Top PDF Remote Sensing of Epibenthic Shellfish Using Synthetic Aperture Radar Satellite Imagery

Remote Sensing of Epibenthic Shellfish Using Synthetic Aperture Radar Satellite Imagery

Remote Sensing of Epibenthic Shellfish Using Synthetic Aperture Radar Satellite Imagery

Figure 2. The top row shows examples of photos of the different substrate classes studied in the 1-m 2 plots. The bottom row shows corresponding examples of the measured height after rasterization of the point clouds at a resolution of 5 mm. The integral equation model, or IEM [18], is commonly used to predict backscatter from surface parameters in intertidal environments (root mean square height, correlation length and dielectric constant) and SAR configuration (polarization of microwave signal, wavelength and angle of incidence) [14,38–40]. In this study, we used an extended version of the IEM that takes into account the phase effect in Green’s function; as a result, this version provides much more accuracy in bistatic scattering and also includes multiple scattering [41]; an elaborate description of the model can be found in Fung and Chen [42]. For this study, we used a spectrum with an exponential autocorrelation function. High moisture contents are typical for shellfish habitats due to the high amounts of silt in the sediment. We used a large number of samples (n = 175) of the upper 3 cm of the surface from a field campaign in the Wadden Sea in 2013 to determine that the average volumetric moisture content of the sediment during low tide is 0.45 (±0.09) cm 3 /cm 3 . Assuming this average value and taking into account the grain-size distribution of the sediment, a dielectric constant of ε = 29.31 + 12.72i and 35.67 + 9.55i was calculated for the X-band and C-band, respectively, following Hallikainen et al. [43]. The validity range of the IEM is given by RMSz/L < 0.4 and k × RMSz < 3, where k is the wavenumber (2 × π/λ). Using the IEM, we simulated radar backscatter over a range of RMSz and L found in the field campaign for co- and cross-polarized channels in X- and C-band.
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Remote Sensing of Epibenthic Shellfish Using Synthetic Aperture Radar Satellite Imagery

Remote Sensing of Epibenthic Shellfish Using Synthetic Aperture Radar Satellite Imagery

(polarization, incidence angle and wavelength) or surface specific (local slope, root mean square of the height RMSz, correlation length L and the relative permittivity ε) [14]. However, surface roughness in terms of RMSz is found to be the most important factor in bare intertidal areas [14]. The length scale of shellfish shells is in the right order of magnitude (centimeters) to effectively affect backscatter of C- and X-band microwave signals, and some authors have shown that both C- and X-band microwaves are sensitive to surface roughness induced by epibenthic shellfish. Choe et al. [15] showed that polarimetric descriptors (including Freeman-Durden target decomposition, cross-polarized ratio, co-polarized correlation and co-polarized phase difference) from fully-polarized Radarsat-2 (C-band) and ALOS PALSAR (L-band) data can be used to pick up the roughness signatures created by oysters. In their study, the influence of the incidence angle on the backscatter in oyster reefs is small, but the difference between oyster reefs and mudflats was most pronounced at larger incidence angles [15]. Dehouck et al. [16] used TerraSAR-X data in combination with optical information to classify intertidal mudflats. Gade et al. [17] used TerraSAR-X data to locate shellfish based on temporal statistics of multiple data acquisitions and also noted that shellfish beds were clearly visible across a range of incidence angles. However, it is unclear how accurate SAR-derived shellfish maps actually are; furthermore, it is not known whether SAR data can be used to distinguish between different reef-forming epibenthic shellfish species (mussels vs. oysters) and whether the backscatter signal allows shellfish densities (cover) to be quantified. To develop a widely applicable method for monitoring shellfish beds, the use of single data acquisition with single or dual polarization would be preferred, as many radar sensors, including Sentinel-1, TerraSAR-X and CosmoSkyMed, typically acquire single or dual polarized data.
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Despeckling of synthetic aperture radar 
		satellite imagery using various filtering techniques

Despeckling of synthetic aperture radar satellite imagery using various filtering techniques

Remote sensing is an acquisition of reliable information about a phenomenon or environment by the remote sensors without being physically in contact with it [1]. Images obtained using remote sensing are used in different applications like land cover mapping, oceanography, updating of geographical databases, urban studies, natural disasters, [2] etc., Images are sensed remotely with the help of ground borne or airborne or space borne sensors by detecting the electromagnetic energy scattered from or emitted from or reflected by the earth’s surface [1]. The ground borne and airborne sensors has limited spatial resolution. Airborne sensors are very costly. Space borne sensors are cost-effective and covers large geographical areas with periodical re-visit of the same area [1].
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Comparative detections of oil spill using multimode radarsat-1 synthetic aperture radar satellite data

Comparative detections of oil spill using multimode radarsat-1 synthetic aperture radar satellite data

Most scientists have shown great interest in the huge maritime environmental damage due to oil slicks, which have increase pollution effects greatly. Space-borne RADARSAT-1 SAR images are used to monitor and control oil slicks, however, the main challenges lies in the difficulties inherent in discriminating between oil spills and look–alikes. According to Maged and Hashim (2005) both appears as a dark spot in SAR data. Also according to Alpers and Hühnerfuss (1988); Trivero et al., (1998), the existence of an oil layer on the sea surface damps the small waves which increase the thickness of the top film and this significantly decreases the measured backscattering energy resulting in darker areas in SAR imagery. The European remote sensing satellite (ERS) task is an example of SAR.
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On the remote sensing of oceanic and atmospheric convection in the Greenland Sea by synthetic aperture radar

On the remote sensing of oceanic and atmospheric convection in the Greenland Sea by synthetic aperture radar

sors. One set of images exhibits mottled signatures showing some similarities to signatures reported to originate from atmospheric convection cells [cf. Mitnik, 1992; Ufermann and Romeiser, 1999b]. For the quantitative interpretation of such signatures and their inversion into wind or current variations, it is vital to determine the atmospheric or oceanic origin of the processes causing them. We show by various means that the observed signatures can only result from the presence of atmospheric convection cells, not from oceanic ones. In the second test case, the presence of an oceanic convective chimney is obvious from in situ data, but an available coincident SAR image does not exhibit any visible signatures of it. We show by numerical simulations that this is consistent with theory, since the SAR signatures to be expected from the observed feature are too weak to become visible in the existing imagery. Finally, we discuss the potential of recent and upcoming dual-polarization and high-resolution spaceborne SARs to detect oceanic convec- tion features in the Greenland Sea and to identify and interpret SAR signatures of oceanic and atmospheric con- vection features unambiguously.
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Synergy between synthetic aperture radar and other sensors for the remote sensing of the ocean

Synergy between synthetic aperture radar and other sensors for the remote sensing of the ocean

The monitoring of the sea surface by synthetic aperture radar (SAR) from space has made enormous progress over the last decades so that today a variety of oceanic processes can be identified with a high degree of confidence from SAR images alone. However there remain cases where SAR signatures are ambiguous and additional information is needed to interpret an image. Since it is usually difficult to find simultaneously acquired in-situ data, the easiest approach to this problem is to use satellite data from other sensors to assist the image analysis. By this means, the advantages of each data type can be combined while some of the disadvantages of one sensor can be compensated for by the benefits of another. Some important measurement characteristics in this context are spatial resolution, sensitivity to atmospheric processes (e.g. cloud cover), swath width of the sensor, comprehensiveness of the measured property and accuracy.
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Detecting Methane Ebullition In Winter From Alaskan Lakes Using Synthetic Aperture Radar Remote Sensing

Detecting Methane Ebullition In Winter From Alaskan Lakes Using Synthetic Aperture Radar Remote Sensing

Introduction The purpose of this appendix is two-fold: 1) to document the methods I used to analyze synthetic aperture radar (SAR) data in a geographic information system (GIS) to estimate methane (CH4) emissions in thermokarst lakes, as outlined in Chapter 2, and 2) to highlight some ‘best practice’ approaches that I developed while using SAR in a GIS environment to study lake- source CH4 emissions. Analyzing SAR data in a geospatial environment using commercial GIS software is a relatively new practice that was greatly facilitated in 2005 when the Alaska SAR Facility (ASF, and since renamed to the Alaska Satellite Facility) unrolled a free software tool suite in 2005, called ‘Convert’, to ingest SAR data and export a geotiff in a map projection. About the same time, GIS developers from the Leica company added the functionality of importing SAR data to their GIS, ERDAS Imagine, particularly in the .D/.L Level 1 processed data format provided by the ASF. The ability to use SAR data in commercial GIS software applications such as ESRI’s ArcMap, ERDAS Imagine and ENVI has created the opportunity for scientists to add SAR as another tool to their scientific investigations by comparing SAR values with field data and with other geospatial data layers.
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Wetland Monitoring and Mapping Using Synthetic Aperture Radar

Wetland Monitoring and Mapping Using Synthetic Aperture Radar

4. Trends in wetland mapping and monitoring with SAR As shown in the previous section, spaceborne SAR remote sensing technology is recognized as essential tool for effective wetland observation. With the presence of global warming and its associated risks on Earth systems, there is an expressed interest in increased temporal and spatial resolution of satellite measurements. Thus, a trend toward increased temporal and spatial resolution of SAR imagery is noted in recent and future SAR missions. The Sentinel-1 SAR mission with its two identical SAR satellites (Sentinel-1A&B) is a good example of a recent SAR mission with a spatial resolution ranging from 5 m to 100 m and a revisit time of 6 days. This high temporal and spatial resolution is expected to be even higher in the near future with the launch of the RCM in late 2018. The RCM is expected to provide SAR imagery in a spatial resolution ranging from 1 m to 100 m, in a revisit time of only 4 days [32]. The increased temporal and spatial resolution would be required to adequately monitor wetlands and char- acterize the actual implications of climate change. Also, it is expected to further improve our understanding of climate change in wetlands and water quality, allowing ecosystem managers and decision makers to have sufficient information regarding wetland preservation.
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MONITORING TROPICAL FORESTS USING SYNTHETIC APERTURE RADAR

MONITORING TROPICAL FORESTS USING SYNTHETIC APERTURE RADAR

Advanced Synthetic Aperture Radar (SAR) remote sensing systems, their versatility and applications for environmental monitoring and survey are briefly introduced. The results of experiments conducted at sites of the Tropenbos Foundation in Colombia and Indonesia are shown to illustrate their capabilities. These are: (1) A newly developed technique to derive 3D tree maps from airborne high-resolution interferometric radar images. (2) Land cover change and fire damage monitoring by the ERS SAR satellite. (3) Selection of appropriate radar system specifications for very accurate monitoring of deforestation, land and forest degradation, secondary regrowth and land cover change. The information needs for tropical forest areas are very diverse. The use of dedicated airborne radar systems in combination with aerial photography and operational satellite systems would provide a sound basis for efficient data acquisition in support of prudent future forest management.
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A Supervised Classification Method for Levee Slide Detection Using Complex Synthetic Aperture Radar Imagery

A Supervised Classification Method for Levee Slide Detection Using Complex Synthetic Aperture Radar Imagery

Received: 10 August 2016; Accepted: 7 September 2016; Published: 12 September 2016 Abstract: The dynamics of surface and sub-surface water events can lead to slope instability, resulting in anomalies such as slough slides on earthen levees. Early detection of these anomalies by a remote sensing approach could save time versus direct assessment. We have implemented a supervised Mahalanobis distance classification algorithm for the detection of slough slides on levees using complex polarimetric Synthetic Aperture Radar (polSAR) data. The classifier output was followed by a spatial majority filter post-processing step that improved the accuracy. The effectiveness of the algorithm is demonstrated using fully quad-polarimetric L-band Synthetic Aperture Radar (SAR) imagery from the NASA Jet Propulsion Laboratory’s (JPL’s) Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR). The study area is a section of the lower Mississippi River valley in the southern USA. Slide detection accuracy of up to 98 percent was achieved, although the number of available slides examples was small.
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Fully convolutional neural network for rapid flood segmentation in synthetic aperture radar imagery.

Fully convolutional neural network for rapid flood segmentation in synthetic aperture radar imagery.

Abstract: Rapid response to natural hazards, such as floods, is essential to mitigate loss of life and the reduction of suffering. For emergency response teams, access to timely and accurate data is essential. Satellite imagery offers a rich source of information which can be analysed to help determine regions affected by a disaster. Much remote sensing flood analysis is semi-automated, with time consuming manual components requiring hours to complete. In this study, we present a fully automated approach to the rapid flood mapping currently carried out by many non-governmental, national and international organisations. We design a Convolutional Neural Network (CNN) based method which isolates the flooded pixels in freely available Copernicus Sentinel-1 Synthetic Aperture Radar (SAR) imagery, requiring no optical bands and minimal pre-processing. We test a variety of CNN architectures and train our models on flood masks generated using a combination of classical semi-automated techniques and extensive manual cleaning and visual inspection. Our methodology reduces the time required to develop a flood map by 80%, while achieving strong performance over a wide range of locations and environmental conditions. Given the open-source data and the minimal image cleaning required, this methodology can also be integrated into end-to-end pipelines for more timely and continuous flood monitoring.
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Target detection in synthetic aperture radar imagery: a state-of-the-art survey

Target detection in synthetic aperture radar imagery: a state-of-the-art survey

C-CORE. He has more than 21 years of experience in radar and remote sensing. He started his career in terrestrial radar, working as an RF designer on an over-the-horizon radar. He was also heavily involved with signal processing and analysis of radar data. Soon after the launch of RADARSAT in 1995, he moved into projects related to earth observation, with his first project dealing with iceberg detection capabilities of SAR. Since that time, he has managed and been technical advisor to a large series of projects at C-CORE involving earth observation, including marine target detection, vehicle detection along right-of-ways, and interferometry for ground deformation measurement. He still is actively involved in development of terrestrial-based radar systems. He is presently the PI of a multimillion-dollar R&D project on radar-based critical infrastructure monitoring funded by the Atlantic Innovation Fund. He is a member of the IEEE and the Association of Professional Engineers and Geoscientists of Newfoundland and Labrador.
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A Supervised Classification Method for Levee Slide Detection Using Complex Synthetic Aperture Radar Imagery

A Supervised Classification Method for Levee Slide Detection Using Complex Synthetic Aperture Radar Imagery

Abstract: The dynamics of surface and sub-surface water events can lead to slope instability resulting in anomalies such as slough slides on earthen levees. Early detection of these anomalies by a remote sensing approach could save time versus direct assessment. We have implemented a supervised Mahalanobis distance classification algorithm for the detection of slough slides on levees using complex polarimetric Synthetic Aperture Radar (polSAR) data. The classifier output was followed by a spatial majority filter post-processing step which improved the accuracy. The effectiveness of the algorithm is demonstrated using fully quad-polarimetric L-band Synthetic Aperture Radar (SAR) imagery from the NASA Jet Propulsion Laboratory’s (JPL’s) Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR). The study area is a section of the lower Mississippi River valley in the southern USA. Slide detection accuracy of up to 98 percent was achieved, although the number of available slides examples was small.
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Agricultural crop monitoring using airborne multi-spectral imagery and C-band synthetic aperture radar

Agricultural crop monitoring using airborne multi-spectral imagery and C-band synthetic aperture radar

Abstract. Airborne optical multi-spectral and C-band HH-polarized Synthetic Aperture Radar (SAR) imagery were acquired in conjunction with contemporan- eous ground-based measurements of various crop conditions (Leaf Area Index, canopy temperature, plant height) at a test site in southern Alberta, Canada on July 19± 20, 1994. Data were acquired for a variety of crops (wheat, canola, peas and beans) and irrigation practices. A number of crop condition± imagery relation- ships were examined to determine whether the imagery could be used to measure the various crop condition parameters. A number of statistically signi® cant cor- relations were found between the imagery and the crop condition parameters, and these correlations vary as a function of crop type, sensor and crop condition parameter. The results suggest that airborne remote sensing is well suited for measuring variations in crop conditions and that C-band SAR and multi-spectral imagery provide complementary information.
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Bistatic synthetic aperture radar processing

Bistatic synthetic aperture radar processing

The Japanese Earth Remote Sensing (JERS-1) satellite was launched in 1992. It was an L-band SAR system for the observation of geological changes and environmental moni- toring. The CSA launched RadarSat-1 in 1995. It was a C-band SAR system with the main purpose for daily monitoring of the Arctic glaciers and icebergs. It was capable of providing SAR images with a spatial resolution of 10m. RadarSat-2 was launched in 2007 which was able to provide a spatial resolution of 3m. COSMO-SkyMed (Constel- lation Of small Satellites for Mediterranean basin Observation) is an Earth observation Italian satellite system with X-band SAR sensors used for the global monitoring and coverage of the Earth’s surface. The first COSMO-SkyMed satellite was launched in June 2007, followed by the launch of a second satellite in December 2007 and still an- other in October 2008. SAR-Lupe is a German satellite system of five identical satellites controlled by a ground station. It uses X-band and operates in spotlight and stripmap modes. Its first satellite was launched in December 2006 and the last one was launched in July 2008. Another, TerraSAR-X, a German Earth observation satellite was launched in 2007. Its data is available both for the scientific purposes and for the commercial use. It has ability to get high resolution Earth images and has been designed to carry out its tasks for five years. It can acquire radar data in stripmap, spotlight and scan SAR modes. It was followed by the launch of another satellite, TanDEM-X in June 2010. Both satellites work in pair to generate a global high resolution Digital Elevation Model (DEM) of the Earth.
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Evaluation of Synthetic Aperture Radar Satellite Remote Sensing for Pavement and Infrastructure Monitoring

Evaluation of Synthetic Aperture Radar Satellite Remote Sensing for Pavement and Infrastructure Monitoring

Fig. 3. Urban growth tracking by using surface changes (a: assigning colors to surface characteristics, AOI presented with dashed line; b: 2004 surface distribution; c: 2010 surface distribution; d: October-2004 optical imagery of area; e: April-2011 optical imagery of area) 7. Cost-Benefit Analysis In many cost-benefit analyses that investigate monetary effects of a possible new technology or method, it is common to evaluate the cost in two parts: capital costs and maintenance/operating costs[22]. Capital costs include one-time expenses such as adaptation of system and associated technical infrastructure, computer and software costs, new personnel, etc. On the other hand, maintenance/operating cost includes ongoing or scheduled expenses during the time where system is in use such as continuous SAR imagery acquisition, data processing cost, etc. It is also considered that there are direct and indirect benefits and costs to both responsible agency such as DOTs and society.
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Retrieval of ocean surface winds and currents using satellite synthetic aperture radar and infrared radiometry

Retrieval of ocean surface winds and currents using satellite synthetic aperture radar and infrared radiometry

Satellite SAR systems in L-band (1 GHz), C-band (5 GHz) and X-band (10 GHz), have been and are currently used to investigate the ocean, revealing information about surface winds, sur- face currents, surface and internal waves, bathymetric features, oil spill and other parameters. These include: the L-band SAR on the early Seasat, the Japanese Earth Resources Satellite 1 (JERS-1) and more recently the Phased Array type L-band (PALSAR) sensor on the Advanced Land Observing Satellite (ALOS); the wide selection of C-band SAR sensors on the Euro- pean remote sensing satellites (ERS) ERS-1 and ERS-2, Radarsat-1 and Radarsat-2, the Radar Imaging Satellite 1 (RISAT-1) and the Advanced SAR (ASAR) sensor on the Environmental Satellite (Envisat). Data from ASAR has been used for the wind retrieval evaluation in Pa- pers A, B, and C; X-band sensors on Terrasar-X, its add-on for Digital Elevation Measurement (TanDEM-X), the COnstellation of small Satellites for the Mediterranean basin Observation (COSMO-Skymed), and the Korea Multi-Purpose Satellite-5 (KOMPSat-5).
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Aperture undersampling using compressive sensing for synthetic aperture stripmap imaging

Aperture undersampling using compressive sensing for synthetic aperture stripmap imaging

Synthetic aperture imaging is a high-resolution imaging technique employed in radar and sonar applications, which construct a large aperture by constantly transmitting pulses while moving along a scene of interest. In order to avoid azimuth image ambiguities, spatial sampling requirements have to be fulfilled along the aperture trajectory. The latter, however, limits the maximum speed and, therefore, the coverage rate of the imaging system. This paper addresses the emerging field of compressive sensing for stripmap synthetic aperture imaging using transceiver as well as single-transmitter and multi-receiver systems so as to overcome the spatial Nyquist criterion. As a consequence, future imaging systems will be able to significantly reduce their mission time due to an increase in coverage rate. We demonstrate the capability of our proposed compressive sensing approach to at least double the maximum sensor speed based on synthetic data and real data examples. Simultaneously, azimuth image ambiguities are successfully suppressed. The real acoustical measurements are obtained by a small-scale ultrasonic synthetic aperture laboratory system.
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Application for displaying synthetic aperture radar imagery in real-time Implementation and results

Application for displaying synthetic aperture radar imagery in real-time Implementation and results

Figure 10 shows down-sampling method based on averag- ing. This method uses two auxiliary matrices. First matrix accumulates values of pixels from original image which will represent single pixel at down-sampled image. In the second matrix counters of how many pixels of the original image will correspond to one pixel in the down-sampled image. In ideal case counter matrix will have the same values at every cell. More complicated case will occur when the radar platform manoeuvres and the flight trajectory is different than straight line.

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Synthetic Aperture Radar Signal Processing using GPGPU

Synthetic Aperture Radar Signal Processing using GPGPU

Abstract. In this work an efficient parallel implementation of the Chirp Scaling Algorithm (CSA) for Synthetic Aperture Radar (SAR) proce- ssing is presented. The architecture selected for the implementation is General Purpose Graphic Processing Unit (GPGPU), as it is well suited for scientific applications and real time implementation of algorithms. The analysis of a first implementation led to several improvements which resulted in an important final speedup. Details of the issues found are ex- plained, and the performance improvement of their correction explicitly shown.
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