Alberta’s oil sands mining operations rank among the largest human-made structures globally. Monitoring through the use of Synthetic Aperture Radar (SAR) and Multispectral satellite imaging is an indispensable strategy in attaining sustainable development and mitigating deforestation in the third-largest verified oil reserves worldwide. This paper introduces a novel approach for cost-effective and reliable monitoring of deforestation caused by oil sands mining, avoiding cumbersome methods. It focuses on observing forest/non-forest areas affected by Suncor Energy Company’s mining assets in Alberta, using a combination of SAR and Multispectral satellite remote sensing. Radar images from Sentinel-1B and Multispectral images from Sentinel-2A were analyzed with SNAP 8.0 and QGIS within a time series from June 2017 to June 2020, providing detailed information to monitor better the potential environmental impact of oil sands mining activities in Canada. The Sentinel satellite system offers several advantages, including near-global coverage, elevated spatial resolution for detecting small-scale deforestation instances, and the ability to track temporal and dynamic changes through time-series analysis. Additionally, the system’s open data policy promotes accessibility, collaboration among researchers, and innovative deforestation monitoring applications. The research results hold potential value for decision-makers, enhancing the efficiency and sustainable development of Suncor’s mining operations.
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Oceanic internal waves are an active ocean phenomenon that can be observed, and their relevant characteristics can be acquired using synthetic aperture radar (SAR). The locations of oceanic internal waves must be determined first to obtain the important parameters of oceanic internal waves from SAR images. An oceanic internal wave segmentation method with integrated light and dark stripes was described in this study. To extract the SAR image characteristics of oceanic internal waves, the Gabor transform was initially used, and then the K-means clustering algorithm was used to separate the light (dark) stripes of oceanic internal waves from the background in the SAR images. The regions of the dark (light) stripes were automatically determined based on the differences between the three classes, that is, the dark stripes, light stripes, and background area. Finally, the locations of the dark (light) stripes were determined by shifting a given distance along the normal direction of the long side with the minimum bounding rectangle of the light (dark) stripes. The best segmentation results were obtained based on the intersection over the union of the images, and the accuracy of segmentation was verified. Furthermore, the effectiveness and practicability of the proposed method in the light and dark stripe segmentation of SAR images of oceanic internal waves were illustrated. The proposed method prepares the foundation for future inversion studies of oceanic internal waves.
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As Earth observation technology has advanced, the volume of remote sensing big data has grown rapidly, offering significant obstacles to efficient and effective processing and analysis. A convolutional neural network refers to a neural network that covers convolutional calculations. It is a form of deep learning, and convolutional neural networks have characterization learning characteristics, which can classify information into different data. Remote Sensing Data Processing from various sensors has been attracting with more information in Remote Sensing. Remote sensing data is generally adjusted and refined through image processing. Image processing techniques, such as filtering and feature detection, are ideal for dealing with the high-dimensionality of geographically distributed systems. The geological entity is a term in geological work which refers to the product of geological processes that occupy a certain space in the Earth’s crust and are different from other materials. They are of different sizes and are divided into different types according to their size. It mainly focuses on improving classification accuracy and accurately describing scattering types. For geological entity recognition, this paper proposed a Deep Convolutional Neural Network Polarized Synthetic Aperture Radar (DCNN-PSAR). It is expected to use deep convolutional neural network technology and polarized SAR technology to explore new methods of geological entities and improve geological recognition capabilities. With the help of Multimodal Remote Sensing Data Processing, it is now possible to characterize and identify the composition of the Earth’s surface from orbital and aerial platforms. This paper proposes a ground object classification algorithm for polarized SAR images based on a fully convolutional network, which realizes the geological classification function and overcomes the shortcomings of too long. The evaluation of DCNN-PSAR shows that the accuracy of the water area is showing a rising trend, and the growth rate is relatively fast in the early stage, which directly changes from 0.14 to 0.6. Still, the increase is slower in the later stage. DCNN-PSAR achieves the highest quality of remote sensing data extraction.
The aim of the TOPVOYS project supported by the MarTERA ERA-Net Cofund program within the European Commission is to advance and implement analyses tools and decision support system for voyage optimisation. Based on marine weather analyses and forecasts combined with near real time satellite-based observations of wind, wave and surface current conditions as well as sea surface temperature fields the best shipping route are examined. The proposed approach aims to identify the optimum balance between minimisation of transit time and fuel consumption as well as reduction of emissions without placing the vessel at risk to damage and or crew injury. As such it is compliant with the International Maritime Organization guidelines [6] for ship routeing to keep the traffic smooth and avoid accidents, notably in the presence of unfavorable marine meteorological conditions. The tool performances will be demonstrated both in post-voyage analyses and real time operations for the North Atlantic Ocean crossings, voyages from Europe through the Mediterranean Sea and the Suez Channel to the Far East (e.g. China, South Korea) and voyages around Southern Africa.
The European Space Agency Sentinel-1 satellites provide good resolution all weather SAR images. We describe algorithms for detection and classification of ships, icebergs and other objects at sea. Sidelobes from strongly reflecting objects as large ships are suppressed for better determination of ship parameters. The resulting improved ship lengths and breadths are larger than the ground truth values known from Automatic Identification System (AIS) data due to the limited resolution in the processing of the SAR images as compared to previous analyses of Sentinel-2 optical images. The limited resolution in SAR imagery degrades spatial classification algorithms but it is found that the backscatter horizontal and vertical polarizations can be exploited to distinguish icebergs in the Arctic from large ships but not small boats or wakes.
This article applies radar interferometry technologies implemented in the ENVI SARscape and SNAP software environment provided by the processing of data from the Sentinel-1 satellite. The study was carried out based on six radar images of Sentinel-1A and Sentinel -1B taken from September 2017 until February 2018 with an interval of one month and on the radar-module of the already mentioned SNAP software. The main input data for solving the considered problem are radar images received from the satellite Sentinel-1B on the territory of Stebnyk-Truskavets for six months with an interval of one month. Monitoring of the Earth’s surface using radar data of the Sentinel-1A with a synthesized aperture is implemented with the application of interferometric methods of Persistent Scatterers and Small baselines interferometry for estimating small displacements of the Earth’s surface and structures. The obtained quantitative and qualitative indicators of monitoring do not answer the processes that take place and lead to vertical displacements the six months but do provide an opportunity to assess the extent and trends of their development. The specification in each case can be accomplished by ground methods, which greatly simplify the search for sites with critical parameters of vertical displacements which can have negative consequences and lead to an emergency.
This paper describes a synthetic aperture radar system for tactical-level imagery intelligence installed on board an unmanned aerial vehicle. Selected results of its tests are provided. The system contains interchangeable S-band and Ku-band linear frequency-modulated, continuous wave radar sensors that were built within a frame of a research project named WATSAR, conducted by the Military University of Technology and WB Electronics S.A. One of several algorithms of radar image synthesis, implemented in the scope of the project, is described in this paper. The WATSAR system can create online and off-line radar images.
W niniejszym artykule opisano wybrane aspekty implementacji dwóch projektów realizowanych aktualnie przez GMV Polska. Projekt BIBLOS-2, stanowiący kontynuację i rozszerzenie projektu BIBLOS, ma za zadanie wykonanie implementacji gotowych algorytmów wykorzystujących karty graficzne do optymalizacji czasu wykonania programu, oraz zdefiniowanie architektury i implementację modułów dla symulatorów misji z aktywnymi i pasywnymi systemami radarowymi. Drugim z opisanych projektów jest projekt „Scatterometer Ground Processor Simulator & Tools GPP in MetOp-SG”, którego celem jest implementacja, integracja oraz przetestowanie algorytmów symulatora systemu radarowego wykorzystywanego do skaterometrii (ang. scatterometry). Projekt realizowany jest na potrzeby misji MetOp-SG we współpracy z firmą Airbus Defence and Space dla Europejskiej Agencji Kosmicznej i Europejskiej Organizacji Eksploatacji Satelitów Meteorologicznych (EUMETSAT).
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This article describes selected aspects of implementation of the two projects currently being implemented at GMV Poland. The BIBLOS-2 project, which is a continuation and extension of the BIBLOS project, apart from the parallel implementation of algorithms using passive optics to observe the surface of the Earth, is primarily aimed at defining and implementing algorithms for radar observation of the Earth, both for passive and active radar systems. The second project described in this article is „Scatterometer Ground Processor Simulator & Tools GPP in MetOp-SG”, which aims to implement, integrate, and test algorithms for the Scatterometer within the Metop-SG mission. This project is realized in cooperation with Airbus Defense and Space for the European Space Agency and the European Organization for the Exploitation of Meteorological Satellites (EUMETSAT).
The paper presents methods of on-line and off-line estimation of UAV position on the basis of measurements from its integrated navigation system. The navigation system installed on board UAV contains an INS and a GNSS receiver. The UAV position, as well as its velocity and orientation are estimated with the use of smoothing algorithms. For off-line estimation, a fixed-interval smoothing algorithm has been applied. On-line estimation has been accomplished with the use of a fixed-lag smoothing algorithm. The paper includes chosen results of simulations demonstrating improvements of accuracy of UAV position estimation with the use of smoothing algorithms in comparison with the use of a Kalman filter.
The paper presents a method of calculation of position deviations from a theoretical, nominally rectilinear trajectory for a SAR imaging system installed on board of UAV. The UAV on-board system consists of a radar sensor, an antenna system, a SAR processor and a navigation system. The main task of the navigation part is to determine the vector of differences between the theoretical and the measured trajectories of UAV center of gravity. The paper includes chosen results of experiments obtained during ground and flight tests.
The modelling of FMCW SAR systems, due to long signal duration time, commonly used start-stop approximation for pulsed radars causes errors in the image. Continuous motion of the radar platform results in additional range-azimuth couplings and range walk term that should be considered in processing of signal from this type of radar. The paper presents an analysis of the following algorithms: Time Domain Correlation (TDC), Range Doppler Algorithm (RDA), and Range Migration Algorithm (RMA). The comparison of the algorithms is based on theoretical estimation of their computation complexity and the quality of images obtained on the basis of real signals of FMCW SAR systems.
Synthetic aperture radars (SAR) allow to obtain high resolution terrain images comparable with the resolution of optical methods. Radar imaging is independent on the weather conditions and the daylight. The process of analysis of the SAR images consists primarily of identifying of interesting objects. The ability to determine their geographical coordi-nates can increase usability of the solution from a user point of view. The paper presents a georeferencing method of the radar terrain images. The presented images were obtained from the SAR system installed on board an Unmanned Aerial Vehicle (UAV). The sys-tem was developed within a project under acronym WATSAR realized by the Military University of Technology and WB Electronics S.A. The source of the navigation data was an INS/GNSS system integrated by the Kalman filter with a feed-backward correc-tion loop. The paper presents the terrain images obtained during flight tests and results of selected objects georeferencing with an assessment of the accuracy of the method.
PL
Lotnicze zobrazowania terenu realizowane za pomocą radaru z syntetyczną aperturą pozwalają na uzyskanie wysokiej rozróżnialności porównywalnej z rozróżnialnością metod optycznych, mając jednocześnie nad nimi przewagę w postaci niezależności obserwacji od warunków pogodowych czy pory dnia. Proces analizy tak uzyskanych zobrazowań składa się przede wszystkim z identyfikacji interesujących obiektów. Możliwość określenia ich współrzędnych geograficznych pozwala na znaczące zwiększenie użyteczności tego rozwiązania z punktu widzenia potencjalnego użytkownika. W artykule przedstawiono metodę georeferencji zobrazowań terenu otrzymywanych za pomocą radaru z syntetyczną aperturą, który zainstalowano na pokładzie bezzałogowego statku powietrznego. System taki opracowano w ramach projektu WATSAR, zrealizowanego przez Wojskową Akademię Techniczną i WB Electronics S.A. Źródłem danych nawigacyjnych był system INS/GNSS, zintegrowany metodą filtracji pośredniej, z korekcją wstecz. W artykule zamieszczono radarowe zobrazowania terenu uzyskane podczas badań poligonowych systemu oraz wyniki georeferencji wybranych obiektów wraz z oceną dokładności wyznaczanego położenia.
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W artykule przedstawiono wyniki pracy badawczo-rozwojowej prowadzonej przez Zespół Technik Radiolokacyjnych Instytutu Systemów Elektronicznych Politechniki Warszawskiej w ramach projektu SARape (Radar z Syntetyczną Aperturą pracujący na platformach bezzałogowych we wszystkich warunkach pogodowych, ang. Synthetic Aperture Radar for all weather PEnetrating UAV application) finansowanego przez Europejską Agencję Obrony. Pracę tę można zaklasyfikować do dynamicznie rozwijanej na świecie dziedziny zwanej teledetekcją (ang. remote sensing), której jednym z elementów jest radiolokacja wykorzystująca technikę syntetycznej apertury (ang. Synthetic Aperture Radar – SAR). Technika ta umożliwia tworzenie zobrazowań terenu o wysokiej rozdzielczości za pomocą radaru pokładowego w praktycznie dowolnych warunkach pogodowych, ograniczonych jedynie poprzez zdolności lotne platformy. Celem projektu SARape było opracowanie nowatorskiego systemu radarowego pasma milimetrowego (94GHz) przeznaczonego do instalacji na pokładzie niedużej platformy bezzałogowej (UAV – ang. Unmanned Aerial Vehicle). Opracowany system radarowy umożliwia tworzenie zobrazowania terenu o bardzo wysokiej rozdzielczości (do 15 cm) w czasie rzeczywistym. Zrealizowany system SAR został przetestowany w warunkach rzeczywistych. Jako nośnik radaru wykorzystano przy tym mały ultralekki samolot (ang. ultralight). Otrzymane wyniki potwierdziły możliwość uzyskania wysokiej rozróżnialności zobrazowań SAR w czasie rzeczywistym z wykorzystaniem metod przetwarzania sygnałów opracowanych przez zespół z Politechniki Warszawskiej.
EN
In the paper results of a project realized by Radar Techniques Research Group from the Institute of Electronic Systems, Warsaw University of Technology are presented. The project, called SARape (Synthetic Aperture RAdar for all weather PEnetrating UAV application), was sponsored by the European Defence Agency. The topic of this project concerned remote sensing using synthetic aperture radar technique. This technique allows for creation of high resolution images using onboard radar, almost independently of the weather conditions. The main limitation is the minimum weather conditions of the radar platform. The aim of the project was to develop a mm-wave radar (operating at 94 GHz) which could be mounted on a UAV (Unmanned Aerial Vehicle). The developed system enables to create real-time imagery with resolution up to 15 cm. The radar has been tested in real flight conditions using ultralight aircraft. The obtained results confirmed the real-time capabilities of high-resolution imagery.
In this paper, air pollutants concentrations for NO2, NO, NOx and PM10 in a single monitoring station are predicted using the data coming from other different monitoring stations located nearby. A cascade feed forward neural network based modeling is proposed. The main aim is to provide a methodology leading to the introduction of virtual monitoring station points consistent with the actual stations located in the city of Catania in Italy.
The paper presents implementation and results of the application for displaying SAR (Synthetic Aperture Radar) imagery operating in real-time. The application performs SAR imagery formation and displays results in real-time after receiving of preprocessed data via an SAR processing application. The application was used in SARape (Synthetic Aperture Radar for all weather penetrating UAV application) project founded by the European Defence Agency. The real-time operation is achieved thanks to implementation based on multithreading.
Artykuł poświęcony jest problematyce kompresji surowego sygnału SAR (ang. Synthetic Aperture Radar) z wykorzystaniem układów FPGA (ang. Field Programmable Gate Arrays). Artykuł składa się z dwóch zasadniczych części. Część pierwsza zawiera prezentację cech sygnału SAR istotnych dla kompresji oraz przegląd wybranych algorytmów kompresji. Na część drugą składa się opis autorskiej implementacji algorytmu BAQ (ang. Block Adaptive Quantizer) w strukturach FPGA oraz wyniki badań symulacyjnych.
EN
This article describes some issues of raw SAR signal compression using FPGA devices. It can be divided into two parts. First part contains presentation of SAR signal properties, which are important in the terms of signal compression and review of selected algorithms. Another part presents author’s own BAQ algorithm implementation in FPGA and simulation results.
In the article the problem of the azimuth ambiguity in synthetic aperture radar (SAR) images and its genesis are presented. A method of suppressing the ambiguities by utilization of Doppler-sensitive signals is proposed, and the necessary modifications to the SAR synthesis algorithm are discussed. The SAR system parameters required for an optimal performance of the method are discussed and simulation results are presented.
Two modifications of the range-Doppler algorithm (RDA) have been proposed to solve problems of SAR platform motion instabilities. First, the multi-look processing based on the RDA with an extended Doppler bandwidth has been introduced for correction of radiometric errors. Second, the RDA has been modified to perform SAR image formation on short-time acquisition intervals to use it in a recently-developed local-quadratic map-drift autofocus (LQMDA) method. The performance of the methods is illustrated with experimental data obtained by airborne SAR systems.
Radar z syntetyczną aperturą SAR (ang. Synthetic Aperture Radar) pozwala na uzyskiwanie zobrazowań terenu o bardzo wysokiej rozróżnialności. Zasada działania takiego systemu opiera się na wykorzystaniu wzajemnego ruchu sensora i obserwowanych obiektów, co skutkuje zmianami fazy początkowej odebranych sygnałów echa od obserwowanych obiektów, zależnymi od położenia obiektu oraz od prędkości nosiciela. Dokładna znajomość funkcji zmiany fazy umożliwia syntezę zobrazowania SAR. W artykule przedstawiono algorytm estymacji prędkości platformy radaru z syntetyczną aperturą bazujący na dopasowaniu krzywej odległościowej sygnału surowego do założonego modelu przy wykorzystaniu metody najmniejszych kwadratów.
EN
Synthetic aperture radar (SAR) is a technique that allows for achieving very high resolution radar images. The principle of the SAR image synthesis is based on mutual movement between radar and the observed objects that generates changes in the received signal initial phase. Those changes depend on target position and radar velocity. Thus the exact value of the radar platform velocity is needed in order to synthesize the SAR iamge. A platform velocity estimation algorithm based on fitting the SAR range curve to a assumed movement model with Least Mean Squares method is presented in this article. Simulation and experimental results are presented.
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Data from the space-borne synthetic aperture radar (SAR) aboard the Envisat satellite and MODIS spectroradiometers on board the Terra/Aqua satellites, and the high resolution Sea Ice-Ocean Model of the Baltic Sea (BSIOM) have been used to investigate two upwelling events in the SE Baltic Sea. The combined analysis was applied to the upwelling events in July 2006 along the coasts of the Baltic States, and in June 2008 along the Polish coast and Hel Peninsula. Comparisons indicated good agreement between the sea surface temperatures and roughness signatures detected in satellite imagery and model results. It is shown that BSIOM can simulate upwelling events realistically. The utilization of modelled hydrodynamics and wind stress data together with SAR and SST information provides an extended analysis and deeper understanding of the upwelling processes in the Baltic Sea. During the active phase of upwelling when the wind is strong, the resulting coastal jet is controlled by vorticity dynamics related to depth variations in the direction of the flow. Typical upwelling patterns are related to the meandering coastal jet and thus associated with topographic features. The longshore transport of the coastal jet is of the order of 104 m3 s-1, and the offshore transport at the surface is of the order of 103 m3 s-1,, which respectively correspond to the total and largest river runoff to the Baltic Sea.
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