.72 concerning the accuracy of urban expansion. Cordoba's urban surface significantly increased (≈900 ha in 10 years) following three main spatial processes in different city sectors (e.g., edge-expansion and outlying on peri-urban areas, and infill inside the ring road), which may have contrasting effects on the sustainability of the metropolitan area. Trajectory analysis highlighted non-linear relations between the urban cover and the PIs. Areas with very low and low urban intensity underwent a steep rise of both urban cover and PI values (e.g., urban patch dimension, complexity and number), depicting urban edge-expansion and outlying processes. In the areas with medium and high urban intensity the increase in patch dimension, along with the decrease in patch number and complexity, evidence the coalescence of urban areas that incorporate in the urban fabric the remnants of non-built up zones and fill the few residual green spaces. The proposed SAR mapping procedure coupled with landscape analysis proved to be useful to detect and depict different moments of urban expansion and, pending more tests on other cities and geographical conditions, it could be postulated among the RS indicators to monitor the achievement of the Sustainable Development Goals established by the United Nations. [ABSTRACT FROM AUTHOR]

Aim of the Study: in humans, spatial orientation consists of the ability to move around the environment through memorized and pre-programmed movements, according to the afferent sensory information of the body and environmental analysis of the Central Nervous System (CNS). The purpose of this study is to analyze the abilities of professional athletes, such as footballers, to use mental navigation systems, cognitive maps, and memorized motor patterns in order to obtain better physical performance and to obtain useful information for training both non-sports subjects and vestibular patients for rehabilitation purposes. Materials and Methods: all the motor performances of sportsmen, healthy non-sporting subjects, or vestibular patients are based on the acquisition of visual–spatial and training information. In this study, we analyzed the visual–spatial performance of 60 trained sportsmen (professional footballers), 60 healthy non-sports subjects, and 48 patients affected by chronic unilateral vestibular loss by means of the Navigation Ability Test 2.0. A score based on the number of targets correctly reached in the various tests quantifies the degree of performance of the subjects. Results: NAT 2.0 scores progressively improve from vestibular subjects to healthy non-sporting subjects to footballers. NAT 2.0 scores improve in all three subject groups as the number of tasks performed in all patient groups increases, regardless of gender and age. Conclusions: the analysis of performance data through NAT 2.0 in athletes (footballers) opens new perspectives for rehabilitation purposes, regardless of age, sex, and training conditions, both in healthy non-sporting subjects to improve their sporting potential and in patients affected by chronic vestibular dysfunction, in order to optimize their motor skills and prevent falls. [ABSTRACT FROM AUTHOR]

Urban areas are subject to multiple and very different changes, in a two- and three-dimensional sense, mostly as a consequence of human activities, such as urbanization, but also because of catastrophic and sudden events, such as earthquakes, landslides, or floods. This paper aims at designing a procedure able to cope with both types of changes by combining interferometric coherence and backscatter amplitude, and provide a semantically meaningful analysis of the changes detected in both city inner cores and suburban areas. Specifically, this paper focuses on detecting multi-dimensional changes in urban areas using a stack of repeat-pass SAR data sets from Sentinel-1A/B satellites. The proposed procedure jointly exploits amplitude and coherence time series to perform this task. SAR amplitude is used to extract changes about the urban extents, i.e. in 2D, while interferometric coherence is sensitive to the presence of buildings and to their size, i. e. to 3D changes. The proposed algorithm is tested using a time-series of two years of Sentinel-1 data, from May 2016 to October 2018, and in two different Chinese cities, Changsha and Hangzhou, with the aim to understand both the temporal evolution of the urban extents, and the changes within what is constantly classified as "urban" throughout the considered time period. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Highlights • ALOS PALSAR data are used to exploit the physical properties of halophyte plant scattering mechanisms in saline wetland environments. • Two novel classifiers the random M5 model forest (RM5MF) and the classification via random forest regression (CVRFR) are proposed. • Suitability of various polarization signatures and features are evaluated for halophyte plant mapping. • The results with two study regions demonstrates substantial performances of the proposed RM5MF and CVRFR. Abstract In this work, fully polarized SAR (PolSAR) data are exploited to characterize halophyte plants in lakeside saline wetland environments thanks to their different scattering properties. To this aim, several polarization signatures and morphological profiles (MPs) are used as inputs to the proposed “random M5 model forest” (RM5MF) and “classification via random forest regression” (CVRFR) classifiers. The experimental results show that parameters such as pedestal height (PH), as well as 3D co-polarization and cross-polarization signature plots, are more suited than biomass index (BMI), volume scattering index (VSI), and canopy scattering index (CSI) to map halophytic plants in arid environments. When we compare the suitability of PolSAR features using RM5MF, random forest (RaF), and other five popular attribute selection approaches, all the results uniformly show that span, MPs and entropy are the most valuable features, while PH and BMI are more valuable than CSI, VSI and the radar forest degradation index (RFDI). Additionally, the diagonal elements of the coherency matrix are more valuable than are the off-diagonal elements, and double-bounce, odd-bounce and wire elements are more valuable than helix bounce and volume bounce. The study results are obtained from PolSAR L-band quad-polarimetric high-sensitivity stripmap data over two study regions by comparing RM5MF and CVRFR with more traditional algorithms (support vector machine (SVM), RaF, rotation forest (RoF), and MultiBoostAB). The RM5MF model achieves the highest accuracy value in the study regions. However, due to the binary splitting criteria in the M5 model tree, it is more computationally intensive than all the others. In contrast, the CVRFR model consumes much less time—approximately 10 times less than RM5MF, and 5 times less than RoF—but still achieves better (3%–8%) classification accuracy than SVM or RoF, and its results are comparable (less than 1% difference) to those by RaF. [ABSTRACT FROM AUTHOR]

The possibility theory, which is an extension of fuzzy sets and fuzzy logic, has shown considerable potential for solving active learning (AL) problems, particularly for multiclass scenarios' classification. Hence, two recently proposed fuzzy multiclass AL algorithms (classification ambiguity (CA) and fuzzy C‐order ambiguity (FCOA)) are investigated to properly generalise them for classifying hyperspectral images, and two improved versions of the CA and FCOA are proposed. In addition to comparing the performances of the original and improved algorithms, several other state‐of‐the‐art AL methods are evaluated, such as breaking ties, margin sampling, and multi‐class level uncertainty, with or without diversity criteria such as angle‐based diversity (ABD), clustering‐based diversity (CBD), and enhanced clustering‐based diversity (ECBD). Tests on two benchmark hyperspectral images confirm that the proposed improved algorithms are superior to and more effective than the original ones. [ABSTRACT FROM AUTHOR]

Nasopharyngeal carcinoma (NPC) is an epithelial malignancy, with a high metastatic potential. Epstein-Barr virus (EBV) infection plays a fundamental role, even if it is not well understood. The diagnosis of the disease in its early stage is infrequent. Imaging studies, positron emission tomography scans in addition to clinical examination, endoscopic examination, and biopsy provide information on the extent of the disease. The application of neoadjuvant chemotherapy followed by concomitant chemoradiation can improve the control of NPC. In March 2016, a 54-year-old male with NPC cT1 cN2 cM0, stage III (8th edition of American Joint Committee on Cancer (AJCC) staging system) underwent to a two-step treatment: induction chemotherapy by TPF regimen (docetaxel, cisplatin, 5-fluorouracil), followed by concomitant chemoradiotherapy (weekly cisplatin). The quantity of free plasma EBV-DNA can be related to the disease stage, and the detection of EBV-DNA during follow-up can be predictive of distant metastases. Especially, either plasma or serum EBV-DNA titer is estimated to reflect tumor volume. Biologically, such EBV-DNA reflects reproduced or released DNA from dead or dying tumor cells. On the other hand, EBV-specific DNA released as exosome may reflect the biological feature of the alive NPC tumor cell. The follow-up is ongoing after 21 months from a complete response. [ABSTRACT FROM AUTHOR]

Active learning (AL) methods that select unlabeled samples only querying by informative measures (i.e., uncertainty and/or diversity criteria) have been extensively investigated. However, these methods usually do not exploit the manifold structure of the unlabeled data from the geometrical point of view, a choice that might lead to a sample bias and consequently undesirable performances. To control and possibly overcome such drawbacks, this paper explores AL methods based on joint informative and manifold structure representative sampling (JI-MSRS). In JI-MSRS, a portion of the unlabeled samples that are added at each iteration is selected according to the informative measures, whereas another portion is selected according to their capability to represent the data cluster structure. Four popular manifold learning methods, namely, principle component analysis (PCA), linear discriminant analysis, kernel PCA, and neighborhood preserving embedding, are used to model the data structure. Then, Delaunay triangulation nets are used to build a discrete approximation of the geometrical structure of the unlabeled data cloud in a low-dimensional space. To show the effectiveness of this novel sampling strategy, results on three real multi-/hyperspectral data sets are presented, adding a thorough comparison with other state-of-the-art AL techniques. In comparison to conventional AL heuristics, the proposed techniques are able to obtain competitive or even better classification accuracy values. [ABSTRACT FROM PUBLISHER]

In order to deal with scenarios where the training data, used to deduce a model, and the validation data have different statistical distributions, we study the problem of transformed subspace feature transfer for domain adaptation (DA) in the context of hyperspectral image classification via a geodesic Gaussian flow kernel based support vector machine (GFKSVM). To show the superior performance of the proposed approach, conventional support vector machines (SVMs) and state-of-the-art DA algorithms, including information-theoretical learning of discriminative cluster for domain adaptation (ITLDC), joint distribution adaptation (JDA), and joint transfer matching (JTM), are also considered. Additionally, unsupervised linear and nonlinear subspace feature transfer techniques including principal component analysis (PCA), randomized nonlinear principal component analysis (rPCA), factor analysis (FA) and non-negative matrix factorization (NNMF) are investigated and compared. Experiments on two real hyperspectral images show the cross-image classification performances of the GFKSVM, confirming its effectiveness and suitability when applied to hyperspectral images. [ABSTRACT FROM AUTHOR]

This paper discusses recent advances in data fusion of remotely sensed data for urban area characterisation. It focuses mostly on feature fusion, as a way to combine information available from multiple sensors, multiple spatial resolutions and multi-temporal acquisitions. The application on urban areas reveals the trend towards application-driven approaches, i.e. techniques aimed at using specific data sets and extracting peculiar features for well-defined tasks. This is also one of the reasons of the reduced importance of pixel-based fusion in this research area. [ABSTRACT FROM AUTHOR]

In this article, we introduce a unitary approach to road extraction in wide area images, obtained by means of satellite sensors in both the optical/infrared and microwave domains. Despite the large amount of methodologies discussed in technical literature for road extraction, they have been mostly tested on relatively small portions of satellite images. Moreover, in many cases, the method targeted an optical or a synthetic aperture radar (SAR) image, and a unitary strategy is missing. This study is aimed at bridging these gaps and provides a unique framework for the extraction of roads with different characteristics using optical or SAR data sets. The approach exploits a multi-scale analysis to adapt to the different resolutions of data and a pre-processing step to adapt to the different wavelengths of data. When possible, the framework allows the fusion of the road networks extracted from optical and SAR data of the same area. The soundness of the approach is proved by means of the analysis of Landsat and ALOS data of an area in Congo. [ABSTRACT FROM AUTHOR]

In many practical applications of hyperspectral remotely sensed data, maps of different land cover classes or features of interest are best achieved by means of different processing algorithms and techniques. In this paper, we introduce a novel methodology to build a multistage hierarchical data processing approach that is able to combine the advantages of different processing chains, which may be best suited for specific classes, or simply already available to the data interpreters. The combination process is carried out using a hierarchical hybrid decision tree architecture where, at each node, the most useful input information source, i.e., the processing chain, is used. The structure of the tree is created by using the predicted accuracy level of the whole structure estimated on a validation set. The final maps are achieved by applying the designed framework to the whole data set. The usefulness of the procedure is proved by two instances of a specific application, i.e., vegetation mapping, in mountainous and plain areas. [ABSTRACT FROM AUTHOR]

Recently, a multidimensional detector for magnetic recording channels has been proposed in order to better exploit the high media noise due to jitter and pulse broadening. The ideal scheme implementation is based on a bank of receiver filters matched to a Taylor approximation of the reading channel model. In this paper, the performance of a sub-optimal Baud-spaced multidimensional detector is evaluated for perpendicular channel systems, where the matched filters are replaced by partial response equalizers. Simulation results look promising, showing that the proposed detector may outperform the standard monodimensional linear predictor detector despite its suboptimal realization. [ABSTRACT FROM PUBLISHER]

Accurate and efficiently updated information on color-coated steel sheet (CCSS) roof materials in urban areas is of great significance for understanding the potential impact, challenges, and issues of these materials on urban sustainable development, human health, and the environment. Thanks to the development of Earth observation technologies, remote sensing (RS) provides abundant data to identify and map CCSS materials with different colors in urban areas. However, existing studies are still quite challenging with regards to the data collection and processing costs, particularly in wide geographical areas. Combining free access high-resolution RS data and a cloud computing platform, i.e., Sentinel-2A/B data sets and Google Earth Engine (GEE), this study aims at CCSS material identification and mapping. Specifically, six novel spectral indexes that use Sentinel-2A/B MSIL2A data are proposed for blue and red CCSS material identification, namely the normalized difference blue building index (NDBBI), the normalized difference red building index NDRBI, the enhanced blue building index (EBBI), the enhanced red building index (ERBI), the logical blue building index (LBBI) and the logical red building index (LRBI). These indexes are qualitatively and quantitatively evaluated on a very large number of urban sites all over the P.R. China and compared with the state-of-the-art redness and blueness indexes (RI and BI, respectively). The results demonstrate that the proposed indexes, specifically the LRBI and LBBI, are highly effective in visual evaluation, clearly detecting and discriminating blue and red CCSS covers from other urban materials. Results show that urban areas from the northern parts of P.R. China have larger proportions of blue and red CCSS materials, and areas of blue and red CCSS material buildings are positively correlated with population and urban size at the provincial level across China. [ABSTRACT FROM AUTHOR]

Abstract: In the literature, different approaches of artificial visual systems inspired to biological solutions can be found. In this paper a particular solution will be described with an experimental case study: the foveated retina approach. The solution can be considered as a proposal of ad-hoc hardware implementation of biological inspired solutions, but also as a model for the internal virtual representation of the real world structures. [Copyright &y& Elsevier]

This paper presents a novel iterative symbol timing recovery (STR) scheme for short burst transmission formats, a paradigm commonly found in modern wireless systems, like, for instance, time division multiple access (TDMA) schemes and future wireless packet data networks. Both data-aided (DA) and decision-directed (DD) solutions are considered and performance is pursued by means of an iterative burst-by-burst scheme which exploits the Farrow structure for the polynomial interpolation filter. The convergence of the algorithm is discussed according to the expectation maximization (EM) framework. Performance is evaluated by simulating 4-QAM and 16-QAM transceivers and simulations results are compared under different modulation orders and channel conditions, for both the decision-directed and data-aided cases. [ABSTRACT FROM AUTHOR]

An introduction to articles about data fusion is presented.

The article discusses the practicality of radar satellite approaches to damage patterns affected by major disasters and ancillary data. The author states that rapid damage mapping approach proves to be robust and useful in detecting damage patterns, however it is imprecise with respect to accuracy. Moreover, the combination of sensed data and geographical databases provides improvement, which makes the data more useful for the end-user. Furthermore, combination of the pre- and post event intensity data provides better results.

In this paper, a mapping procedure exploiting object boundaries in very high-resolution (VHR) images is proposed. After discrimination between boundary and nonboundary pixel sets, each of the two sets is separately classified. The former are labeled using a neural network (NN), and the shape of the pixel set is finely tuned by enforcing a few geometrical constraints, while the latter are classified using an adaptive Markov random field (MRF) model. The two mapping outputs are finally combined through a decision fusion process. Experimental results on hyperspectral and satellite VHR imagery show the superior performance of this method over conventional NN and MRF classifiers. [ABSTRACT FROM AUTHOR]

In this paper, the problem of rapid earthquake dam- age detection in urban areas using multitemporal synthetic aperture radar data is addressed. It is shown that the combination of intensity and phase features enhances the damage pattern extracted from the data temporal stack using a spatially aware classifier. Moreover, the use of ancillary data, easily available for urban areas, further improves the accuracy by discarding uninteresting parts of the scene and forcing homogeneous classification within city blocks to avoid "class-blurring" effects consequential to the window-based computation of relevant measures. The procedure is validated based on results for the town of Barn, Iran, and compared with ground-based survey maps. [ABSTRACT FROM AUTHOR]

The article presents a case report and review of literature concerning fibrous dysplasia. It states that the condition is a known skeletal disorder which accounts for 2.5% of all benign diseases of bone. It mentions that there exists only a few cases of cholesteatoma associated with fibrous dysplasia of the temporal bone. The author points out that surgical treatment is warranted only when there is an observed stenosis of the external auditory canal.

A general processing framework for urban road network extraction in high-resolution synthetic aperture radar images is proposed. It is based on novel multiscale detection of street candidates, followed by optimization using a Markov random field description of the road network. The latter step, in the path of recent technical literature, is enriched by the inclusion of a priori knowledge about road junctions and the automatic choice of most of the involved parameters. Advantages over existing and previous extraction and optimization procedures are proved by comparison using data from different sensors and locations. [ABSTRACT FROM AUTHOR]

In this paper, the problem of change detection from synthetic aperture radar (SAR) images is addressed. Feature-level change-detection algorithms are still in their preliminary design stage. Indeed, while pixel-based approaches are already implemented into existing, commercial software, this is not the case for feature comparison approaches. Here, the authors propose a joint use of both approaches. The approach is based on the extraction and comparison of linear features from multiple SAR images, to confirm pixel-based changes. Though simple, the methodology proves to be effective, irrespectively of misregistration errors due to reprojection problems or difference in the sensor's viewing geometry, which are common in multitemporal SAR images. The procedure is validated through synthetic examples, but also two real change-detection situations, using airborne and satellite SAR data over the area of the Getty Museum, Los Angeles, as well as over an area around the city of Bam, Iran, stricken in 2003 by a serious earthquake. [ABSTRACT FROM AUTHOR]

This work is an enlarged version of the paper ‘Discriminating urban environments using multiscale texture and multiple SAR images’, presented at WARSD'03.The use of multiscale textural features for urban satellite Synthetic Aperture Radar (SAR) image characterization is introduced. The multiscale nature of urban environments requires that no single scale of analysis is exclusively considered. An accurate texture‐based discrimination of land use/land cover classes needs, for instance, the computation of multiscale textural features for a wide range of parameters of the co‐occurrence algorithm. The technique proposed in this paper shows how to reduce the full multiscale feature set to a subset, the most suitable for classification using a fuzzy ARTMAP neural network. This is done by analysing the relevance of each feature for this particular classifier by means of the Histogram Distance Index (HDI). We validate the procedure by providing results of the classification of several satellite SAR data of the same urban test site. The results are encouraging. They show the potential of this technique for automatic extraction of the best texture and scale subset, suitable for efficient urban mapping using SAR satellite data. [ABSTRACT FROM AUTHOR]

Abstract: This presentation aims at stimulating ongoing research and development on the topic of urban remote sensing exploiting multiple data sets by offering a bibliographic review of the state-of-the-art and a look at the future in terms of the identifiable needs and advancement potential. The increasing availability of large amounts of data suitable for urban applications demands the development of adequate algorithms and procedures for their (semi-) automatic characterization. This study takes stock of the current achievements as reported in the technical literature that are particularly relevant to the interests of the remote sensing & information technology (RS&IT) community. It is also aimed at foreseeing and proposing future lines of research, focusing on the algorithms and techniques particularly suited for multiple data sets analysis in the urban areas. Many sensor types (optical, thermal, laser, SAR) are considered, and particular attention is given to satellite data analysis. After a review of the existing literature, the paper describes interesting application fields that are very much open for further research and the potential that urban remote sensing and urban data fusion offer to end users. [Copyright &y& Elsevier]

In this paper, we derive two techniques for the classification of multifrequenc/multitemporal polarimetric SAR images, based respectively on a statistical and on a neural approach. Both techniques are especially designed to exploit the spatial structure of the observed scene, thus allowing more stable classification results. Such techniques are useful when looking at medium- to large scale features, like the boundaries between urban and nonurban areas. They are applied to a set of SIR-C images of a urban area, to test their effectiveness in the identification of the different classes that compose the observed scene. A lower and an upper bound to the classification performance are introduced to characterize their limits. They correspond respectively to pixel-by-pixel classification and to the joint classification of the pixels belonging to the different classes identified in the ground truth. The results achieved with the two approaches are quantitatively analyzed by comparing them to the ground truth, Moreover, a hybrid approach is presented, where the homogeneous regions identified through statistical segmentation are classified using a neurofuzzy technique. Finally, a quantitative analysis of the results achieved with all the proposed techniques is carried out, showing that their classification performance is much higher than the lower bound and reasonably close to the upper bound. This is a consequence of their effectiveness in the exploitation of the spatial information. [ABSTRACT FROM AUTHOR]

In this paper, we present some improvements to urban area characterization by means of synthetic aperture radar (SAR) images using multitemporal and multiangle datasets. The first aim of this research is to show that a temporal sequence of satellite SAR data may improve the classification accuracy and the discriminability of land cover classes in an urban area. Similarly, a second point worth discussing is to what extent multiangle SAR data allows extracting complementary urban features, exploiting different acquisition geometries. To these aims, in this paper, we show results on the same urban test site (Pavia, northern Italy), referring to a sequence of European Remote Sensing Satellite ½ (ERS-½) C-band images and to a set of simulated X-band data with a finer spatial resolution and different viewing angles. In particular, the multitemporal data is analyzed by means of a novel procedure based on a neuro-fuzzy classifier whose input is a subset of the ERS sequence chosen using the histogram distance index. Instead, the multiangle dataset is used to provide a better characterization of the road network in the area, overcoming effects due to the orientation of the SAR sensor. [ABSTRACT FROM AUTHOR]

In this paper, we present how we used neural networks (NNs) and a pyramidal approach to model the data obtained by a weather radar and to short-range forecast the rainfall behavior. Very short-range forecast is useful, for instance, for estimating the path attenuation in terrestrial point-to-point communications. Radial basis function NNs are used both to approximate the rain field and to forecast the parameters of this approximation in order to anticipate the movements and changes in geometric characteristics of significant meteorological structures. The procedure is validated by applying it to actual weather radar data and comparing the outcome with a linear forecasting method, the steady-state method, and the persistence method. The same approach is probably useful also for predicting the behavior of other meteorological phenomena like clusters of clouds observed from satellites. [ABSTRACT FROM AUTHOR]

In this letter, we investigate the use of cooccrrence texture measures to provide information on different building densities inside a town structure. We try to improve the pixel-by-pixel classification of an urban area by considering texture measures as a means for block analysis and classification. We find some interesting hints concerning the optimal dimension of the window to be considered for texture measures, as well as the most useful measures. Moreover, we show that it is possible to use medium-resolution readily available satellite synthetic aperture radar images for a more refined urban analysis than previously shown. [ABSTRACT FROM AUTHOR]

In this paper, we describe a method to obtain a reliable set of elevation data suitable for data validation on the Shuttle Radar Topography Mission (SRTM), starting from laser scanning measurements on an urban test site: Pavia, Northern Italy. The elevation dataset is obtained through extraction of digital terrain models. The source digital surface model is first filtered by means of a lowpass or morphological kernel. Then, buildings are suppressed through analysis of the height histogram. Finally, a lowpass filter suppresses the surviving elevation artifacts. We show that, starting from a digital surface model at 1-m ground resolution, we end up with a digital terrain model that can be used as a ground truth for SRTM topographic analysis of an urban area. [ABSTRACT FROM AUTHOR]

Presents information a study which described a fuzzy approach to the analysis of airborne synthetic aperture radar (SAR) images or urban environments. Road network detection in SAR images; Overall structure of the proposed algorithm; Experimental results; Conclusions.

Presents information on a study which proved that point diffusion technique (PDT) is an effective tool in shaped-based queries in meteorological image archives. Mathematical formulation of the point diffusion technique (PDT); Application to shape similarity evaluation; Database used for the experiments; Results and discussion; Conclusions and proposals for future development of the research.

Presents information on a study which analyzed the data recorded by a ground penetrating radar. Ground penetrating radar description; Data preprocessing; Neural pipe detection; Experimental results; Conclusion.

Deals with a study which presented a complete procedure for the extraction and characterization of building structures from interferometric synthetic aperture radar data. Building detection strategy; Critical points of the algorithm for interferometric data segmentation; Experimental results; Conclusions.

Rural and urban green areas are essential territories that support life and ecosystems. The significant reduction of these areas due to urbanization is a pressing issue. The process of land take consumes not only land resources but also the connected ecosystems and the benefits generated for human society. Reducing the quantity of land taken is imperative, but preserving high-quality territories is essential to achieving sustainable development. Evaluating the quality of non-urbanized areas can be done by assessing the ecosystem services (ESs) provided by these areas. In this paper, the authors present a further step: an evolution and deepening of the previous methodology (published in 2020) to evaluate the quality of rural and urban green areas through the assessment of the ESs provided. The methodology first allows the identification of the ESs provided by different typologies of rural and urban green areas according to the common international classification of ESs (provisioning, regulation and maintenance, and cultural). Then, it allows the calculation of several singular indexes and a final composite quality index through the use of geographical information systems. An analytic hierarchy process was performed with the creation of different scenarios to consider the different importance of the singular indexes assigned by planners and communities involved. The methodology was applied to the province of Monza Brianza (Italy), for testing and validation purposes. The application to the municipality of Sovico, which is presented in this report, allowed for the identification of areas with higher quality in the different scenarios that were created to consider the relative importance of the territorial characteristics. [ABSTRACT FROM AUTHOR]

Presents information on a study which demonstrated the potentials and meteorological structures of boundary integral-resonant mode expansion (BI-RME) used for tracking and analyzing the spatial evolution of rain patterns. Meteorological data processing chain; Shape analysis by the BI-RME approach; Experimental results; Conclusions.

Discusses the application of neural network approach to the detection of cylindric objects as well as their geometric and electrical characteristics inside a given domain investigation. Details on the neural network inverse scattering approach; Numerical experiments; Results; Conclusions.

Presents a study which examined the application of a simplified shape analysis technique, in reference to the finite-element shape analysis. Modal shape analysis techniques; Information on deformation modes for different sampled similar shapes; Analysis of the search effectiveness on a larger database.

An introduction is presented in which the editor discusses various reports within the issue on topics including Institute of Electrical & Electronics Engineers (IEEE), remote sensing, and geology.

Flood is one of the most destructive natural hazards affecting the environment and the socioeconomic system of the world. The effects are higher in the developing countries due to their higher vulnerability to disaster and limited coping capacity. The Awash basin is one of the flood-prone basins in Ethiopia where the frequency and severity of flooding has been increasing. Amibara district is one of the flood-affected areas in the Awash basin. To minimize the effects of flooding, reliable and up-to-date information on flooding is highly required. However, flood monitoring and forecasting systems are lacking in most basins of Ethiopia including the Awash basin. Therefore, this study aimed to (i) identify important flood causative factors, (ii) evaluate the performance of random forest (RF), linear regression, support vector machine (SVM), and long short-term memory (LSTM) machine learning models for flood prediction and susceptibility mapping in the Amibara area. For developing flood prediction and susceptibility modeling, nine causative factors were considered, namely elevation, slope, aspect, curvature, topographic wetness index, soil texture, rainfall, land use/land cover, and curve number. The Pearson correlation coefficient and information gain ratio (InGR) techniques were used to evaluate the relative importance of the factors. The machine learning models were trained and tested using 400 historic flood points collected from the 10 September 2020 Sentinel 2 image, during which a flood event occurred in the area. Multiple metrics, namely precession, recall, F1-score, accuracy, and receiver operating characteristics (area under curve), were used to evaluate the performance of the models. The results showed that all the factors considered in this study were important; elevation, rainfall, topographic wetness index, aspect, and slope were more important while land use/land cover, curve number, curvature, and soil texture were less important. Furthermore, the results showed that random forest outperformed in predicting and mapping flooding for the study area whereas the linear regression model showed the next best performance to RF. However, SVM performed poorly in flood prediction and susceptibility mapping. The integration of satellite and field datasets coupled with state-of-the-art-machine learning models are novel approaches and thus improved the accuracy of flood prediction and susceptibility mapping. Such methodology improves the state-of-the-art knowledge in this field and fills the gaps of traditional flood mapping techniques. Thus, the results of the study can provide crucial information for informed decision-making in the processes of designing flood control strategies and risk management. [ABSTRACT FROM AUTHOR]

Accurate land cover information is pivotal in numerous planning and management activities. Synthetic Aperture Radar (SAR) data has emerged as a valuable resource for land cover assessment. Extracting scattering power components from Polarimetric SAR (PolSAR) data provides essential insights into the characteristics of land cover targets, aiding in their detailed characterization. Various techniques in radar polarimetry have been introduced to obtain scattering power components and explore their significance in land cover applications, typically leveraging full-polarimetric (full-pol) SAR data. However, applying these methods directly to dual-polarimetric (dual-pol) HH-HV or VV-VH data presents challenges due to the limited polarimetric information available compared to full-pol SAR data. Distinguishing between the scattering mechanisms of dihedral (or double-bounce) targets and trihedral (or surface) targets based solely on conventional polarimetric information is particularly challenging in dual-pol data compared to its full-pol counterpart. To address this limitation, our study introduces two indices (DpRBI and DpRSI) designed to characterize scattering from "dihedral-like" (e.g., buildings, bridges) and "surface-like" (e.g., water bodies, bare fields) targets in dual-pol SAR data. These indices, measuring the similarity of a given pixel to either a "dihedral-like" or a "surface-like" target, are then used in the Scattering Power Factorization Framework (SPFF) to derive the proposed scattering power components. The SPFF discerns the primary scattering power contribution within a resolution cell by factorizing the total power, employing the two indices. Our method applies to both Single Look Complex (SLC) and Ground Range Detected (GRD) dual-pol SAR data from operational platforms such as Sentinel-1 and the upcoming NISAR missions. We assess the proposed methodology using Sentinel-1 SLC and GRD SAR data for two scenarios. (1) Spatial variation in the scattering power components over various land covers near Delhi, India; Milan, Italy; and Barcelona, Spain. (2) Temporal variation in the scattering power components over rice-cultivated areas near Vijayawada, India. During these assessments, we observed significant variations in scattering power components over different land cover targets and rice-cultivated fields at various phenological stages. This highlights the capability of the proposed methodology to discern scattering information from dual-pol SAR data. [ABSTRACT FROM AUTHOR]

Satellite images from the same scene observed over time can be composed in an image stack, which could be modeled as a 3-D cube. To handle this type of remote sensing data, on the one side, unidimensional dynamical models have been considered, modeling each pixel separately along the time (pixel-based approach), and exploring the temporal correlation. On the other side, 2-D approaches have been considered to process each image at one date, exploring the spatial correlation. In this article, we propose a new 3-D autoregressive (AR) (3-D-AR) model useful for multitemporal image interpretation exploring the correlation in three dimensions altogether. The 3-D-AR model is statistically defined, and a robust parameter estimation method is discussed. The tools for filtering, forecasting, and detecting anomalies are also introduced. A Monte Carlo simulation study is performed to evaluate the finite signal length performance of the robust estimation and its sensitivity to outliers. The proposed model is applied to a multitemporal normalized difference vegetation index (NDVI) image stack for filtering, prediction, and anomaly detection purposes. The numerical results show the importance of the proposed 3-D-AR model for spatiotemporal remote sensing data interpretation. [ABSTRACT FROM AUTHOR]

Monitoring dense forest ecosystems, such as the laurel forest in Garajonay National Park, is vital for biodiversity conservation, carbon storage, and ecological balance. This study employs satellite remote sensing technologies to introduce a novel methodology, based on vegetation indices, aiming to assess and protect the health of the forest. Utilizing the Jeffries-Matusita distance and a histogram-based method, optimal indices to map forest degradation, like Wide Dynamic Range Vegetation Index (WDRVI) and Modified Simple Ratio (MSR), were identified among 19 generated indices. The study processed imagery from three satellite sensors (WorldView-2, PlanetScope and Sentinel-2), producing maps distinguishing healthy and degraded areas. The study's practical significance lies in offering a method to assess the suitability of sensors and indices for effectively mapping forest degradation. This approach aids conservation efforts and provides valuable insights for environmental managers and policymakers, facilitating the implementation of targeted strategies to safeguard Garajonay National Park's unique laurel forest ecosystem. Emphasizing the role of remote sensing in practical vegetation protection endeavors, the study contributes to on-the-ground initiatives, ensuring the preservation and sustainability of the park's rich biodiversity. [ABSTRACT FROM AUTHOR]

A timely and accurate spatial mapping of built-up areas (BA) is crucial in making cities and human settlements safe, resilient, and sustainable. Synthetic Aperture Radar (SAR) data are useful for BA mapping due to strong coherent backscatter from diverse human-made targets, distinct texture patterns, and sensitivity to its geometric characteristics. However, BA mapping using SAR data is still challenging due to various geometrical and physical factors that often lead to misinterpretations. Therefore, this study presents a novel method to map BA using single-date dual polarized Sentinel-1 SAR data to overcome such challenges in complex built-up scenarios. Here, we propose three normalized descriptors derived from the Stokes vector elements. These three descriptors are found to be vital in characterizing different types of BA structures. We then consider the symmetric quadratic mean of the normalized descriptors to propose a novel dual polarimetric radar built-up index (DpRBI). The proposed index provides information about the average normalized polarized power, which is useful for mapping diverse types of BA or their combination in an image. We assess the proposed methodology utilizing the Sentinel-1 image acquired over three major cities: Delhi, Milan, and Barcelona. The BA map accuracy for all three cities was found to be ≈ 85 % with a low BA omission error. We compared the mapping results with available BA/land cover products: World Settlement Footprint 2019 (WSF-19) and World cover 2020 (WC-20). The BA map obtained using the proposed index was better than WSF-19 and WC-20, particularly for BA omission errors. For example, we observed a relatively low BA omission error of 22.34 % using DpRBI compared to 31.86 % and 41.79 % for WC-20 and WSF-19, respectively, over Milan, Italy. The code is available at: https://github.com/navv37/DpRBI [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM PUBLISHER]

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. [ABSTRACT FROM PUBLISHER]

Automatic road extraction from synthetic aperture radar (SAR) imagery has been studied with success in the past two decades. However, a method that combines full interferometric SAR (InSAR) information is as yet missing. In this paper, we present an algorithm toward robust road extraction by fully exploring the multitemporal InSAR covariance matrix. To improve the detection performance and reduce false alarm ratio, intensity and coherence are first accurately estimated without loss of image resolution by homogeneous pixel selection and robust estimators. After the identification of road candidates from each quantity using multiscale line detectors, novel information fusion rules are applied to integrate the extracted results and generate the final road network. The method is tested and quantitatively evaluated on TerraSAR-X data sets depicting two scenes where complex road features make it hard for standard SAR-based methods. The experimental results show that the new method can achieve satisfactory detection performances. [ABSTRACT FROM PUBLISHER]

In this paper, we present the supervised multi-view canonical correlation analysis ensemble (SMVCCAE) and its semi-supervised version (SSMVCCAE), which are novel techniques designed to address heterogeneous domain adaptation problems, i.e., situations in which the data to be processed and recognized are collected from different heterogeneous domains. Specifically, the multi-view canonical correlation analysis scheme is utilized to extract multiple correlation subspaces that are useful for joint representations for data association across domains. This scheme makes homogeneous domain adaption algorithms suitable for heterogeneous domain adaptation problems. Additionally, inspired by fusion methods such as Ensemble Learning (EL), this work proposes a weighted voting scheme based on canonical correlation coefficients to combine classification results in multiple correlation subspaces. Finally, the semi-supervised MVCCAE extends the original procedure by incorporating multiple speed-up spectral regression kernel discriminant analysis (SRKDA). To validate the performances of the proposed supervised procedure, a single-view canonical analysis (SVCCA) with the same base classifier (Random Forests) is used. Similarly, to evaluate the performance of the semi-supervised approach, a comparison is made with other techniques such as Logistic label propagation (LLP) and the Laplacian support vector machine (LapSVM). All of the approaches are tested on two real hyperspectral images, which are considered the target domain, with a classifier trained from synthetic low-dimensional multispectral images, which are considered the original source domain. The experimental results confirm that multi-view canonical correlation can overcome the limitations of SVCCA. Both of the proposed procedures outperform the ones used in the comparison with respect to not only the classification accuracy but also the computational efficiency. Moreover, this research shows that canonical correlation weighted voting (CCWV) is a valid option with respect to other ensemble schemes and that because of their ability to balance diversity and accuracy, canonical views extracted using partially joint random view generation are more effective than those obtained by exploiting disjoint random view generation. [ABSTRACT FROM AUTHOR]