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Feature extraction techniques play an essential role in classifying and recognizing targets in synthetic aperture radar (SAR) images. This article proposes a hybrid feature extraction technique based on convolutional neural networks and principal component analysis. The proposed method is used to extract features of oil rigs and ships in C-band synthetic aperture radar polarimetric images obtained with the Sentinel-1 satellite system. The extracted features are used as input in the logistic regression (LR), support vector machine (SVM), random forest (RF), naive Bayes (NB), decision tree (DT), and k-nearest-neighbors (kNN) classification algorithms. Furthermore, the statistical tests of Kruskal-Wallis and Dunn were considered to show that the proposed extraction algorithm has a significant impact on the performance of the classifiers. © 2022 SPIE. open access

Currently, there is a significant increase in the number of violations of the airspace by small aircrafts in civilian protected areas, such as airports, crowded areas, industrial enterprises, etc. In this regard, the task of detecting, determining the coordinates and tracking air objects crossing the guarded perimeter and used both for video filming and for the delivery of goods becomes especially urgent. Often used locating tools located on the territory have blind spots, or observation of objects is difficult, which is mainly observed in dense urban development. The paper presents the advantages and disadvantages of radar and optical monitoring systems allowing for real-time observation. The problem of processing frames of the video stream of optical and radar images for the purpose of tracking, as well as extrapolating the coordinates of the position of unmanned aerial vehicles in the event of a temporary loss of visibility, including full shading, partial shading or other types of natural interference. To solve this problem, a method is proposed based on the use of a set of adaptive filters tuned to various types of motion of objects in order to predict their position.

Investigation of the mixing processes in river confluence zones is an important problem, particularly in application to ecological monitoring of rivers and inland waters. Such processes can be very complex and can affect riverbed deformation, mixing and pollution transport, etc. A typical example is a confluence zone (CZ) of the Volga and Oka rivers (Russia). In this paper, the Volga/Oka CZ signatures in satellite optical (Sentinel-2 MSI) and radar (Sentinel-1 SAR) images are analyzed for the period of ice/snow melting in early spring. First, the ice melting appears immediately downstream of the meeting point of the rivers. At the early stages of the ice cover melting the CZ is seen as a narrow dark band in the optical images and as a bright band in radar images because of wet snow/ice cover within the CZ. The faster melting in the CZ than outside can be associated both with the emissions of warm water from factories/thermal power plants and with water transportation towards the CZ by weak transverse currents in the river flows. The enhanced radar backscatter in the CZ can be associated with an increase of the snow/ice moisture and thus an increase of snow/ice cover roughness. With air temperature increase, an open water patch with fragmented ice appears transformed later into a long polynia. The polynia is manifested as a dark band in radar imagery presumably due to the suppression of wind waves associated with fragmented ice. This happens even at moderate wind velocities normally exceeding the threshold of wind wave excitation.

It is well known that marginal ice zones are characterized by different forms of initial stages of ice such as, e.g., grease and fragmented ice which act as surface wave absorbers and thus affect microwave radar backscattering. As a result, mapping of boundaries between solid ice and open water areas using radar may become rather complicated. Another aspect of the problem of wind wave damping due initial stages of ice is that the areas of strong wave damping due to ice can be erroneously interpreted as surface pollutions in radar imagery. Studies of wave damping due to ice floes are still insufficient, and relations between the floe geometry and wave damping are poorly established. The motivation of this study is to improve our understanding of the process of wave damping due to ice floes for elaboration of physical models of wave damping. New wave tank experiments were carried out to investigate the damping of regular mechanically generated waves and of irregular wind waves due to drifting floe imitators (washing sponges) as well as for the case of stationary, non moving floes. Dependencies of the damping coefficient on wave frequencies for regular and wind waves for different floe sizes and different areas occupied by the floes were obtained. One of the most interesting results was that the damping coefficient indicated a local maximum when the floe size was about half the wave length. A physical interpretation of the results was given, based on the analysis of floe movement under the action of the orbital wave motion taking into account the floe added mass.

Nowadays the importance of Unmanned Aerial Vehicle (UAV) based sensing technologies is globally recognized. Indeed, thanks to the ability of investigating large areas in a very short time and at very reduced cost, the UAV sensing technology has been widely used in multiple application contexts, including security and surveillance inspections, environmental monitoring, geology, agriculture, archeology and cultural heritage. Actually, the widespread remote sensing technologies mounted on-board UAVs are mainly optical, thermal and multi-spectral sensors, which are passive technologies designed to measure the signals emitted into the optical and (near and far) infrared portions of the electromagnetic spectrum thus providing useful 2D and 3D information about the observed scene. Radar systems represent an important complementary solution. Indeed, radar system is an active system which transmits and receives electromagnetic signals at microwave frequencies, thus offering the advantages of performing inspections in free space and through-obstacle scenarios. However, UAV based radar imaging is not yet a well consolidated technology due to the significant challenges related to the acquisition modality and data processing strategies. Since both transmitting and receiving radar units must be installed on-board the UAV, this introduces not trivial issues related to payload and assets constrains. Moreover, in order to obtain reliable and easily interpretable images, a high precision UAV trajectory reconstruction must be deployed. As a contribution to this topic, an UAV imaging system prototype based on a microwave tomographic approach was recently proposed. Experimental tests at the Archaeological Park of Paestum (SA) has been recently carried out. During the survey, the UAV platform was piloted in path-planning mode, i.e. “autonomous flight” on a predefined rectangular grid and a novel imaging strategy which exploits multiple measurement lines has been developed.

In this work a novel approach to collision avoidance radar is presented. Leveraging the chipsets currently in development for the automotive industry, with an operational band of 77 to 81 GHz a new frontend has been developed using a distributed frequency swept antenna. The frequency swept antenna is able to steer the beam based on the transmit frequency. Multiple sub-array elements are distributed across the bumper of a vehicle increasing the aperture size of the system for improved beam resolution, thereby leading to better system sensitivity. To this end, system modeling was implemented to study the tradeoff between system sensitivity or range and power, gain, antenna aperture size and number of sub-array elements. The sub-arrays were designed, optimized, manufactured, and characterized using a conformal, flexible, low loss high frequency Rogers 3003 material. The measured far field patterns of the developed antenna array demonstrated consistent angular steering characteristics of -4° to 8° over the frequency from 75 to 85GHz with minimum reflection. The developed sub-array elements are cascaded and then synchronized using electronically controlled, high resolution, wideband, low loss W-band phase shifters. To drive such a large distributed array, we also focused on the development of high-resolution or analog phase shifters with 360 degrees coverage from 77-81GHz. The phase shifter chip was designed based on three-vector method and manufactured by leveraging SiGe foundry run. RF integration of the fabricated chips along with the control circuit was also conducted to demonstrate fully phase control over the band of interest. The packaged antenna subarrays and phase shifters are integrated together to form a distributed array. Through synchronization, coherent operation of the system can be established, enhancing the angular resolution of the system. The developed antenna array will be integrated with a Frequency Modulated Continuous Wave (FMCW) transceiver for applications of automobile radars.

A concept for radar imaging based on multiple incoherent millimeter wave antennas is presented. The goal is to use 3D-Imaging, where the object moves along a line and passes a linear array of antennas. To reduce the hardware complexity, a novel radar principle is introduced, which does not require a coupling between TX and RX, but a reference reflector. The RX performs just a RF-power measurement for each frequency step in a SFCW setup. After preprocessing the raw data, any focusing algorithm can be used to obtain the 2D image. Multiple 2D images can be combined to 3D with backprojection.

UMass Lowell BTTC researchers are investigating the use of a high resolution 75 GHz radar to identify the internal defects of the fiberglass filaments in wind turbine blades. Developing the acquisition and image analysis software, the team is fabricating a laboratory based test radar and anechoic chamber for performing the preliminary measurements. Central to the team's challenge is the development of an implementable design for a manufacturing plant’s wide area inspection system. The measurements and system design will be presented.

Doppler radar is the main remote sensing equipment to monitor severe convective weather which has significant threats to social and economic activities. It is important to accurately predict the time and location of severe weather events. In this study, we use a deep learning technique to predict severe weather events based on radar images. Firstly, we transform the prediction problem into a binary classification problem and use Generative Adversarial Networks (GANs) to construct a classifier. Then Doppler radar images are used to train the model. The critical success index, probability of detection, and false alarm ratio are used to evaluate the prediction results. The experimental results show that the GANs model provides satisfactory results.

Fires, floods, water pollution and landfills are among some of the core environmental problems today. In the present work we consider the processes related to temperature pollution from landfills for municipal waste storage. Landfills where a temperature anomaly occurred as a result of a fire are also considered. Optical images from the multispectral instrument (MSI) of the Sentinel 2 platform and radar (SAR) data from the Sentinel 1 platform of the Copernicus program of the European Space Agency were used. The heat channel of the Landsat 5 - 7 (ETM) and Landsat 8 (OLI / TIRS) sensors was used to calculate the surface temperature from the landfill. By using orthogonation of satellite images, the dynamics of the individual components of the earth's surface - vegetation, soil and moisture - was traced. A combination of optical and radar imaging has been used to showcase the potential of this method by which landfills can be identified. Disturbance index was used as a quantitative indicator of the studied areas. Satellite data from different seasons and years have been selected to monitor the dynamics of thermal pollution from the landfills.

The presence of plastic litters in the environment (both onshore and offshore) has long been identified as a threat for the ecosystems. Remote sensing provides an efficient and quick access to concentration areas of plastic litters. Due to composition similarities between plastics and hydrocarbons, absorption wavelengths are expected for plastics around 1730 and 2310 nm. Kuhn’s Hydrocarbon Index can be used for the detection of plastic targets on a hyperspectral aerial product. Spectral comparison algorithms (Spectral Angle Mapper and Spectral Information Divergence) as well as a spectral unmixing algorithm are used. J. Bioucas-Dias SISAL and MVSA algorithms are adapted for the automation of endmembers selection. Those previous results obtained in a controlled environment were expanded upon by using previous plastic detection algorithms as well as an index-based method (Flooding Debris Index; Biermann et al., 2020) on Sentinel 2 multispectral products. Using a cut-off value on the results of the FDI in southern Spain, onshore greenhouses and offshore plastic debris were detected. A supervised classification complemented the method, based on optical properties of five hundred greenhouses roof and five hundred non-plastic targets. In addition to plastic detection on land, we assessed temporal distribution of threadlike film presence at the sea surface on radar images in the North Atlantic. Wind conditions derived from Sentinel-1 images also help to understand the detection conditions. Spectral band configurations of free constellation of Earth observation satellites are not covering plastic absorption peaks which represent a significant limitation for their detection. The spatial resolution of commercial satellites (i.e Worldview-3) may be better adapted to plastic waste detection that have size distributions lower than open access satellite constellations. Even if offshore plastic detection with open access constellations remain challenging, image processing techniques may improve the detection

We propose the use of 3D-printed helix antennas for millimeter-wave radar imaging. This concept is promising for a number of reasons: Additive manufacturing involving 3D-printing is a relatively cost efficient fabrication technique and offers increased geometrical freedom of design compared to conventional manufacturing processes. From an imaging point of view, using helix antennas is advantageous because of the circular polarization the antennas emit. That way, imaging thin dipole-like structures is possible regardless of their orientation. In contrast, imaging systems using linearly polarized antennas are unable to image dipoles orientated orthogonally to their polarization direction. Radar systems using circular polarization additionally enable polarimetric imaging and decomposition. In security screening this can achieve a higher classification accuracy in discriminating threat objects and reduce false alarms. Furthermore, the thin helix antennas (typical coil diameter: ca. 1.5 mm) can be mounted very closely to each other, which is interesting for array design. A security screening example was investigated for demonstration: A cardboard box with metallic and dielectric threat objects was screened at 70 GHz – 90 GHz by a quasi-monostatic synthetic aperture radar consisting of two 3D-printed helix antennas, one right-hand and one left-hand circularly polarized. As a reference, the same object was screened with split-block linearly polarized horn antennas. With the proposed setup, the resolution of the reconstruction images was comparable to that of the reference system. However, the circular polarization was able to depict thin structures in a better fashion than the linearly polarized reference system.

Landslides or rockfalls in mountainous areas in Western Greece are a continuous hazard and thus, observation either in situ or through satellite data is of great necessity. In order to study landslides or rockfalls a very accurate representation of the relief is a prerequisite. In now days diverse remote sensing satellites provides digital surface elevation models (DSMs) with global coverage. The specific data sets present the advantages of covering large areas in a very low cost in comparison with the surveys in the field. In addition, high resolution DSMs enable the analysis of topography with high levels of detail and consequently geomorphometric approach becomes more accurate for studying landlsides or rockfall events. Information which can be extracted from geological and topographic maps in GIS format such as the slope angle distribution, deduced from high resolution DSMs, are substantial in rockfalls survey. In the present work, the accuracy of freely available DSMs are under control for landslides areas in Achaia prefecture in NW Peloponnese. The accuracy of the DSMs in the broader area is investigated using reference points of certified elevation while for the landslide area ground control collected with differential GNSS receiver are used. More specifically, free available DSMs as TanDEMX, ASTER GDEM, SRTM DEM with 30m and 90m spatial resolution, ALOS AW3D30 DEM, DSM from photogrammetric airphoto processing and DSM produced by Interferometric processing of radar images, are under examination in the current study. Furthermore, diverse statistical parameters such as the 2D Root Mean Square Error or the percentile value are computed and presented. The purpose of the aforementioned procedure is to detect the most accurate DSMs which are appropriate for landslide of rockfall monitoring.

Remote sensing of wind waves propagating in the areas covered by ice at initial stages of its formation in the marginal ice zone using radar is an urging problem for mapping ice boundaries and their dynamics. Another aspect of the problem is to investigate possibilities of discrimination between the marginal ice zone and oil spills in radar imagery. This study is focused on modeling the damping of surface waves due to ice floes in laboratory and field experiment. Laboratory experiments were carried out is a container filled with pure water in order to exclude the effect of surfactant films on wave damping. The container was mounted on a vibration table, so that surface gravity-capillary waves (GCW) could be parametrically generated in the container when the amplitude of the vibrations exceeded some threshold level. The wave damping coefficient could be retrieved when measuring the threshold. The floes in experiment were modeled using thin plastic pieces of two different sizes, the relative square of the “floe” coverage of the water surface was controlled in experiment. The dependences of the damping coefficient at different relations between the surface wavelength and the floe dimensions as functions of the floe coverage area were obtained. It is obtained that the damping of gravity-capillary waves in the presence of floes comparable in size with GCW can be one to two orders of magnitude greater that the wave damping due to inextensible film. Preliminary field experiments have been conducted on the Gorky Water Reservoir using a research catamaran vessel of the Institute of Applied Physics. Plywood pieces with sizes several times smaller that the studied surface wavelengths were used as imitators of ice floes and were deployed in between the catamaran hulls. Surface waves propagating between the halls were generated mechanically by a vertically oscillating motor boat. The amplitude of attenuating surface waves due to the “plywood floes” was measured with wire gauges mounted at the bow and the stern of the catamaran. The damping distance due to ice floes obtained in the field experiment was estimated as about 10 wavelengths thus indicating that that wave suppression due to the floes was essentially stronger than the viscous wave damping for clean or contaminated water surface. Wave damping observed both in the laboratory and field experiments can be comparable with the wave damping due to crude oil/oil emulsion films, so the problem of discrimination between, e.g. grease ice and oil spills in radar imagery can be nontrivial.

Sea ice plays a major role in our planet’s climate. It’ acts as a reflector of solar energy, mainly in spring and summer. Sea ice covered with fresh snow can reflect 75-90% of solar energy, the open sea reflects just 5-15%. Sea ice acts as an insulator in autumn and winter. This insulating effect limits the amount of both heat and moisture the ocean loses to the atmosphere. The declining sea ice disrupts the climate, societies and fauna of Polar areas, but encourages the econcmic and industrial development. The relevance of this study is related to current trends in the use of remote sensing in solving problems of a different nature in environmental monitoring. The sea ice was analyzed and mapped according to the European Space Agency data (ESA), acquired by sensors of Sentinel-1 SAR (Synthetic Aperture Radar), Sentinel- 2MSI (Multi Spectral Instrument), Sentinel- 3 and GIS. The subject of the study is to demonstrate the dynamics, during the summer season from 2015 to 2019, around the coastline of Livingston Island, New Shetland Islands in Antarctica and Longyearbyen in the Arctic. Changes in environmental objects are indicated by radar images through different processing approaches. The results clearly show that sea ice melting can be best recorded by using SAR data through the C-band. The results obtained are data in the form of thematic maps showing the spatial reflectance of sea ice and its dynamics over time.

Three-dimensional (3D) remote imaging may improve target characterization and help improve tracking and aim-point maintenance. Digital holography (DH) has been shown to be very effective in generating 3D images of objects using frequency diversity. The digital holographic nature of the process can eliminate the need for imaging lenses when the pupil-plane is used for the detection plane. In fact, this approach is the optical analog of the widely used frequencymodulation (FM) radar imaging. The current implementation of the holographic FM 3D imaging involves forming a set of holographic 2D images each at a different temporal frequency and then inverse Fourier transform to recover the actual 3D image. In this paper we present results from both laboratory and simulations of a coherent 3D holographic imaging method based on continuous frequency chirping of a laser. Laboratory 3D imaging results were obtained in both the pupilplane using a spatial heterodyne method, and in the image-plane using a temporal heterodyne method.

Surface penetrating imaging technology reconstructs target images that conform to visual perception by interpreting radar data, which reflects the target's geometry and electromagnetic scattering characteristics directly. For surface penetration imaging, various sources of clutter cause failure of image interpretation, therefore clutter suppression is the key to obtain target information accurately. Though clutter suppression approaches aiming at the strong clutter make the target image prominent enough for imaging process, the weak clutter with energy close to the target affects the imaging quality. In this paper, a nonlinear gain control method is proposed for the suppression of weak clutter and improving the contrast of the imaging by non-linearly weakening the data below the threshold and enhancing the data above the threshold. Experiments show that the proposed method can effectively improve the imaging quality.

This article is devoted to the problems of radar sensing. Herein, we have considered the tasks of modeling and recognizing radar images. The modeling technology was based on the independent creation of terrain models and objects, which were then integrated into a three-dimensional (3D) scene. This approach enabled the operative creation of a number of image variants of different classes. Recognition methods and algorithms were based on the use of the so-called conjugacy index as a measure of proximity. At the same time, support subspaces of the minimum dimension were formed by vectors, components of which were samples of the radar image. Problems of higher accuracy of recognition due to a division of classes into subclasses and a combination of the support subspace method with the neural convolutional networks were considered.

One of the primary challenges in helicopter flight is landing in a degraded visual environment (DVE). In a DVE, the situational awareness of the pilot is inhibited by natural phenomena such as rain, snow, fog, etc., or aircraft induced phenomena such as brownout or whiteout. Typically, the pilot is assisted by feedback from a lidar system, but if the particles are dense enough, the lidar is unable to provide useful information about the terrain and other obstacles. Nonetheless, radar can be used to create useful imagery of the surrounding area, albeit at a reduced resolution, as it has the ability to penetrate precipitation, dust, and other obscurants. In this paper, a forward-looking synthetic aperture radar (FLSAR) concept, which can form three-dimensional (3-D) imagery from a 1-D array, is proposed. A frequency domain imaging algorithm, the polar format algorithm (PFA), is investigated for its applicability to the FLSAR geometry. We show that a wavefront curvature correction (WCC) procedure is required to compensate for the far-field approximation made in the PFA which is not valid at the frequencies and operational ranges under consideration. A filter transfer function for WCC for the FLSAR geometry is derived. Finally, the effectiveness of the derived filter transfer function is demonstrated in simulated 2-D imagery.

Recent analyses have yielded non-parametric techniques that perform automatic compensation of defocusing effects in synthetic aperture radar (SAR) imagery, which arise from atmospheric refraction of the radar waveforms. The temporal variation of the delay and bending arising from atmospheric refraction can be modeled using power- law spectra. The present study investigates the effects of applying different values of the perturbation amplitude of the refraction-induced fluctuations. This investigation reveals that the subject refraction-based autofocus techniques give sharp scene refocus when applied to measured Ku-band SAR image data for various values of the perturbation amplitude of the modeled power law spectra.

In this paper, we describe the recent development of new algorithms applied to short-range radar imaging. Facing the limitations of classical backpropagation algorithms, the use of techniques based on Fast Fourier Transforms has led to substantial image computation accelerations, especially for Multiple-Input Multiple-Output systems. The necessary spatial interpolation and zero-padding steps are still particularly limiting in this context, so it is proposed to replace it by a more efficient matrix technique, showing improvements in memory consumption, image computation speed and reconstruction quality.

Three-dimensional (3-D) radar imaging is an important task underpinning many applications that seek target detection, localization, and classification. However, traditional 3-D radar imaging is computationally cumbersome and needs a large memory space to store the cubic data matrix. These challenges have hindered 3-D radar imaging development and limited its applications. Owing to the sparsity of the radar image of typical targets, the data size could be significantly reduced by exploiting compressive sensing and sparse reconstruction techniques. These techniques prove important to mitigate the sidelobe levels that arise from successive Fourier-based processing. Towards this end, we first perform polar reformatting to the 2-D data matrix in azimuth and range. Then, a series of images over different values of the vertical variable z are generated by using different focusing filters. Sparse optimization is afterwards applied to the 3-D data cube to produce high-resolution, significantly reduced sidelobe 3-D image. Compared with the conventional 3-D radar imaging methods, the proposed method, in addition to high fidelity images, requires fewer data measurements and offers computationally efficient processing. Numerical simulations are provided to evaluate the effectiveness of the proposed method.

The laser anti-collision imaging radar is composed of the following key technologies: high beam quality, high repetition frequency, high power laser shooting machine technology, high sensitivity-high precision receiving technology, high efficiency-fast and linear optical scanning technology, and fast image processing and obstacle recognition and warning technology, etc. This paper studies on the key technology above.

Laser imaging radar is the combination of four technologies, laser, radar, optical scanning control, and high-speed computer processing. It is extensively used in both military and civilian applications. Non-scanning imaging radar, which works without a scanning device, is characterized by high frame rate, wide field of view, and high reliability and has become the focus of research in recent years. The objective of this study is to firstly, elaborate the structure of non-scanning imaging laser radar and basic principle of plane array ranging. Next, it focuses on the latest developments in the non-scanning laser 3D imaging radar systems. Thereafter, it categorizes and summarizes various non-scanning laser 3D imaging radar systems, such as flash, polarization modulation, and gain modulation. From the comprehensive coverage of these systems presented in this study, it can be inferred that the laser 3D imaging has the advantages of high resolution, high energy utilization, and high signal-to-noise ratio. In addition, a discussion of their disadvantages is presented. Finally, this study concludes with some challenges and possible future research directions in this field.

This paper proposes a combination detection and fusion method for targets on sea surface, which is based on radar and infrared channels, this method is also suitable for sea surface target combination detection and fusion of radar and visible light camera at the same time. Radar detection has the advantages of full-time, all-weather, and large detection range, but the ability to acquire target details is poor. In comparison with radar, infrared detection has higher angular resolution and can acquire the target's thermal radiation characteristics, which allows for a wealth of detailed information. However, the infrared field of view is small. Based on the advantages and disadvantages of these two detection methods, the radar is used for target detection. Then, according to the azimuth angle and the distance of the target in the radar image, the infrared camera is guided to observe the target. The combined detection method of radar-guide-infrared can combine the advantages of radar’s long detection range, large coverage area with the high resolution and rich detail information of infrared, which covers the details missing of radar detection and the small view field of infrared. After the radar directs the infrared to the target, we will get the infrared image of target. If there is more than one target, it is necessary to merge the data of the radar channel and infrared channel, so that the targets detected by the radar correspond to the targets of the infrared detection. However, due to the different error sources of radar and infrared detection, the error characteristics of the two channels are greatly different, which makes the target fusion of radar and infrared channel difficult. In order to deal with this problem, first of all, the Extend Kalman filter models for radar channel and infrared channel are designed respectively, which are on the base of the imaging characteristics of radar and infrared. After the filtering of the target position information obtained from radar and infrared channels, the random error of the target and the error of target solution will be reduced. What’s more, the velocity information of the targets will be solved at the same time. After that, the error distribution mathematical models of the radar and the infrared channels will be established. Combine the error distribution models with the filtering results and the relationship between the radar coordinate system and the infrared coordinate system, the probability distribution of the targets in radar image and infrared image will be obtained. Finally, according to the probability of the target distribution, numerical method is used to compute the matching matrix of the targets in radar and infrared images. After dealing with the matching matrix, the target fusion of radar channel and infrared channel is accomplished. After that, according to the target matching result, the radar and infrared data are fused to obtain higher-precision target position information.

Noise in SAR imagery was produced due to different backscatter response from the objects in the earth surface. This resulted in a grainy image, usually known as “salt and pepper” noise, which reducing the capability to identify object from radar imagery. Therefore, speckle filtering was conducted to decrease this noise from SAR imagery. This study aims to assess the performance of different types of speckle filters especially when used to construct forest aboveground biomass (AGB) model from Sentinel-1 data in Barru Regency, South Sulawesi. There were 4 filters used in this study i.e. Frost, Gamma-MAP, Median, and Refined Lee. AGB model was constructed by using dual polarization C-band SAR of Sentinel1 data and ground inventory plots. 23 plots were collected in the field and the allometric equation was used to calculate the biomass value of the field survey data then cross validation models were generated by using biomass value and backscatter data from VV and VH polarization. Quality control was performed by comparing the coefficient of determination (R2 ) of those filters. The result shows that Frost filter especially on VH polarization was chosen as the bestfit model to estimate the AGB based on higher value of R2 (0.3464158) and RMSE (33.5231). The result demonstrated Frost filter as the best filter for retaining and/or enhancing the backscatter signal in Sentinel-1 data to be used in vegetation bio-physical modelling.

Compressive sensing (CS), which breaks the classical Nyquist limit and does not require a high sampling rate, can be used to recover a complete signal by using much less information and an optimization strategy. Further, it reduces the calculations required for signal reconstruction, and requires simpler signal collection and processing than other sensing techniques. It can reduce the data rate of high-resolution imaging radar systems and the amount of sampling, storage, and transmission data effectively. In this study, we first describe the basic theory model of CS. Then, we review the latest developments in radar imaging algorithms based on CS, followed by a comprehensive review of CS applications in high resolution radars, including SARs/ISARs, through-the-wall (TTW) radars, MIIMO radars, and ground-penetrating (GP) radars. This review highlights the importance of CS in simplifying radar hardware, overcoming data limitations, and improving the radar imaging performance. Next, an in-depth discussion and analysis of the advantages and disadvantages of CS-based radar imaging are presented. Finally, we highlight some of the challenges and research directions in this field.

One of the main issues that concerns mankind today is the problem of domestic waste and how it affects climate change, air pollution and the environment. In the present work the heat pollution from the waste disposal site is tracked at various time points. The waste disposal site near Vidin was selected for the purpose of the research. Optical satellite data from the Sentinel 2 multi-spectral instrument (MSI) and synthetic – aperture radar (SAR) data from the Sentinel 1 platform of the Copernicus program of the European Space Agency were used. The Landsat 5 -7 (ETM) and Landsat 8 (OLI / TIRS) sensors were used to calculate the surface thermal pollution of the waste disposal sites. Orthogonalization of satellite imagery was made to trace the dynamics of the main components of the Earth's surface - vegetation, moisture and soil. On this basis, a correlation is made to trace the link between the different components of the Earth's surface at different time points. Climate data on average air temperature, evapotranspiration, radiation and rainfall was used and a comparative analysis of surface temperature from landfill and climatic data was made.

Grassland occupies a large proportion of utilised agricultural area, especially in mountainous regions. Despite its importance current and reliable data on grassland yields and cutting frequencies with a sufficient spatial coverage are lacking. Both are essential for optimizing the use of grassland, nature conservation and policy consultation. Model approaches for the assessment of grassland yields take cutting dates and frequency into account despite environmental and cultivation factors. The European Earth Observation programme Copernicus provides large quantities of spatial and temporal high resolution data collected by a set of Sentinel satellites. The freely and openly accessible Sentinel-1 radar data form a valid basis for automated satellite and ground data processing methods to detect cutting events. These cutting frequencies are a fundamental information source for further analysis – the computation of grassland yields with different model approaches. In this study we like to present our overall approach integrating and analyzing data of different sources. A comparison between two different automated data processing methods to detect cutting frequencies from radar satellite data in three different regions in Bavaria is included. The common statistical detection represents a robust and reliable way by analysing time series of Sentinel-1 radar images of the same acquisition geometry with time intervals of 6 days. In contrast, machine learning techniques offer the opportunity to increase the accuracy and limit cutting dates to more precise time intervals.

River confluence is a ubiquitous phenomenon which plays an important role in river dynamics, mixing processes, pollution transport etc. It can be often visually observed that two converging rivers continue to flow as two parallel weakly mixing streams separated by a relatively thin transition region – a mixing zone (MZ), which can reach out for some distance downstream the junction apex. A typical example of a river confluence is the merging of the Volga and Oka rivers (Russia). This paper presents some new results on the Volga/Oka rivers confluence based on both ‘in situ’ measurements of hydrological characteristics in the MZ and on satellite observations. During the ‘in situ’ measurements a large set of data regarding velocity fields, chlorophyll-a concentration, water temperature, turbidity, parameters of organic surface films etc. was obtained. It is found that significant differences between the hydrological characteristics of the Volga and Oka flows are observed at sufficiently large distances downstream the junction apex (about 10 river widths or more); and the mixing zone remains quite narrow. Film sampling and further analysis of the surfactant films in the MZ were carried out. It is shown that the surface tension decreases and the film elasticity grows in the foam/slick bands separating the Volga and Oka flows thus indicating the increased concentration of surfactants in the bands. Satellite images of the Volga-Oka MZ are collected indicating that the confluence area is clearly seen in satellite optical imagery (MSI Sentinel-2 satellite) due to strong difference in color between the flows. The radar imagery shows the mixing zone in the form of a slick/foam band (SAR Sentinel-1) which manifests on the water surface due to enhanced damping of short wind waves.

The main aim of this research is to examine the interpretation possibilities of images from the constellation of Sentinel satellites, i.e. Sentinel-1, and Sentinel-2, and to assess their suitability for the analysis of objects from category 01 Airfields according to the standardization document STANAG 3596. In the presented research analysis of chosen airbases in Europe was presented. In the course of the research, image data, both electro-optical and microwave data, were pre-processed appropriately, i.e. radiometric and geometric corrections, and filtrations were applied, in order to finally analyze and evaluate interpretations. In the research radar images in the C band, which are recorded by sensors placed on the satellites of the Sentinel-1 constellation, as well as electro-optical images acquired by multispectral sensors placed on the satellites of the Sentinel-2 constellation, were used. In addition, the fusion of microwave and electro-optical data was made to verify the usefulness of data integration for reconnaissance purposes. Then the Sentinel data was combined with high-spatial-resolution data from Google Earth, in order to improve the quality and to verify the usefulness of the integration of multitemporal data in the process of imagery interpretation and object detection. The analysis consisted of assessing the interpretability of images by the possibility of distinguishing specific objects on it. The results were next evaluated using high- spatial resolution open-source imagery data from Google Earth, and open source GIS data. The conducted analyses showed that the use of Sentinel image data in recognizing objects at airports seems to be justified due to their availability, although these should be objects of considerable size. In order to detect smaller objects, multi-resolution data integration should be applied, as showed in the research.

Through wall indoor surveillance and monitoring is an emerging area of research and developments in many real-life applications, including security and search and rescue. In this context, it is important to deploy remote sensors able to glean information about the presence of people in indoor areas. Radar devices based on the exploitation of electromagnetic signals represent an attractive sensing modality. When operating at frequencies from hundreds of MHz to few GHz, they are capable to penetrate common construction materials enabling the detection of people in inaccessible environments. Two different types of surveillance radars are considered in this paper. The first one exploits the synthetic aperture concept with the aim to detect, locate, and track multiple subjects in the scene. The second one illuminates the scene from a fixed position and exploits the Doppler effect. Despite the limited information available with respect to aperture radars, Doppler radars allow the detection of moving targets behind walls and inside enclosed structures. This work presents a brief overview of recent developments in the field of radar data processing for indoor monitoring together with their assessment by means of numerical and experimental tests.

System solutions for commercial applications such as autonomous driving, augmented reality, medical imaging, and security imaging, exploit active illumination. In these applications, the active source is used to provide photons but also to code and decode relevant information such as range or spectral response. The wavelengths of choice range from visible to millimeter waves depending on the application and associated requirements. Across these wavelengths, the targets range from Lambertian to specular. For single element and scanned systems, ranging is commonly modeled using conventions borrowed from the radar and antenna community. Staring and scanning systems that facilitate resolution in the cross-range are modeled using conventions borrowed from the synthetic aperture radar community or the passive imaging community. All the borrowed conventions, however, make assumptions about the size and nature of the target in relationship to the illumination and wavelength, unresolved versus resolved and Lambertian versus specular. These assumptions are relevant for the calculation of system signal to noise ratio and resolution; therefore, they should be carefully considered when adopting the conventions. Examples of systems where modeling falls between active radar and passive imaging include wide band Terahertz array imaging systems and solid state lidar systems. This paper generalizes and bridges the models used by the active radar community and the passive imaging community. We apply the model to a wide band terahertz array imaging system enabled by terahertz array technology recently developed at imec. The model is validated using simulated measurements from a two-dimensional terahertz array.

Conventionally, resolution characterization of an imaging radar is performed by means of analyzing the diffraction limited point-spread-function (PSF) pattern of the radar. Such an analysis is straightforward and can easily be implemented at microwave and millimeter-wave frequencies using simple point-scatter targets. However, it poses significant challenges at submillimeter-wave (or THz) frequencies due to the strong scattering response of secondary objects that are used to align the PSF targets for imaging at these frequencies. As a result, the reconstructed PSF patterns suffer from artifacts caused by the secondary objects present in the background. In this work, we present the use of the acoustic levitation principle to obtain the PSF characterization of a 340 GHz stand-off imaging radar. We show that using a water droplet acoustically levitated at the focal point of the 340 GHz imaging radar, high-fidelity PSF characterization of the radar is achieved, revealing the resolution limits of the radar while exhibiting good signal-to-noise ratio (SNR).

Many existing methods of object detection, including edge detection, blob detection, and background subtraction (implemented in libraries such as OpenCV) have proven to be enormously successful when applied to many types of video datasets. However, detecting objects over water presents challenges that are unique and not easily accommodated for by pre-existing algorithms available in popular image processing libraries. In this paper, existing approaches are brie y reviewed, and the challenges encountered in above-water video datasets are highlighted. A recently proposed approach to object detection in radar images - a novel, pixel-intensity statistic based thresholding approach | is then reviewed. In this paper, this approach has been successfully applied to EO/IR datasets as well, extending the implementation to ensure success when applied onto other types of image datasets.

During recent years of developing a near-range ground-penetrating radar for explosive hazard detection, re- searchers at the U.S. Army Combat Capabilities Development Command Army Research Laboratory have been focused on developing receiver design parameters that optimize system performance. In general, a radar de- signer often aims to reduce the bandwidth of a receiver because it will result in a reduction in noise floor and analog-to-digital converter requirements. However, if receiver blanking is employed (i.e., the process of pulsing transmission and reception so that they do not occur simultaneously), the system’s chosen bandwidth can negatively impact the effectiveness of receiver blanking. That is to say, the step response of the system (which approximately occurs during the receiver turn-on stage) is dictated by the receiver’s bandwidth. The response can be characterized by its delay and, more importantly, by its rise (or fall) time. The rise and fall time can manifest in a smearing (or ramping) at the near and far boundaries of the illuminated scene of interest. This could lead to missed detections of close-in or far-out targets. The aforementioned is discussed in detail, and the ramifications on near-range synthetic aperture radar image formation is presented.

The task of detecting and locating landmines, unexploded ordnances (UXO) and improvised explosive devices (IEDs) is still a major challenge up to the present day. Problems such as the distance between the hazardous area and the measurement system as well as the differentiation between the target of interest and the surrounding soil are of importance in the development of the sensor system. Various types of radar based systems have been developed over the last decades to solve these problems. Compared to other methods ground penetrating synthetic aperture radar (SAR) has the ability to scan large areas from a safe standoff distance in a relatively short time. In this paper, two different imaging radar systems are compared. The first one is a ground-based SAR (GB-SAR) developed at German Aerospace Center (DLR). The other system is an unmanned aerial vehicle-based SAR (UAV-SAR) from the University of Ulm. The presented data originates from a joint campaign using the same measurement scenarios.

Knowing the grain angle of structural wood members relative to the direction of loading is important in estimating their mechanical performance. The strength of a structural wood member can vary greatly depending on the orientation of grain, making the understanding of grain angle an essential requirement for machine grading and for structural analysis. Condition assessment of structural wood members using non-destructive evaluation (NDE) techniques (e.g., microwave/radar, ultrasonic, stress wave, and X-ray) is a major approach for existing wood structures. Among existing NDE techniques, synthetic aperture radar (SAR) imaging offers technical advantages include remote sensing and subsurface sensing. The objective of this paper is to use SAR imaging to quantify the change in grain direction of various size wood dowels relative to the imaging radar. Two wood specimens were produced each 14 in. long with diameters of 1.25 in. and 1.5 in. Each wood specimen was placed vertically inside an anechoic chamber and imaged at different orientations using a 10 GHz SAR system. It was found that the integrated SAR amplitude and amplitude distribution were affected by the grain orientation of the wood specimens. Further analysis was conducted by estimating the area of contour slices of the SAR images taken in the amplitude direction and were used to confirm the findings.

Detecting underground/subsurface metallic objects such as landmines and IEDs (improvised explosive devices) using efficient and effective inspection techniques is crucial in demining and mine clearance missions. The use of microwave/radar sensors in remote sensing represents a new approach to reduce demining risks and to improve efficiency. To achieve the goal, electromagnetic signatures of underground metallic objects must be thoroughly studied. The objective of this paper is to use synthetic aperture radar (SAR) imaging to investigate the size effect of an underground metallic object buried in SAR images at three ranges (15 cm, 30 cm, and 60 cm). Three different sizes (diameters = 7.6 cm, 15.2 cm, and 20.3 cm) were used and buried inside a container (sandbox) filled up with dry sand. A 10-GHz SAR imaging radar sensor was applied to generate all SAR images inside an anechoic chamber. It was found that SAR amplitude decreases with the increase of radar range. In addition, reconstructed distribution of SAR amplitudes depended on the size of underground steel disk specimens. SAR amplitudes (maximum and integrated) were related to the range and modeled by empirical equations.

The paper deals with subsurface imaging via radar systems mounted onboard aerial platforms. Specifically, the attention is focused on a radar prototype installed on a small unmanned aerial vehicle (S-UAV), previously proposed by few of the authors. In particular, the challenges in terms of electromagnetic modeling and flight dynamics knowledge and control are here tackled. In this frame, an ad-hoc designed data processing strategy is presented; this strategy involves a pre-processing step and a reconstruction step. The pre-processing is performed in time domain and, beyond filtering procedures commonly exploited in radar imaging, involves a procedure devoted to compensate flight altitude variations and to account for the S-UAV trajectory, which is estimated by processing measurements collected by an onboard GPS receiver. In addition, the reconstruction of the investigated scenario is performed by means of a microwave tomographic approach based on a linear model of the electromagnetic scattering and the concept of equivalent dielectric permittivity for the propagation path. This latter allows us to properly face the imaging of buried objects, while avoiding the mathematical complexity introduced by the presence of the air-medium interface. Accordingly, the imaging is faced as a linear inverse scattering problem formulated in the spatial domain similarly to the case of a homogeneous scenario and, thanks to the concept of equivalent permittivity, depth and horizontal position of buried objects are retrieved properly. This is corroborated by means of a numerical analysis accounting for synthetic data.

A Ka-band microwave photonic imaging radar demonstrator with 10.02 GHz-bandwidth is proposed and experimentally demonstrated. Continuous linear frequency waveform is optically generated in the transmitter and processed in the receiver. The range resolution of the demonstrator is tested to be 1.68 cm. Out-field tests while demonstrator works at inverse synthetic aperture radar (ISAR) and synthetic aperture radar (SAR) mode are carried out to image different targets.

This research presents the results of a survey on the extent of the affected areas in the municipality of Bregovo from the flood of the Timok River, which occurred on 11.03.2018. The application of SAR and optical data for assessment of the spatial and temporal characteristics of the occurred flood is the objective of this paper. This methodology applies orthogonal transformation of different multispectral images from Sentinel 2 mission combined with SAR data from Sentinel 1. The assessment was made on the basis of the orthogonal transformation’ components of the bands from different multispectral Sentinel 2 imagery: Tasseled Cap Transformation TCT-brightness, TCT-wetness, TCT-greenness. Indicators of quantitative changes in areas affected by the floods have been obtained. Satellite images from Sentinel 1- SAR and Sentinel 2-MSI satellite missions, orthophotography, terrestrial and in-situ terrain data from the Bregovo municipality affected by flooding were used. The processed satellite images are from different sensors and are selected by different dates before and after the day of the natural disaster. Pseudocomposite radar images with different polarization (vv,vh) were used to clarify and more precisely visualize the territorial coverage of the flood in the surveyed area. Various normalized quantitative differentiation indices (vegetation, humidity, vapors, etc., NDGI,) are generated after image processing. Results are presented for correlation between the values obtained for the different data types. On the basis of the obtained data and results, a comparative analysis of the dynamics of the changes occurred as a result of the disaster was carried out and a quantitative assessment of the changes occurred and respectively the registered negative environmental impacts in the territorial extent of the flood.

Synthetic Aperture Radar (SAR) is an active microwave imaging radar to obtain target image. Due to the character of robust performance under all-weather conditions and features of reflection of radio waves from various surfaces, SAR is widely used in military and civilian fields. In contrast to optical images, due to the physical principle of operation of the radar, the multiplicative noise is present in the radar images. This noise, also called speckle, significantly complicates the classification of objects in the image. The object orientation normalization is applied in addition to filtration using traditional classifiers such as SVM. Comparative analysis of traditional classifiers with different filters and methods of normalizing the orientation of the object has been formed previously. The attempt to use neural networks in various areas, where traditional methods dominated, it is a high research trend now. Because of this, various architectures of neural networks have been proposed to enhance the efficiency on synthetic aperture radar automatic target recognition (SAR-ATR) and obtained state-of-art results on targets classification in many articles. Most of the results were obtained using a widely used Moving and Stationary Target Acquisition and Recognition (MSTAR) database. Therefore, MSTAR database is used in this paper for easy comparison of the results obtained. In this paper, we compared the effects of various methods for preprocessing radar images for traditional methods such as SVM, decision trees and methods based on neural networks.

Remote sensing of the Earth allows to receive information of medium, a high spatial resolution from space vehicles and to conduct hyperspectral measurements. In many cases, waste disposal sites (WDS) is not legal. So, it is very important to use the system for automatic detection such places using satellite images. In this paper, a model of the automated space monitoring system for the presence of waste disposal sites is developed. The proposed system includes the following blocks: a database of WDS; a subsystem for detecting unauthorized WDS; a subsystem for monitoring the design, operation and reclamation rules for existing WDS; a subsystem for estimating the parameters of the WDS and their impact on the environment; a subsystem of satellite monitoring. We propose a method of detecting high-rise buildings landfills, such as municipal dumps and solid waste, according to a radar image (the height of the ground level). For system design, we use the apparatus of discrete orthogonal transformations. The impact of the WDS influence on agricultural crops is analyzed, based on the data of the Earth remote space sensing based on the orthogonal transform. The purpose of the work is the modeling of an automated space monitoring system for the presence of waste disposal facilities using regularization method in the problem of filtering of space images from the noise stored in the archives. As a result, the proposed method demonstrates good accuracy in detection the solid waste disposal site on real satellite images.

Vortex structures of different types are common in the Southeastern Baltic Sea. Intensive western winds, a complex coastline and an absence of steady currents make this region very appealing for studying the nature of vortex processes. These processes are clearly identified from Space. We present results of a multi-year satellite monitoring of mesoscale and submesoscale vortex structures in the Southeastern part of the Baltic Sea and of supporting field studies. An important part of our work was accumulation of remote sensing data as the first step in evaluation of circulation patterns and vortex structures presented on the sea surface. An analyzed remote sensing data include color composite images from MSI Sentinel-2, OLI Landsat-8 and ETM+ Landsat-7 as well as radar images from Sentinel-1 and Radardsat-2 that also provide a powerful tool for an identification of circulation processes on the sea surface. Continuous monitoring of the Southeastern Baltic Sea with an analysis of satellite visual and radar images show that the Gulf of Gdansk is the most frequent area with mesoscale and submesoscale eddies. They appear in this area mostly in summer and mainly under the atmospheric influence. Some eddies remain stable for at least of 8 days and could be easily tracked on color composite images. The other area of the vortex structure was determined to the north of the Cape Taran. In summer months from 2014 to 2018, we performed oceanographic concurrent experiments in the southeastern part of the Baltic Sea. Field studies of spatial and temporal characteristics of vortex structures proved results found by an analysis of satellite images.