This study investigates the efficacy of NDVI (Normalized Difference Vegetation Index), EOMI (Exogenous Organic Matter Index), and MNDWI (Modified Normalized Difference Water Index) in identifying application of manure in sandy soil farmlands. By leveraging Sentinel-2 multispectral imagery acquired over Turkish farmland in the Adana region, this study aims to assess the joint utilization of these indices to enhance detection methodologies within the specified geographic context.

Independent, large-scale assessment of agricultural practices can be useful for inventorying and certification purposes. Satellite Earth observation technology may represent a powerful tool to implement this application. In this paper we analyze the problem of identifying from NDVI sequences the type of inter-row management in vineyards, i.e. whether vegetation is allowed to develop in the inter-row space or this latter is tilled and soil remains exposed. Typical sequences are analyzed from Sentinel-2 acquisitions over a test site in northern Italy, and both supervised and unsupervised classification is applied to features extracted from time sequences. The accuracy levels reach high values only in some cases, but joint analysis of features and resulting accuracy levels is providing significant clues laying the basis for future improvement.

Intra-body communication will facilitate next-generation personalized medicine by enabling interconnection among implanted devices. To this purpose, energy-efficient communication technologies are required such as galvanic coupling (GC). Although some GC testbeds have been developed to implement the entire communication chain, synchronization problems have not yet been tackled exhaustively. While some papers simply assumea-prioriperfect synchronization between GC transmitter and receiver, other studies developed solutions that often are time-consuming. In this paper, an energy-efficient and fast maximum-log-likelihood (ML) synchronization method is proposed, that can operate in real time and follow channel variations. Experiments reveal that the proposed ML synchronization scheme is very effective for short-range GC communication up to 4 cm, with performance similar to the current State-of-the-Art. It shows slightly lower performance levels for higher distances, still it offers the benefit of lower computational requirements than the reference method.

Intra-body communication (IBC) will foster personalized medicine by enabling interconnection of implanted devices. Communication takes place through energy-efficient technologies such as capacitive coupling (CC) and galvanic coupling (GC); however, their modeling is still incomplete. This paper tackles characterization of the human body channel using impulse response, including a first-ever comparison of CC and GC in both wearable and implantable configurations. Experimental data are leveraged to evaluate the measured impulse response in ex-vivo chicken tissue and in-vivo human tissue in a frequency range up to 100 kHz. Pseudorandom noise (PN) sequences are transmitted in baseband and a correlative channel sounding system is implemented. Experimental results demonstrate that the channel is relatively flat in the frequency range of interest, thus offering the opportunity to simplify the design of an IBC transceiver. The relationship between the channel responses and the transmitter-to-receiver distance is also examined using linear correlation, and two regression models are developed. The results show that CC channels are not affected by distance within the range of investigation, while a negative relationship is found for GC channels. Finally, experiments reveal that implantable CC with isolated ground -not deeply investigated yet- is a very promising solution for IBC.

In space applications, hardware (HW) implementation is made more expensive not only by the levels of performance required, but also by complex and rigorous HW qualification tests. Reducing qualification cost and time is thus a key design requirement. In this paper, a new versatile transmitter is proposed for space telemetry, capable of soft-switching across different linear and continuous phase modulation schemes while maintaining the same hardware structure. This permits a single HW qualification to “cover” diverse uses of the same hardware, and thus avoid re-qualification in case of configuration changes. The envisaged solution foresees the use of a single filter, suitable not only for linear modulations such as M-QAM, but also for continuous phase modulation methods. At this stage, we focus on pulse code modulation/frequency modulation (PCM/FM), for which we propose a minimum mean square error (MMSE) algorithm. The proposed algorithm, which adds to the system flexibility and effectiveness, may use a single first filter based on Laurent decomposition for initialization, if needed. Performances are assessed using the mean square error (MSE) measure between the proposed MMSE-modulated signal and the completely modulated signal. Simulation results confirm that the proposed algorithm leads to MSE values that are lower than the case of Laurent decomposition using the first component only.

Food traceability in organic agriculture requires a comprehensive "crop history" that includes information from the moment seedlings begin to sprout. Radar remote sensing could contribute in this framework by providing satellite-observable variables and time sequences that help build a more complete crop history. One possible application of this concept is monitoring tillage techniques, which have different impacts on soil properties. The so-called "minimum tillage" reduces erosion and surface runoff, and translates into different backscattering mechanisms in spaceborne radar observation, compared to those of conventional ploughing. In this work, preliminary experiments were conducted to assess the type of tillage based on sequences of spaceborne radar data, specifically plowing-and-harrowing versus minimum tillage. By using radar remote sensing, customers can achieve a more comprehensive understanding of the history of their food from "farm-to-fork", which can be particularly important for high-tier organic food.

This paper investigates the feasibility of using multitemporal Sentinel-2 data to help monitor compliance with "closed periods" regulations for manure applications in agriculture, and to detect potential infringements. Previous work used Exogenous Organic Matter Indices (EOMIs), derived from Sentinel-2 bands, to identify fields with and without manure spreading. In this study, time series of the EOMI index are analyzed, and a method for satellite-based manure detection is proposed. Results show distinctive features on the occasion of known manuring events, but further validation is needed due to the lack of an extensive set of samples with related ground truth. The proposed method shows potential for operational use in monitoring manuring compliance.

In this work, we investigate how time series of the Normal-ized Differential Vegetation Index (NDVI) can provide use-ful clues to enhance the traceability of organic food, and dis-cuss the possibility to use machine learning in this context. Crop rotation, non-chemical weed control operations such as “green mulching”, fertilization, water supply management, all reflect into variables that are observable from space and may help reassuring the consumer that the traceable food they are purchasing matches the declared standards of sustainability and organic compliance. In this study we address detection of green mulching and weeding, based on experiments on a set of rice fields in North-ern Italy. Our findings suggest that the cover crops associated with green mulching can be confirmed and weeding can be detected using data from the Sentinel-2 satellite constellation, whereas fertilization is far more difficult to detect correctly. The cost associated with procuring training data seems to dis-courage the use of machine learning at this stage.

The use of multitemporal SAR sequences is becoming more intensive in mapping and change detection applications, be-cause of the availability of data sets with enough time length and sampling frequency. However, this procedure often requires to consider sequences of heterogeneous SAR data sets acquired by different sensors with different spatial resolution, frequency and polarimetric features. Their full exploitation is still an open and interesting research task. This work introduces first a pre-processing sequence ap-plied by the team of the project MultiBiGSARData to obtain calibrated and co-registered heterogeneous SAR sequences. Then, a first example of their use for land cover classification is provided. The results are shown for test areas in Italy and Argentina, because the data sets are obtained by combining images acquired by the SIAGSE constellation, composed by the COSMO-SkyMed (CSK), COSMO-SkyMed Second Generation (CSG), and SAOCOM satellites.

Change detection (CD) from satellite images has become an inevitable process in earth observation. Methods for detecting changes in multi-temporal satellite images are very useful tools when characterization and monitoring of urban growth patterns is concerned. Increasing worldwide availability of multispectral images with a high revisit frequency opened up more possibilities in the study of urban CD. Even though there exists several deep learning methods for CD, most of these available methods fail to predict the edges and preserve the shape of the changed area from multispectral images. This article introduces a deep learning model called urban CD network (UCDNet) for urban CD from bi-temporal multispectral Sentinel-2 satellite images. The model is based on an encoder–decoder architecture which uses modified residual connections and the new spatial pyramid pooling (NSPP) block, giving better predictions while preserving the shape of changed areas. The modified residual connections help locate the changes correctly, and the NSPP block can extract multiscale features and will give awareness about global context. UCDNet uses a proposed loss function which is a combination of weighted class categorical cross-entropy (WCCE) and modified Kappa loss. The Onera Satellite Change Detection (OSCD) dataset is used to train, evaluate, and compare the proposed model with the benchmark models. UCDNet gives better results from the reference models used here for comparison. It gives an accuracy of 99.3%, an$F1$score ($F1$) of 89.21%, a Kappa coefficient (Ka) of 88.85%, and a Jaccard index (JI) of 80.53% on the OSCD dataset.

Snow is an important environmental variable and a primary water resource in many areas of the world. Monitoring seasonal snowpack properties is also crucial for properly managing snow-related hazards such as snow avalanches and snowmelt floods. Recently, an innovative radar architecture, based on the use of two receivers, has been proposed for snowpack monitoring for the case of dry snow, where the snowpack depth and bulk density can be calculated with one single radar measurement, without any kind of external aid. This article presents the extension of this innovative radar architecture for the case of wet snow. The approach to determine, not only the snowpack depth and bulk density but also the liquid water content, is outlined and discussed in detail, along with the experimental validation of the operating principle for two cases.

Probably a consequence of the unbalance between rice production in Asia and in Europe, satellite-based rice identification in Asia is widely discussed in scientific literature whereas SAR-based mapping of European rice paddy field has received less attention so far. In this paper, we propose a simple methodology for identifying European rice paddy fields from time series of SAR data. Standard practices for management of water in conventional European rice paddy fields translates into a distinctive pattern of low backscatter values between April and May, typically preceded and followed by higher backscatter values due to ploughing and emergence. Our proposed method leverages such pattern to discriminate rice against other crops and in a test involving the entire Italian rice-producing region of Lombardy has achieved very good Overall Accuracy (OA) scores. This paper reports the method, our test results and draws some preliminary conclusions.

Risk mitigation strategies to reduce the impact of avalanches on infrastructures, such as evacuation of mountain villages, and planned closure of roads, railways and ski resorts, are heavily dependent on avalanche forecasting capability. Moreover, the possibility to determine the snow water equivalent (SWE) of a snowpack is a crucial step for water management strategies used, for example, in agriculture and hydroelectric power plants. In both cases, for dry snow, two key physical parameters are the total snow thickness and the wave speed in the medium. Microwave radars are being used to monitor snowpacks, but they invariably invoke external aids or a priori assumptions to calculate these physical parameters. This paper presents an innovative radar architecture for snowpack monitoring, of a single emitting and two receiving antennas. This novel configuration enables simultaneous identification of both total snow thickness and wave speed in the medium without any additional hypothesis or device. For dry snow, consequently, snow density and SWE can also be immediately determined. The proposed architecture is validated using first numerical simulations and then indoor and outdoor experimental results. These latter achieved accuracy levels better than 10% for total snow thickness and better than 13% for wave speed.

The long-time technological trend towards ever-finer ground resolution in space-borne multispectral data has opened the doors to finer levels of urban mapping and monitoring. Single buildings and their features can nowadays be detected and mapped starting from nadiral data; yet, despite a large body of research results, an exhaustive solution to space-based building mapping is still to be found. In this paper, we give our contribution by proposing a novel approach to the extraction of rooftop shapes of buildings from very-high-resolution (VHR) optical multi-spectral data. The approach is derived from existing work, namely an automatic rooftop extraction method intended for aerial imagery. Because of the very different nature of the data, it was necessary to rearrange the reference method, modifying and adding new constraints, applying both spectral and spatial conditions. This work was developed in the framework of the EU H2020 Satellite Swarm Sensor Network (S3NET) project.

In this paper, a synthetic snowpack created to test, with an indoor controlled setup, microwave radars aimed at snow cover monitoring, is presented for the first time. The synthetic snowpack is realized using low-cost materials available in large formats, such as cork and polystyrene, whose dielectric properties are experimentally characterized in the frequency range from 100 MHz to 1 GHz. It is shown that it is possible to replicate the dielectric properties of dry snow for a wide range of snow density. Then, different layers of cork and polystyrene are used to compose three different synthetic snowpacks, which are validated using a frequency modulated continuous wave microwave radar to identify the internal layers of the snowpacks.

In this manuscript, we discuss a new approach to snow pack monitoring from buried radar, using more than one receiver to solve the ambiguity given by the unknown refraction index, in the simplified hypothesis of homogeneous snow. The concept is presented in previous papers, whereas in this paper we briefly analyze the expected accuracy of snow height estimates as a function of where the two receivers are located.

In order to ensure homogeneity in performance assessment of proposed algorithms for information extraction in the Earth Observation (EO) domain, standardized remotely sensed datasets are particularly useful and welcome. Fully aware of this principle, the IEEE Geoscience and Remote Sensing Society (GRSS) and especially its Image Analysis and Data Fusion Technical Committee (IADF), has been organizing for some years now the Data Fusion Contest (DFC). In the DFC, one specific dataset is made available to the scientific community, which can download it and use it to test its newly developed algorithms. The consistence of the starting dataset across participating groups ensures the significance of assessing and ranking results, to finally proclaim the winner who scored the highest. More recently, the IEEE GRSS has provided one more contribution to the standardization effort by building the Data and Algorithm Standard Evaluation (DASE) website. DASE can distribute to registered users a limited set of possible “standard” open datasets, together with some ground truth info, and automatically assess the processing results provided by the users. In this paper we report on the birth of this initiative and present some recently introduced features.

The recent trend toward open Earth observation (EO) data has revived a general interest in satellite-based monitoring and mapping of the Earth surface. The open policy now applied to LANDSAT data, and the starting of Sentinel operations, whose data are freely distributed even for commercial purposes, tore down a financial barrier to wider use of EO data in business activities, especially those with a narrow financial margin. Notwithstanding the flood of open data, some aspects of the Earth surface still escape satisfactory monitoring from space, especially in complex, anthropic areas. Due to insufficient spatial resolution, lack of visibility, or unsuitable revisit times, important pieces of information may not emerge from spaceborne data.In situsensing can represent a vital source of integrative information to fill the aforementioned gaps and build a more complete and accurate picture of the situation and trends in the observed area. The contribution ofin situsensing was envisaged quite early in the Earth observation history, but for a long time it remained limited to tailored sensors displaced in strategic locations. With the increasing circulation of smartphones, a new opportunity has recently opened for a different paradigm of in situ sensing, offering a huge mass of additional data by tapping on data generated by mobile devices. Even if such data may be less specialized and less usable for various reasons, the sheer size of the data flow ensures that statistical analysis will pick possible useful clues. The increasing availability of mobile connections has indeed revived the concept of “crowdsourcing,” i.e., entrusting a pool of actors with problem solution or information collection tasks. In our scenario, individuals carrying mobile devices can become “citizen sensors” on a voluntary basis by contributing data through their connected terminals. Even considering the typical issues of crowdsourced data, like quality and reliability, the balance remains definitely positive. This paper provides an overview of the theme and discusses how it relates to an important, coordinated EO initiative like Copernicus. It finally presents a specific example realized in the framework of a recent research project under the Copernicus aegis.

Centuriation was a regular method for land parcel definition used by the ancient Romans, whose vestiges are still visible today in some places. Detecting such vestiges is relevant to landscape archaeology studies. In this paper we propose a new method for extensive automated inspection of heritage aerial photos in order to detect clues of centuriation.

The concept of crowdsourcing is to collect and share information from “the crowd”. The diffusion of smartphones and tablets led to what can be pictured as a “dense network of observers” that can be used to submit useful data possibly complementing satellite-based Earth observation. In this paper, we propose a multi-purpose framework specifically designed to collect data in a distributed way; the model is based on a client-server architecture and an open-source framework including PHP and SQL. Communication among the devices is guaranteed by standard protocols like HTTP and JSON. An example of application based on this framework is also presented; the main objective is to collect information about water quality by asking users to provide feedback. The interface is both web- and mobile-based.

Satellite acquisitions from LANDSAT (LS) and CBERS programs are widely used in monitoring land cover dynamics. In the acquired products, clouds form opaque objects are obscuring parts of the scene and preventing a reliable extraction of information from these areas. Consequently, cloud shadows create similar problems, as the reflected intensity of the shadowed areas is highly reduced, generating additional info gaps. The problem can be handled by replacing clouds/shadows pixels from other close-date acquisitions, but that would assume a prior knowledge of the spatial distribution of clouds and their corresponding shadows in a scene. This research introduces a method that provides the clouds/shadows layers and their percentage in LS (TM & ETM+) and CBERS (HRCC) scenes. The approach relies on a set of literature indicators to create a composite image that enhances the visual differentiation of clouds/shadows from other objects. The created composite RGB are then warped to a relative luminance raster calculated from the linear bands components. Afterwards, the raster is processed by a K-means unsupervised classifier with a definite number of classes in order to isolate the target-layer pixels. Next, the statistical mode for the population of each class is calculated, compared and used to select the cloud/shadow class automatically, and finally the results are refined by a set of morphological filters. The processing chain avoids the usage of thresholds and highly reduces the user intervention. The achieved outcomes on various test cases are promising and stable, and encourage further developments.

Climate change challenges our understanding of risk by modifying hazards and their interactions. Sudden increases in population and rapid urbanization are changing exposure to risk around the globe, making impacts harder to predict. RASOR will develop a platform to perform multi-hazard risk analysis for the full cycle of disaster management, including targeted support to critical infrastructure monitoring and climate change impact assessment. A scenario driven query system simulates future scenarios based on existing or assumed conditions and compares them with historical scenarios. Initially available over five case study areas, RASOR will ultimately offer global services to support in-depth risk assessment and full-cycle risk management.

Location of engines on an aircraft body is crucial for preliminary, quick discrimination of the aircraft type. Fans in jet engines are known to generate a modulation on the reflected radar wave, generating a large Doppler spread. Such spread results in multiple, iso-range detections of significant backscatter at the range location of each engine, whose frequency displacements reflect in quasi-periodic structures on an iso-range line. This paper reports on a method we have developed for detecting such lines based on their specific characteristics in order to determine the range location of aircraft engines.

Change detection is by definition the capability to detect and highlight changes occurring in space and time. Earth Observation satellites represent a fundamental source of information thanks to repeatability in time and spatial resolution. In this paper, we propose an unsupervised change detection technique capable of processing a series of single-date built-up area extractions with two main goals: determining the age of different parts of an urban area and fixing errors due to the automatic extractions suggested in previous papers by our group. Results show a general stabilization of the Kappa value but further investigation is still necessary. The proposed algorithm is available to the general public as a part of a QGIS plugin named SENSUM Earth Observation (EO) tools.

Urban expansion monitoring and organization can be performed through space-based observation thanks to the revisit time and level of details guaranteed by satellite remote sensing. In particular, the Landsat mission products are the most used thanks to the long time coverage and open access policy. This paper proposed a hybrid method - combination of pixel- and object-based analysis - in order to automatically extract built-up areas from Landsat imagery. Segments are delineated from spectral indices computed in order to increase the spectral distance among the different land cover classes. The principal component analysis is applied to the original bands and constitutes the pixel-based side of the method. Segments and PCA are then combined and classified using an unsupervised approach. Results of the method were quite satisfying with an average Kappa value over 0.5 in both case studies.

The aim of this research is to depict a rough, synthetic description of what technical education in aerospace and remote sensing means to Italian Universities. This work was done in the framework of the activities of the “Education” Working Group of the Lombardy Aerospace Industry Cluster, and this paper briefly summarizes some of the findings. A similar, but more in-depth and extensive work is in progress at the European and global level.

The phenomenon of urban sprawling calls for a cost-effective and rapid monitoring tool. Space borne technology offered the ability of extracting such information which is critical for urban planning and resource management. This paper introduces a newly developed indicator from Landsat acquisitions for highlighting built-up areas. On a multitemporal scale the results obtained on urban extraction were used to estimate the age of built up area, connected to codes applied in building design and ultimately to their vulnerability. In conclusion, the extracted information serves as inputs for an integrated approach on multi-risk buildings' exposure and physical vulnerability assessment and mapping.

This paper describes a combined approach used to assess damage occurred to the city of L'Aquila after the April 2009 earthquake. A survey mission in October 2011 provided the ground images of the most-affected area, still useful because of the substantially unchanged area. Main concept is the fusion of the manual-made interpretation of space-borne satellite imagery along with a ground-based damage analysis, based on the hypothesis of different damage detectability from the two considered sources. Obtained results show improvements in the total accuracy for the proposed hybrid approach confirming the initial hypothesis. This approach can be considered as an attempt to consider the crowd as a source of information (in this case information provided in the form of pictures).

The characterization of urban areas can be improved considerably by combining spectral and spatial features. As a matter of fact, depending on objects of interest in a specific application, the exploitation of both types of features at multiple spatial resolutions is required. This paper proposes a decision fusion method that relies on both spectral and textural features. The proposed approach is able to produce different classification results based on distinct partitions of the same input data set. Experiments conducted on CBERS-2B data demonstrate a significant performance improvement brought by the combination of spectral and textural features in comparison to the use of only spectral features to describe the image objects.

In this paper we propose a new method for aircraft identification and classification using high-resolution ISAR images acquired from an airborne radar sensor. The proposed method takes advantage of some of the geometric features extracted from the image, and some signal features as well (like the Jet Engine Modulation phenomenon that allows us detecting and analyzing the engines of the unknown aircraft). In the next session we will provide a brief explanation of the problem, in order to show the proposed method. An ad hoc classification database has been developed, containing physical characteristics of some aircrafts. Features extracted from radar data have been compared with the database content for classification and some preliminary results have already been obtained. These results appear encouraging and will be shown in the paper.

In the framework of the monitoring of structures and infrastructures from environmental disasters, the COSMO-SkyMed constellation has a huge potential, thanks to up to metric spatial resolution, short revisit time, and the day/night all-weather acquisition capability ensured by SAR. This paper focuses on the scientific results of the project “Development and validation of multitemporal image analysis methodologies for multirisk monitoring of critical structures and infrastructures,” funded by the Italian Space Agency. Several change-detection, data-fusion, and feature-extraction techniques, which were developed and experimentally validated in the project for COSMO-SkyMed imagery and for their integration with other data sources (including very high resolution optical data), are described and examples of processing results are discussed.

In this paper, a survey of the techniques and data sets used to evaluate earthquake damages using remote sensing data is presented. After a few preliminary definitions about earthquake damage, their evaluation scale, and the difference between identification of damage “extent” and identification of damage “level,” the advantages and limits of different remote sensing data sets are presented. Furthermore, a survey of proposed algorithms for data interpretation and earthquake damage extraction is presented, and two examples of these algorithms and their results are discussed. According to the outcome of this survey, some open issues are finally presented and discussed, identifying possible research lines as well as working solutions.

Automated earthquake damage assessment from post-event only remotely sensed data is highly desirable, especially when new generation, Very High Resolution (VHR) spaceborne data is concerned, lacking extensive pre-event archives. Though, most damage assessment method either rely on human interpretation or on pre-post-event comparison. In this paper we illustrate some possible tracks for investigating damage assessment on post-event only data, focusing on the 6thApril 2009 Abruzzi, Italy earthquake and on related COSMO/SkyMed acquisitions.

With the continuing increase in the number of images collected everyday from different sensors, the automated registration of multisensor/multispectral images has become a very important issue. This is particularly true when pre- and postevent image comparison is concerned: For this particular application, the requirement of obtaining the earliest possible postevent image imposes the use of data potentially possessing significantly different characteristics with respect to the pre-event image. Strongly inhomogeneous image pairs require robust automatic registration techniques, preferably based on resolution-independent feature-based registration. In a previous paper, we proposed a mode-based feature-matching scheme mutated from the computer vision domain and adapted to pre- and postevent feature matching. Some of the weak points highlighted in that first version are addressed in this paper, where a new version of the method is proposed, which exploits a new piece of information, i.e., the adjacency between feature points, generally preserved across the disaster event. Extensive generation of synthetic cases allowed one to obtain significant feedback and, consequently, tune the algorithm. Three real cases of pre- and postevent feature matching on high-resolution satellite images are shown and discussed.

The Sichuan Earthquake on the 12th of May 2008, and the extensive rescue operations following this tragic event, proved the value of high-resolution optical and radar remote sensing during the emergency response. Optical data provide a fast and simple way to value ¿at glance¿ damages while radar sensors can deliver images independent of weather conditions, day and night, and thus in principle can represent a mean to obtain a damage map in the immediate aftermath of an event, providing precious information for intervention planning. On the other hand, SAR data is far more difficult to interpret than optical data both to the expert and non-expert. In this paper we present a case study of damage assessment on the Sichuan earthquake experimenting the use of very high resolution data from both worlds, discussing preliminary results and perspectives.

With the continuing increase in the number of images collected every day from different sensors, automated registration of multisensor/multispectral images has become a very important issue. This is particularly true when pre- and postevent image comparison is concerned: For this particular application, the requirement of obtaining the earliest possible postevent image imposes the use of data potentially showing strongly different characteristics with respect to the pre-event image. Strongly inhomogeneous image pairs require robust automatic registration techniques. Resolution-independent feature-based registration is naturally preferred over correlation-based registration where data are inhomogeneous. In this letter, we propose a mode-based feature matching scheme, formerly invented for computer vision application and adapted in this letter to pre- and postevent matching. We list a few weak points in the original technique when used for this particular application and illustrate how a significant improvement was obtained by modifying the algorithm. Three real cases of pre- and postevent feature matching on high resolution satellite images are shown and discussed.

This work is devoted to the definition and application of a processing chain for rapid mapping using TerraSAR-X data, The approach is based on a quick extraction of spatial features such as linear and textural elements of the scene, and their combination with the original SAR data. The suitability of such procedure for an operative use is proved by the results shown on a simulated and two real TerraSAR-X data sets.

In this paper, we describe a first approximation to the relevant issue of defining a part of the hyperspectral processing chain in a flexible manner. An ultimate goal of our study is to objectively quantify the impact of different (standard and new) processing stages on the generation of a realistic, user-oriented product in the context of an urban land cover mapping problem by means of hyperspectral data, selected in this work as an application case study for demonstration purposes. Although the proposed study is linked to a specific application domain, our experimental results reveal interesting considerations that may help image analysts in defining customized processing chains based on parameters which can be identified and objectively evaluated a priori, such as available sensor resolution or ancillary information. In addition, our study also demonstrates the importance of incorporating information related to both the spatial and the spectral domain in the different steps that comprise the hyperspectral processing chain; particularly when such chain can take advantage of the combined use of both sources of information as it is the case in the considered urban characterization application.

Abstract—This paper addresses the main goals and objec-tives of the Hyperspectral Imaging Network (HYPER-I-NET), a recently started Marie Curie Research Training Network. The project is designed to build an interdisciplinary research community focusing on hyperspectral imaging activities. The core strategy of the network is to create a powerful interdisciplinary synergy between different domains of expertise closely related to hyperspectral imaging activities in Europe, ranging from sensor design and flight operation to data collection, processing, interpretation, and dissemination. Our main goals in this paper are to present the project to the Geoscience and Remote Sensing community and to provide an overview of the planned activities in each sub-activity covered by the network.

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.

In this paper, the problem of rapid earthquake damage 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 Bam, Iran, and compared with ground-based survey maps

In this letter, the problem of detecting urban road networks from high-resolution optical/synthetic aperture radar (SAR) images is addressed. To this end, this letter exploits a priori knowledge about road direction distribution in urban areas. In particular, this letter presents an adaptive filtering procedure able to capture the predominant directions of these roads and enhance the extraction results. After road element extraction, to both discard redundant segments and avoid gaps, a special perceptual grouping algorithm is devised, exploiting colinearity as well as proximity concepts. Finally, the road network topology is considered, checking for road intersections and regularizing the overall patterns using these focal points. The proposed procedure was tested on a pair of very high resolution images, one from an optical sensor and one from a SAR sensor. The experiments show an increase in both the completeness and the quality indexes for the extracted road network

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

In this paper, the problem of change detection in road networks from SAR images is addressed. Featurelevel 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. As far as the change detection task is concerned, the availability of Synthetic Aperture Radar (SAR) data promises high potentialities, thanks both to the insensitivity of SAR imagery to atmospheric conditions and cloud cover issues and to the short revisit time planned for future SAR-based missions. Hence, multitemporal SAR imagery is expected to play a relevant role, for instance, with respect to ecological and environmental monitoring applications or to disaster assessment and prevention.

SAR images from space-borne platforms have proved to be helpful data for identification of oil spills and other surface anomalies, such as low wind areas, man-made targets, and natural films. The use of fractal dimension, which is related to the concept of surface "roughness", as a feature for classification, improves the detection of anomalies, since enhances texture discrimination. In the particular case of oil slicks, the surface tension of seawater is increased and the surface wave motion is significantly depressed. This effect relatively reduces the sea surface roughness, decreases the radar backscattered energy and enables oil slicks to be discernible from the radar image. Several algorithms may be applied for local fractal dimension estimation, but most solutions are tailored for specific applications and are characterized by estimation accuracies depending on the adopted image model and also on the value being estimated. This paper describes a decision-based fusion approach for local fractal dimension estimation of SAR images of the sea surface. Three different estimation algorithms are considered and the three resulting fractal maps are fused by means of a weighted average. The weights are calculated from the performance characteristics of the three algorithms measured on synthetic fractal surfaces. The experimental results carried out on ERS-2 SAR images prove the effectiveness of the proposed decision-based fusion approach

In this paper, we analyze a multiple sensor data set corresponding to three-dimensional data coming from interferometric radar (InSAR) or laser ranging (LIDAR) measurements. LIDAR and InSAR are now mature technologies, and there are examples of their usefulness for urban area characterization. Unfortunately InSAR measurements show a serious disadvantage in describing built areas, due to problems derived from radar ranging. As a matter of fact, the possibility to have in the same area LIDAR data can reliably help in correcting all these effects. The advantage of LIDAR and InSAR joint use resides in exploiting the higher resolution offered by laser data and comes from the fact that LIDAR data is more expensive, and usually at the same cost we may obtain InSAR data on a much wider area than the one obtainable with a laser scanning survey

In this paper we investigate the use of SAR and multispectral sensors on board of the ENVISAT-1 satellite for urban remote sensing applications. We are interested mainly on the mapping capabilities of these two sensors and provide results for urban land use extraction using ASAR data and urban area definition using MERIS bands

Very high resolution hyperspectral data should be very useful to provide detailed maps of urban land cover. In order to provide such maps, both accurate and precise classification tools need, however, to be developed. In this letter, new methods for classification of hyperspectral remote sensing data are investigated, with the primary focus on multiple classifications and spatial analysis to improve mapping accuracy in urban areas. In particular, we compare spatial reclassification and mathematical morphology approaches. We show results for classification of DAIS data over the town of Pavia, in northern Italy. Classification maps of two test areas are given, and the overall and individual class accuracies are analyzed with respect to the parameters of the proposed classification procedures.

Provides a first assessment of a coregistration technique suitable for multiangle fine spatial resolution synthetic aperture radar (SAR) images. The technique is based on crossroad and road junction extraction and matching and exploits recently introduced road extraction routines for SAR data. These features are matched using relational and geometrical analysis. Results are encouraging and show the possibility to exploit multiangle SAR available from future airborne and satellite missions.

The aim of this paper is to investigate whether fractal maps extracted from sea SAR images are useful for discriminating the sea from other entities or anomalies. Fractal mapping consists of locally estimating the fractal dimension of the image. To this purpose four different methods based on covering and spectral analysis are proposed and compared when applied to real ERS1-2 GEC images. Wind falls, sea and line coast are well distinguishable in the fractal maps. This result clearly shows that the use of image fractal processing is a promising and powerful technique for identifying sea surface anomalies.

This paper is devoted to urban hyperspectral remote sensing. Very high resolution hyperspectral data are used to provide detailed maps of urban land cover, exploiting different classification tools. In particular, multiple classifications and spatial refinement step are used to improve the mapping accuracy. We show results on DAIS data over the town of Pavia, Northern Italy. The four flight lines over the area, kindly provided by DLR in the framework of the HySens project, are partially overlapping. This helps, besides the test of the classification procedure here presented, even to understand the advantages of combining different views of the same area.

In this work we present an image magnification technique aimed to improve the overall accuracy of hyperspectral data classification in an urban area. Furthermore, we discuss how techniques originally introduced for image enhancement in printers may be useful also for remote sensing applications. Finally, we compare different classifiers to look for the one able to exploit as much as possible the enhanced imagery. We find that fuzzy ARTMAP allows obtaining the best results.

In this paper we present and compare different techniques for the fusion of multitemporal SAR and multiband optical images. We consider both neuro-fuzzy and statistical approaches for the exploitation of the contextual information and the classification, and different schemes for the multisensor fusion. The proposed techniques are applied to a set of two multitemporal SAR and a Landsat multiband image of an urban area. Results show that it is possible to fully exploit the potentialities of the two sensors, by appropriately fusing their information. In particular, the proposed schemes are useful to retain at the same time the change detection capability and the best possible classification accuracy, thus they are of practical interest for civil protection applications.

We investigate the use of co-occurrence 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.

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 forecasting 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.

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 1/2 (ERS-1/2) 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.

In this paper, we derive two techniques for the classification of multifrequency/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.

Fractal dimension of the sea surface is strictly related to its roughness, and may thus be helpful in determining the sea state, or where motion-damping oil spills are located. A possible way to determine the fractal dimension of the sea surface is that of performing a fractal analysis of remote sensing images, in particular satellite images, which have the advantage of observing a large area at one time. This paper aims to show the utility of fractal analysis of ERS-1 SAR images, and presents the results obtained by three different algorithms. The considered data was sensed by ERS-1 in the Mediterranean Sea at times and locations suitable for comparison with data coming from the Italian "Sistema Ondametrico Nazionale," an environmental measurement system including a number of buoys carrying accelerometers and communications instruments. The experimental results show some accordance between the buoy data and the ERS-1 fractal analysis outcome, but more data are required to provide statistical support to the conclusions.

This paper is devoted to the introduction of a fuzzy ARTMAP classifier based on a two-step approach. First, a pixel-by-pixel classification is performed, then a kernel-based refinement is applied to the output of the first step. The approach is applied to a multitemporal data set of a urban area, both to improve any single classification and to exploit the extra information carried by more images of the same area in different dates. We show that the fuzzy ARTMAP neural classifier is well suited to handle this kind of data. We discuss also the advantages deriving from the use of a multitemporal data set in urban areas, where many features are stable and so more data make their detection and recognition easier.

This paper deals with a feature fusion technique, especially implemented to characterize street networks extracted from multiple SAR images. The fusion approach may be useful in a number of ways: when applying different road extractors to the same data set, when using different pre-processing algorithms before applying the same road extractor to the same data set, or finally when applying the same road extractor to more images of the same area. The proposed approach applies "AND" and "OR" rules to the street nets to be compared. These operators are either hard or fuzzy ones, according to the way these networks were obtained. Therefore, the method is able to maintain the fuzzy reliability indicators obtained by fuzzy street extraction algorithms, but also to deal with differently classified segments and paths. We show through some examples how the proposed technique improves the results with respect to the "before fusion" street networks, and we also discuss future developments.

The problem of analysing and forecasting the motion of rain structures sensed by weather radar is mostly faced using approaches based on correlation of rain intensity values. Some approaches, however, consider rain structures as a base for the analysis. Of such approaches we considered a neural RBF-based one, of which we recently presented an improved version. The method we develop shows advantages over a linear prediction, while on the other side it is heavy and thus requires some tuning of the parameters to avoid exceedingly long processing times. In this paper we present some facts we discovered about time-saving compromises in tuning parameters and provide some rule-of-thumb guidelines for selecting their values.

In this paper we derive two techniques for the classification of multipolarimetric/multifrequency SAR images, based respectively on a statistical and on a neural approach. Both techniques are especially designed to exploit of the spatial structure of the observed scene, thus identifying homogeneous regions that can be jointly classified. Such techniques are useful when looking at medium to large scale features, like the boundaries between urban and non-urban areas. They are applied to a set of multipolarimetric/multifrequency SIRC images of a urban area, to test their effectiveness in the identification of built up areas. A quantitative comparison of the results achievable with the two techniques is carried out, showing a similar behavior, even if the statistical approach tends to achieve better performance.

Analysis and reconstruction of range images usually focuses on complex objects completely contained in the field of view; little attention has been devoted so far to the reconstruction of simply shaped wide areas like parts of a wall hidden behind furniture pieces in an indoor range image. The work presented in the paper is aimed at such reconstruction. First of all, the range image is partitioned based on depth discontinuities and fold edges. Next, the planes best fitting each of the regions constituting the partition of the image are determined. A third step locates potentially contiguous surfaces, while a final step reconstructs the hidden regions. The paper presents results for reconstruction of the shape of planar surfaces behind arbitrary occluding surfaces. The system proved to be effective and the reconstructed surfaces appear to be reasonable. Some examples of results are presented from the Bornholm church range images.

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.

The authors work on meteorological satellite image archives and provide a novel and useful query-by-shape tool. To this aim, they first present the point diffusion technique (PDT), a fast and efficient method for shape similarity evaluation. Thanks to its very structure, this approach is suitable to handle objects whose shape is not well defined and can be represented by a set of sparse points. PDT is thus suitable for application to similarity-based retrieval from remotely sensed image archives, where shapes are hardly defined but are still among the major features of interest. Moreover, they prove here that PDT is almost as effective as more standard procedures for shape-based database queries, although significantly faster. In other words, it manages to combine retrieval speed and precision, the features of greatest importance for a first remote sensing data prescreening in many applications. Archives of meteorological satellite images are typical examples of very large-sized, remote sensing-based databases with a special attention for shape features. Each meteorological satellite produces terabytes of data every day, a large part of which is not immediately analyzed and ends being stored in archives. The application of PDT to such a database is presented and discussed, and a comparison with a standard method developed for meteorological shape analysis is provided.

The authors present a fuzzy approach to the analysis of airborne synthetic aperture radar (SAR) images of urban environments. In particular, they want to show how to implement structure extraction algorithms based on fuzzy clustering unsupervised approaches. To this aim, the idea is to segment first the sensed data and recognize very basic urban classes (vegetation, roads, and built areas). Then, from these classes, we extract structures and infrastructures of interest. The initial clustering step is obtained by means of fuzzy logic concepts and the successive analyses are able to exploit the corresponding fuzzy partition. As a possible complete procedure for urban SAR images, they focus on the street tracking and extraction problem. Three road extraction algorithms available in literature (namely, the connectivity weighted Hough transform (CWHT), the rotation Hough transform, and the shortest path extraction) have been modified to be consistent with the previously computed fuzzy clustering results. Their different capabilities are applied for the characterization of streets with different width and shape. The whole approach is validated by the analysis of AIRSAR images of Los Angeles, CA.

The so called point diffusion technique is introduced. It allows one to quickly evaluate the similarity between two figures defined by "clouds" of points. The new technique is compared with widespread modal matching approaches, showing similar results with respect to the efficiency of the queries in an image database, and using sensibly less CPU-time.

We present the application of a simplified shape analysis technique based on a modal representation of the object shape, and which is useful for improving the efficiency and effectiveness of shape-driven searches in image databases. The proposed method computes the representation of an object by means of modes very similar to the deformation modes of a mechanical system, but in a numerically more stable way than the usual finite-element method approach. Moreover, to make the technique for the visual search more effective, many different definitions of similarity indexes are introduced and discussed. The problems related to the comparison between objects represented by a very different number of feature points are also discussed. Finally, to prove the effectiveness of the approach, the indexes are studied in a simple case study (a small database of character shapes). However, their performance on a larger image database is also addressed, as well as the ability of the method to efficiently assess the problem of retrieving images similar to a user-defined sketch.