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This study presents a remote sensing application of using time series Landsat satellite images for monitoring the solid waste disposal site (WDS). 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 disposal site detection a variety steps of image processing used (calculation image average level of the earth's surface; filtering thresholds spectral brightness coefficients, the size of the connected components, the nature of reducing the level of height with the distance of the maximum level). The spatial geometric features of waste disposal facilities are analytically expressed by linear and radial characteristics from other objects of the earth surface. As a result, the proposed method demonstrates good accuracy in detection the solid waste disposal site on real satellite images.

Most existing radar algorithms are developed under the assumption that the environment, data clutter, is known and stationary. However, in practice, the characteristics of clutter can vary enormously in time depending on the operational scenarios. If unaccounted for, these nonstationary variabilities may drastically hinder the radar performance. It is essential that the radar systems dynamically detect changes in the environment, and adapt to these changes by learning the new statistical characteristics of the environment. In this paper, we employ sparse recovery for clutter identification, specifically we identify the statistical profile the clutter follows. We use Monte Carlo simulations to simulate and test clutter data coming from various distributions.

With the increasingly frequent human activities in space, number of dead satellites and space debris has increased dramatically, bring greater risks to the available spacecraft, however, the current widespread use of measuring equipment between space target has a lot of problems, such as high development costs or the limited conditions of use. To solve this problem, use radar multi-target measure error information to the space, and combining the relationship between target and the radar station point of view, building horizontal distance decoding model. By adopting improved signal quantization digit, timing synchronization and outliers processing method, improve the measurement precision, satisfies the requirement of multi-objective near distance measurements, and the using efficiency is analyzed. By conducting the validation test, test the feasibility and effectiveness of the proposed methods.

With the development of space technology, the number of spacecrafts and debris are increasing year by year. The demand for detecting and identification of spacecraft is growing strongly, which provides support to the cataloguing, crash warning and protection of aerospace vehicles. The majority of existing approaches for three-dimensional reconstruction is scattering centres correlation, which is based on the radar high resolution range profile (HRRP). This paper proposes a novel method to reconstruct the threedimensional scattering centre structure of target from a sequence of radar ISAR images, which mainly consists of three steps. First is the azimuth scaling of consecutive ISAR images based on fractional Fourier transform (FrFT). The later is the extraction of scattering centres and matching between adjacent ISAR images using grid method. Finally, according to the coordinate matrix of scattering centres, the three-dimensional scattering centre structure is reconstructed using improved factorization method. The three-dimensional structure is featured with stable and intuitive characteristic, which provides a new way to improve the identification probability and reduce the complexity of the model matching library. A satellite model is reconstructed using the proposed method from four consecutive ISAR images. The simulation results prove that the method has gotten a satisfied consistency and accuracy.

This contribution gives an overview of recent investigations regarding the detection of a consumer market unmanned aerial vehicles (UAV). The steadily increasing number of such drones gives rise to the threat of UAVs interfering civil air traffic. Technologies for monitoring UAVs which are flying in restricted air space, i. e. close to airports or even over airports, are desperately needed. One promising way for tracking drones is to employ radar systems. For the detection and classification of UAVs, the knowledge about their radar cross section (RCS) and micro-Doppler signature is of particular importance. We have carried out numerical and experimental studies of the RCS and the micro-Doppler of an example commercial drone in order to study its detectability with radar systems.

Knowing the exact growth stage of agricultural crops can be valuable information for crop management and monitoring. In Canada, canola fields are particularly vulnerable for crop disease development during their flowering stage, especially when the fields are under persistent wet conditions. Clubroot and sclerotinia are diseases that can occur in canola when these two factors come together. Remote sensing can provide an interesting tool for the monitoring of crop phenological stages over large agriculture landscapes. Reliable and frequent access to data is needed to determine field-specific growth stages. Given their all-weather capability, radar sensors are optimal for monitoring such a dynamic crop parameter. In 2014, Agriculture and Agri-Food Canada collected crop phenology information over multiple canola fields in the area of Carman, Manitoba. Coincidental to ground data collection, fully polarimetric RADARSAT-2 and dual-polarimetric TerraSAR-X satellite data were acquired over the study site. In collaboration with A. U. G. Signals Ltd., a methodology will be developed and validated for the identification of inflorescence emergence and flowering in canola fields. Analysis of the polarimetric datasets from this study determined that several polarimetric parameters were sensitive to the emergence of flower buds and the flowering stage in canola. The alpha angle and entropy in both the C- and X-band were able to identify these growth stages, in addition to any of the reflectivity ratios and differential reflectivity responses that incorporated an HV response. The RADARSAT-2 scatter diversity, degree of purity and depolarization index also demonstrated great potential at identifying canola flower emergence and flowering. These latter polarimetric parameters along with the reflectivity ratios may be advantageous given their ease in implementation within a larger risk assessment satellite-derived methodology for canola crop disease.

The Dual-frequency Precipitation Radar (DPR) on the Global Precipitation Measurement (GPM) core satellite was developed by Japan Aerospace Exploration Agency (JAXA) and National Institute of Information and Communications Technology (NICT). The objective of the GPM mission is to observe global precipitation more frequently and accurately. The GPM core satellite is a joint product of National Aeronautics and Space Administration (NASA), JAXA and NICT. NASA developed the satellite bus and the GPM Microwave Imager (GMI), and JAXA and NICT developed the DPR. The inclination of the GPM core satellite is 65 degrees, and the nominal flight altitude is 407 km. The non-sunsynchronous circular orbit is necessary for measuring the diurnal change of rainfall. The DPR consists of two radars, which are Ku-band precipitation radar (KuPR) and Ka-band precipitation radar (KaPR). GPM core observatory was successfully launched by H2A launch vehicle on Feb. 28, 2014. DPR keeps its performances on orbit after launch. DPR products were released to the public on Sep. 2, 2014. JAXA is continuing DPR trend monitoring, calibration and validation operations to confirm that DPR keeps its function and performance on orbit. JAXA have started to provide new version (Version 4) of GPM standard products on March 3, 2016. Various improvements of the DPR algorithm were implemented in the Version 4 product. Moreover, the latent heat product based on the Spectral Latent Heating (SLH) algorithm is available since Version 4 product. Current orbital operation status of the GPM/DPR and highlights of the Version 4 product are reported.

High-resolution Synthetic Aperture Radar (SAR) data represent an essential resource for the extraction of Ground Control Points (GCP) with sub-metric accuracy without in situ measurement campaigns. Conceptually, SAR-based GCP extraction consists of the following two steps: (i) identification of the same local feature on more SAR images and determination of their range/azimuth coordinates; (ii) spatial 3D positioning retrieval from the 2D radar coordinates, through spatial triangulation (stereo analysis) and inversion methods. In order to boost the geolocation accuracy, SAR images must be acquired from different line of sights, with intersection angles typically wider than 10 degrees, or even in opposite looking directions. In the present study, we present an algorithm specifically designed for ensuring robustness and accuracy in the fully automatic detection of bright isolated targets (steel light poles or towers) even when dealing with opposite looking data takes. In particular, the popular Harris algorithm has been selected as detector because it is the most stable and robust-to-noise algorithm for corners detection on SAR images. We outline the designed algorithmic solution and discusses the results derived over the urban area of Pisa (Italy), where more than ten COSMO-SkyMed Enhanced Spotlight (ES) stereo images are available, thus resulting an optimal test site for an assessment of the performances of the processing chain. The experimental analysis proofs that, assumed timing has been properly recalibrated, we are capable to automatically extract GCP from CSK ES data takes consisting in a very limited number of images.

Ground Control Points (GCP), automatically extracted from Synthetic Aperture Radar (SAR) images through 3D stereo analysis, can be effectively exploited for an automatic orthorectification of optical imagery if they can be robustly located in the basic optical images. The present study outlines a SAR/Optical cross-matching procedure that allows a robust alignment of radar and optical images, and consequently to derive automatically the corresponding sub-pixel position of the GCPs in the optical image in input, expressed as fractional pixel/line image coordinates. The cross-matching in performed in two subsequent steps, in order to gradually gather a better precision. The first step is based on the Mutual Information (MI) maximization between optical and SAR chips while the last one uses the Normalized Cross-Correlation as similarity metric. This work outlines the designed algorithmic solution and discusses the results derived over the urban area of Pisa (Italy), where more than ten COSMO-SkyMed Enhanced Spotlight stereo images with different beams and passes are available. The experimental analysis involves different satellite images, in order to evaluate the performances of the algorithm w.r.t. the optical spatial resolution. An assessment of the performances of the algorithm has been carried out, and errors are computed by measuring the distance between the GCP pixel/line position in the optical image, automatically estimated by the tool, and the “true” position of the GCP, visually identified by an expert user in the optical images.

This paper presents an approach to remove SLAR (Side-Looking Airborne Radar) image regions with low visual quality to be used for an automatic detection of oil slicks on a board system. This approach is focused on the detection and labelling of SLAR image regions caused by a poor acquisition from two antennas located on both sides of an aircraft. Thereby, the method distinguishes ineligible regions which are not suitable to be used on the steps of an automatic detection process of oil slicks because they have a high probability of causing false positive results in the detection process. To do this, the method uses a hybrid approach based on edge-based segmentation aided by Gabor filters for texture detection combined with a search algorithm of significant grey-level changes for fitting the boundary lines in each of all the bad regions. Afterwards, a statistical analysis is done to label the set of pixels which should be used for recognition of oil slicks. The results show a successful detection of the ineligible regions and consequently how the image is partitioned in sub-regions of interest in terms of detecting the oil slicks, improving the accuracy and reliability of the oil slick detection. This work was supported by the project (RTC-2014-1863-8) of call for collaboration challenges MINECO.

This paper focuses on the generation of Digital Elevation Models (DEMs) with COSMO SkyMed Spotlight data in providing DEMs. In particular, the peculiarity of Spotlight data (affected from Doppler centroid drift) is investigated, and the use of the processing chain included in the Delft Object-oriented Radar Interferometric Software (DORIS [1]). The effects of not correctly handled Doppler drift is shown. The standard interferometric processing, without Doppler drift handling, has been applied to Spotlight image pairs, resulting in interferometric coherence loss in interferograms as we move away from scene center. So, the standard processing chain has been modified to take in account the Doppler centroid drift affecting Spotlight data and very high resolution and accuracy DEMs have been obtained. Some Spotlight image pairs have been processed and the obtained DEMs have been shown and analyzed proving the high details and product accuracy.

Due to the unique imaging approach for ground-based radar, identification and classification in observation area is very difficult. In order to improve the accuracy of the calculation and application combine with other data resource. it is necessary to implement data matching of radar images and 3D laser point cloud. First, the 3D cloud should to be transformed to orthographic maps, and then the horizontal rotation and orbit attitude angle parameters would be estimated for similarity transformation according to the characteristics such as common points and lines. Finally, the same reference point of the ground-based SAR data and cloud data is employed to accomplished in a two-dimensional coordinate system (called local common coordinate system).

This paper is based on a proposed unmanned aerial system platform that is to be outfitted with high-resolution sensors. The proposed system is to be tethered to a moveable ground station, which may be a research vessel or some form of ground vehicle (e.g., car, truck, or rover). The sensors include, at a minimum: camera, infrared sensor, thermal, normalized difference vegetation index (NDVI) camera, global positioning system (GPS), and a light-based radar (LIDAR). The purpose of this paper is to provide an overview of existing methods for pollution detection of failing septic systems, and to introduce the proposed system. Future work will look at the high-resolution data from the sensors and integrating the data through a process called information fusion. Typically, this process is done using the popular and well-published Kalman filter (or its nonlinear formulations, such as the extended Kalman filter). However, future work will look at using a new type of strategy based on variable structure estimation for the information fusion portion of the data processing. It is hypothesized that fusing data from the thermal and NDVI sensors will be more accurate and reliable for a multitude of applications, including the detection of pollution entering the Chesapeake Bay area.

Water level was estimated, using AltiKa radar altimeter onboard the SARAL satellite, over the Ukai reservoir using modified algorithms specifically for inland water bodies. The methodology was based on waveform classification, waveform retracking, and dedicated inland range corrections algorithms. The 40-Hz waveforms were classified based on linear discriminant analysis and Bayesian classifier. Waveforms were retracked using Brown, Ice-2, threshold, and offset center of gravity methods. Retracking algorithms were implemented on full waveform and subwaveforms (only one leading edge) for estimating the improvement in the retrieved range. European Centre for Medium-Range Weather Forecasts (ECMWF) operational, ECMWF re-analysis pressure fields, and global ionosphere maps were used to exactly estimate the range corrections. The microwave and optical images were used for estimating the extent of the water body and altimeter track location. Four global positioning system (GPS) field trips were conducted on same day as the SARAL pass using two dual frequency GPS. One GPS was mounted close to the dam in static mode and the other was used on a moving vehicle within the reservoir in Kinematic mode. In situ gauge dataset was provided by the Ukai dam authority for the time period January 1972 to March 2015. The altimeter retrieved water level results were then validated with the GPS survey and in situ gauge dataset. With good selection of virtual station (waveform classification, back scattering coefficient), Ice-2 retracker and subwaveform retracker both work better with an overall root-mean-square error

Satellites play a major role in understanding the spatial and vertical distribution of aerosols and cloud microphysical parameters over a large area. However, the inherent limitations in satellite retrievals can be improved through inter-comparisons with airborne platforms. Over the Indian sub-continent, the vertical profiles retrieved from space-borne lidar such as CALIOP (Cloud-Aerosol LIdar with Orthogonal Polarization) on board the satellite CALIPSO and Cloud Profiling Radar (CPR) on board the satellite CloudSat were inter- compared with the aircraft observations conducted during Cloud Aerosol Interactions and Precipitation Enhancement Experiment (CAIPEEX). In the absence of high clouds, both aircraft and CALIOP showed similar features of aerosol layering and water-ice cloud signatures. As CALIOP could not penetrate the thick clouds, the aerosol information below the cloud is missed. While the aircraft could measure high concentrations below the cloud base and above the low clouds in the presence of high clouds. The aircraft derived liquid water content (LWC) and droplet effective radii (R e ) showed steady increase from cloud base to cloud top with a variable cloud droplet number concentration (CDNC). While the CloudSat derived LWC, CDNC and R e showed increase from the cloud top to cloud base in contradiction to the aircraft measurements. The CloudSat profiles are underestimated as compared to the corresponding aircraft profiles. Validation of satellite retrieved vertical profiles with aircraft measurements is very much essential over the tropics to improve the retrieval algorithms and to constrain the uncertainties in the regional cloud parameterization schemes.

Forest biomass acts as a backbone in regulating the climate by storing carbon within itself. Thus the assessment of forest biomass is crucial in understanding the dynamics of the environment. Traditionally the destructive methods were adopted for the assessment of biomass which were further advanced to the non-destructive methods. The allometric equations developed by destructive methods were further used in non-destructive methods for the assessment, but they were mostly applied for woody/commercial timber species. However now days Remote Sensing data are primarily used for the biomass geospatial pattern assessment. The Optical Remote Sensing data (Landsat8, LISS III, etc.) are being used very successfully for the estimation of above ground biomass (AGB). However optical data is not suitable for all atmospheric/environmental conditions, because it can’t penetrate through clouds and haze. Thus Radar data is one of the alternate possible ways to acquire data in all-weather conditions irrespective of weather and light. The paper examines the potential of ALOS PALSAR L-band dual polarisation data for the estimation of AGB in the Corbett Tiger Reserve (CTR) covering an area of 889 km2. The main focus of this study is to explore the accuracy of Polarimetric Scattering Model (Extended Water Cloud Model (EWCM) with respect to Backscatter model in the assessment of AGB. The parameters of the EWCM were estimated using the decomposition components (Raney Decomposition) and the plot level information. The above ground biomass in the CTR ranges from 9.6 t/ha to 322.6 t/ha.

Experimentations with applications of machine learning algorithms such as random forest (RF), support vector machines (SVM) and fuzzy inference system (FIS) to lithological classification of multispectral datasets are described. The input dataset such as LANDSAT-8 and Advanced Space-borne Thermal Emission and Reflection Radiometer (ASTER) in conjunction with Shuttle Radar Topography Mission (SRTM) digital elevation are used. The training data included image pixels with known lithoclasses as well as the laboratory spectra of field samples of the major lithoclasses. The study area is a part of Ajmer and Pali Districts, Western Rajasthan, India. The main lithoclasses exposed in the area are amphibolite, granite, calc-silicates, mica-schist, pegmatite and carbonates. In a parallel implementation, spectral parameters derived from the continuum-removed laboratory spectra of the field samples (e.g., band depth) were used in spectral matching algorithms to generate geological maps from the LANDSAT-8 and ASTER data. The classification results indicate that, as compared to the SVM, the RF algorithm provides higher accuracy for the minority class, while for the rest of the classes the two algorithms are comparable. The RF algorithm effectively deals with outliers and also ranks the input spectral bands based on their importance in classification. The FIS approach provides an efficient expert-driven system for lithological classification. It based on matching the image spectral features with the absorption features of the laboratory spectra of the field samples, and returns comparable results for some lithoclasses. The study also establishes spectral parameters of amphibolite, granite, calc-silicates, mica-schist, pegmatite and carbonates that can be used in generating geological maps from multispectral data using spectral matching algorithms.

In remote sensing, the change detection topic represents a broad field of research. If time series data is available, change detection can be used for monitoring applications. These applications require regular image acquisitions at identical time of day along a defined period. Focusing on remote sensing sensors, radar is especially well-capable for applications requiring regularity, since it is independent from most weather and atmospheric influences. Furthermore, regarding the image acquisitions, the time of day plays no role due to the independence from daylight. Since 2007, the German SAR (Synthetic Aperture Radar) satellite TerraSAR-X (TSX) permits the acquisition of high resolution radar images capable for the analysis of dense built-up areas. In a former study, we presented the change analysis of the Stuttgart (Germany) airport. The aim of this study is the categorization of detected changes in the time series. This categorization is motivated by the fact that it is a poor statement only to describe where and when a specific area has changed. At least as important is the statement about what has caused the change. The focus is set on the analysis of so-called high activity areas (HAA) representing areas changing at least four times along the investigated period. As first step for categorizing these HAAs, the matching HAA changes (blobs) have to be identified. Afterwards, operating in this object-based blob level, several features are extracted which comprise shape-based, radiometric, statistic, morphological values and one context feature basing on a segmentation of the HAAs. This segmentation builds on the morphological differential attribute profiles (DAPs). Seven context classes are established: Urban, infrastructure, rural stable, rural unstable, natural, water and unclassified. A specific HA blob is assigned to one of these classes analyzing the CovAmCoh time series signature of the surrounding segments. In combination, also surrounding GIS information is included to verify the CovAmCoh based context assignment. In this paper, the focus is set on the features extracted for a later change categorization procedure.

This paper describes a system concept for persistent (up to 365 days, 24/7), unmanned, local-area surveillance of air and maritime surface and sub-surface objects in the Canadian Arctic. The system concept is based on unmanned, remotely controlled and monitored Arctic Surveillance Systems that could be deployed at one or more maritime chokepoints, at locations in the Canadian Arctic Archipelago. The Artic Surveillance Systems would be operated over a satellite communication channel from a Southern Control Centre located in the south of Canada. Surveillance information for each detected platform and environmental reports for the local operating area would be compiled semi-automatically, from the integration of data transmitted from multiple above-water and underwater sensors and self-reporting systems, and be disseminated, in near real-time, to defence, security, and public safety clients. Potential sensors and self-reporting systems include radar, radar intercept receiver, underwater acoustic arrays, electro-optical/infrared camera system, Automatic Identification System (AIS), Automatic Dependent Surveillance - Broadcast (ADS-B), and a meteorological sensor system. The system concept is comprised of a surveillance concept – which includes environmental and geographic factors, platform types under potential surveillance, end-user information requirements, contributions of individual sensor types to surveillance, and the role of local-area surveillance within the broader context of Arctic Domain Awareness – and a system operating concept. The system operating concept describes the system capabilities and operating modes required to achieve the surveillance concept. Specific consideration is given to the interplay between the automation level, the quality and timeliness of the resulting information productions, the allocation of processing functions between the Control Centre and the Arctic Surveillance System, and transmitted data volumes. The system concept is being used in support of the specification of functional requirements for a concept demonstration system for Arctic surveillance, currently under development by Defence R&D Canada.

Earth Clouds, Aerosols and Radiation Explorer (EarthCARE) is a Japanese-European collaborative earth observation satellite mission aimed to deepen understanding of the interaction process between clouds and aerosols and their effects on the Earth’s radiation. The outcome of this mission is expected to improve the accuracy of global climate change prediction. As one of instruments for EarthCARE, the Cloud Profiling Radar (CPR) is the world’s first space-borne Doppler cloud radar jointly developed by the Japan Aerospace Exploration Agency (JAXA) and the National Institute of Information and Communications Technology (NICT). In Japan, the critical design review of the CPR has been completed in 2013, and CPR proto-flight model was manufactured and integrated until summer in 2015. Finally, the proto-flight test have been just started. This paper describes the design results and current status of CPR proto-flight test.

In this paper we propose to simulate SAR radar images that can be acquired by aircraft or satellite. This corresponds to a real problematic, in fact, an airborne radar data acquisition campaign, was conducted in the south east of France. We want to estimate the geometric deformations that a digital terrain model can be subjected. By extrapolation, this construction should also allow to understand the image distortion if a plane is replaced by a satellite. This manipulation allow to judge the relevance of a space mission to quantify geological and geomorphological data. The radar wave is an electromagnetic wave, they have the advantage of overcoming atmospheric conditions since more wavelength is large is better crossing the cloud layer. Therefore imaging radar provides continuous monitoring.

A test environment is established to obtain experimental data for verifying the positioning model which was derived previously based on the pinhole imaging model and the theory of binocular stereo vision measurement. The model requires that the optical axes of the two cameras meet at one point which is defined as the origin of the world coordinate system, thus simplifying and optimizing the positioning model. The experimental data are processed and tables and charts are given for comparing the positions of objects measured with DGPS with a measurement accuracy of 10 centimeters as the reference and those measured with the positioning model. Sources of visual measurement model are analyzed, and the effects of the errors of camera and system parameters on the accuracy of positioning model were probed, based on the error transfer and synthesis rules. A conclusion is made that measurement accuracy of surface surveillances based on binocular stereo vision measurement is better than surface movement radars, ADS-B (Automatic Dependent Surveillance-Broadcast) and MLAT (Multilateration).

Currently, high resolution imaging of the space satellite is a popular topic in the field of radar technology. In contrast with regular targets, the satellite target often moves along with its trajectory and simultaneously its solar panel substrate changes the direction toward the sun to obtain energy. Aiming at the imaging problem, a signal separating and imaging approach based on the empirical mode decomposition (EMD) theory is proposed, and the approach can realize separating the signal of two parts in the satellite target, the main body and the solar panel substrate and imaging for the target. The simulation experimentation can demonstrate the validity of the proposed method.

The Information Telecommunication System "Solaris" was created to solve the actual problems of navigation. The system allows analyzing an operational local and synoptic hydrometeorological situation on a vessel route to issue recommendations to navigators about the optimization of the movement from the point of view for the safety of navigation and economic efficiency. ITS "Solaris" represents a combination of two hardware-software complexes one of which is fitted on the vessel, and the other one ashore. The ship complex is based on the use of regular ship radar station to which AD converter and the computer with the software is connected. The coastal complex is a server that collects, processes, analyzes stores and transmits satellite and ship information. To operate the system on a particular ship the proposed model calculates the speed of the vessel, depending on the direction and speed of meteorological parameters such as wind, waves and currents, of ice conditions. The system can be used in the initial planning of the route on the basis of operational and climatic data according to the seasonal variability of the weather conditions. The created system improves the safety of navigation and increase economic efficiency.

The European Space Agency (ESA) and the Japan Aerospace Exploration Agency (JAXA) are cooperating to develop the EarthCARE satellite mission with the fundamental objective of improving the understanding of the processes involving clouds, aerosols and radiation in the Earth’s atmosphere. The payload consists of two active and two passive instruments: an ATmospheric LIDar (ATLID), a Cloud Profiling Radar (CPR), a Multi-Spectral Imager (MSI) and a Broad-Band Radiometer (BBR). The four instruments' data are processed individually and in a synergetic manner to produce a large range of products, which include vertical profiles of aerosols, liquid water and ice, observations of cloud distribution and vertical motion within clouds, and will allow the retrieval of profiles of atmospheric radiative heating and cooling. The presentation will cover the configuration of the satellite with its four instruments, the mission implementation approach, an overview of the ground segment and the overall mission development status. The four instruments performance, based upon currently available analyses and test results will also be briefly addressed with a focus on the ESA instruments.

In this paper, we propose an unsupervised change detection method using the labeled co-occurrence matrix on multitemporal SAR images. In SAR images, each land cover (LC) class has a distinct reflectivity to radar signals and presents a specific backscattering value. Generally, the amplitude of the SAR images can be roughly clustered into three classes according to the backscattering behaviors of the LC classes. The changes occurred between the images can be considered as a backscattering variation that is changed from one backscattering class into another. As a result, we analyzed the possible cases of the positive and negative backscattering variations, and merged the initial three backscattering classes into two classes with the pixel in the medium backscattering class being attached to the strong backscattering class and the low backscattering class respectively in a membership degree. Two pairs of fuzzy-label images are derived accordingly, where each pair of fuzzy-label images are computed from the multi-temporal SAR data. The labeled cooccurrence matrix is computed locally on each pair of fuzzy-label images by combining the membership values in a conjunctive operator, and the autocorrelation feature is extracted. The classifications are implemented by Otsu Nthresholding algorithm on the derived two autocorrelation features. The final binary change detection map is achieved by combining the obtained two classification results. Experiments were carried on portions of multi-temporal Radarsat-1 SAR data. The effectiveness of the proposed approach was confirmed.

An interferometric performance analysis for repeat-pass geosynchronous circular SAR (GEOCSAR) is presented. The analysis mainly includes the errors caused by the following sources: radar thermal noise, spatial baseline decorrelation, images’ misregistration and the atmospheric effects. For circular SAR (CSAR) imaging on the geosynchronous orbit, the altitude is very high, the atmospheric effect is severe and the high sidelobe is existed in the focused GEOCSAR signal, the characteristics of the various error sources of GEOCSAR interferometry will behave differently from the conventional interferometric SAR.

We present an investigation of surface deformation using Differential SAR Interferometry (DInSAR) time-series carried out in an active open pit iron mine, the N5W, located in the Carajas Mineral Province (Brazilian Amazon region), using 33 TerraSAR-X (TSX-1) scenes. This mine has presented a historical of instability and surface monitoring measurements over sectors of the mine (pit walls) have been done based on ground based radar. Two complementary approaches were used: the standard DInSAR configuration, as an early warning of the slope instability conditions, and the DInSAR timeseries analysis. In order to decrease the topographic phase error a high resolution DEM was generated based on a stereo GeoEye-1 pair. Despite the fact that a DinSAR contains atmospheric and topographic phase artifacts and noise, it was possible to detect deformation in some interferometric pairs, covering pit benches, road ramps and waste piles. The timeseries analysis was performed using the 31 interferometric pairs, which were selected based on the highest mean coherence of a stack of 107 interferograms, presenting less phase unwrapping errors. The time-series deformation was retrieved by the Least-Squares (LS) solution using an extension of the Singular Value Decomposition (SVD), with a set of additional weighted constrain on the acceleration deformation. The atmospheric phase artifacts were filtered in the space–time domain and the DEM height errors were estimated based on the normal baseline diversity. The DInSAR time-series investigation showed good results for monitoring surface displacement in the N5W mine located in a tropical rainforest environment, providing very useful information about the ground movement for alarm, planning and risk assessment.

Ka-band RADAR frequency range has not yet been used for Synthetic Aperture Radar (SAR) from space so far, although this technology may lead to important applications for the next generation of SAR space sensors. Therefore, feasibility studies regarding a Ka-band SAR instrument have been started [1][2], for the next generation of SAR space sensors. In spite of this, the lack of trusted references on backscatter at Ka-band revealed to be the main limitation for the investigation of the potentialities of this technology. In the framework of the ESA project “Ka-band SAR backscatter analysis in support of future applications”, this paper is aimed at the study of wave interaction at Ka-band for a wide range of targets in order to define a set of well calibrated and reliable Ka-band backscatter coefficients for different kinds of targets. We propose several examples of backscatter data resulting from a critical survey of available datasets at Ka-band, focusing on the most interesting cases and addressing both correspondences and differences. The reliability of the results will be assessed via a preliminary comparison with ElectroMagnetic (EM) theoretical models. Furthermore, in support of future technological applications, we have designed a prototypal software acting as a “library” of earth surface radar response. In our intention, the output of the study shall contribute to answer to the need of a trustworthy Ka-Band backscatter reference. It will be of great value for future technological applications, such as support to instrument analysis, design and requirements’ definition (e.g.: Signal to Noise Ratio, Noise Equivalent Sigma Zero).

The Dual-frequency Precipitation Radar (DPR) on the Global Precipitation Measurement (GPM) core satellite was developed by Japan Aerospace Exploration Agency (JAXA) and National Institute of Information and Communications Technology (NICT). The GPM is a follow-on mission of the Tropical Rainfall Measuring Mission (TRMM). The objectives of the GPM mission are to observe global precipitation more frequently and accurately than TRMM. The frequent precipitation measurement about every three hours will be achieved by some constellation satellites with microwave radiometers (MWRs) or microwave sounders (MWSs), which will be developed by various countries. The accurate measurement of precipitation in mid-high latitudes will be achieved by the DPR. The GPM core satellite is a joint product of National Aeronautics and Space Administration (NASA), JAXA and NICT. NASA developed the satellite bus and the GPM Microwave Imager (GMI), and JAXA and NICT developed the DPR. JAXA and NICT developed the DPR through procurement. The contract for DPR was awarded to NEC TOSHIBA Space Systems, Ltd. The configuration of precipitation measurement using active radar and a passive radiometer is similar to TRMM. The major difference is that DPR is used in GPM instead of the precipitation radar (PR) in TRMM. The inclination of the core satellite is 65 degrees, and the flight altitude is about 407 km. The non-sun-synchronous circular orbit is necessary for measuring the diurnal change of rainfall similarly to TRMM. The DPR consists of two radars, which are Ku-band (13.6 GHz) precipitation radar (KuPR) and Ka-band (35.5 GHz) precipitation radar (KaPR). Both KuPR and KaPR have almost the same design as TRMM PR. The DPR system design and performance were verified through the development test and the proto flight test. DPR had handed over to NASA and integration of the DPR to the GPM core spacecraft had completed in May 2012. GPM core spacecraft satellite system test had completed in November 2013. The result of the satellite system test concerning to the DPR satisfied system requirements. GPM core observatory was shipped to Tanegashima Space Center, JAPAN and Launch Site Operations had started on November 2013 and GPM core observatory was launched at 18:37:00 (UT) on February 27, 2014 successfully. DPR orbital check out was completed in May 2014. The orbital check out and the initial calibration and validation operation result of DPR is reported.

The Project focuses on the resolution of decreased water supply as a result of continued water shortage. The water loss is the result of several causes, notably water losses in the water supply network pipes, the ineffective operation of pumping stations and design of the networks are some of the most important problems which need to be addressed to optimize the performance of the water networks and save both water and the energy consumed. The Project is innovative as it is the first time that several, state-of-the-art technologies, including Global Positioning System (GPS), Radar Scanners and Satellite Remote Sensing (SRS) will be combined to map the water networks. The data produced will be entered into a Geographic Information System (GIS), with the objective of developing a digital imprint and mapping of the network. Following, a representative section of the network will be selected and modeled in the new upgraded laboratory in order to conduct detailed experimental studies, which will be correlated with computational/mathematical studies. The main scientific challenge of the Project, which also characterizes its broader innovative aspect, lies in the possibility to optimize the design and also the operation of all the components of the network, through the combined use of hydraulic simulation models and multi-criteria evolution algorithms. © 2014 SPIE.

We present a novel approach to use mountain drainage patterns for GPS-Denied navigation of small unmanned aerial systems (UAS) such as the ScanEagle, utilizing a down-looking fixed focus monocular imager. Our proposal allows extension of missions to GPS-denied mountain areas, with no assumption of human-made geographic objects. We leverage the analogy between mountain drainage patterns, human arteriograms, and human fingerprints, to match local drainage patterns to Graphics Processing Unit (GPU) rendered parallax occlusion maps of geo-registered radar returns (GRRR). Details of our actual GPU algorithm is beyond the subject of this paper, and is planned as a future paper. The matching occurs in real-time, while GRRR data is loaded on-board the aircraft pre-mission, so as not to require a scanning aperture radar during the mission. For recognition purposes, we represent a given mountain area with a set of spatially distributed mountain minutiae, i.e., details found in the drainage patterns, so that conventional minutiae-based fingerprint matching approaches can be used to match real-time camera image against template images in the training set. We use medical arteriography processing techniques to extract the patterns. The minutiae-based representation of mountains is achieved by first exposing mountain ridges and valleys with a series of filters and then extracting mountain minutiae from these ridges/valleys. Our results are experimentally validated on actual terrain data and show the effectiveness of minutiae-based mountain representation method. Furthermore, we study how to select landmarks for UAS navigation based on the proposed mountain representation and give a set of examples to show its feasibility. This research was in part funded by Rockwell Collins Inc.

ACE is a proposed Tier 2 NASA Decadal Survey mission that will focus on clouds, aerosols, and precipitation as well as ocean ecosystems. The primary objective of the clouds component of this mission is to advance our ability to predict changes to the Earth's hydrological cycle and energy balance in response to climate forcings by generating observational constraints on future science questions, especially those associated with the effects of aerosol on clouds and precipitation. ACE will continue and extend the measurement heritage that began with the A-Train and that will continue through Earthcare. ACE planning efforts have identified several data streams that can contribute significantly to characterizing the properties of clouds and precipitation and the physical processes that force these properties. These include dual frequency Doppler radar, high spectral resolution lidar, polarimetric visible imagers, passive microwave and submillimeter wave radiometry. While all these data streams are technologically feasible, their total cost is substantial and likely prohibitive. It is, therefore, necessary to critically evaluate their contributions to the ACE science goals. We have begun developing algorithms to explore this trade space. Specifically, we will describe our early exploratory algorithms that take as input the set of potential ACE-like data streams and evaluate critically to what extent each data stream influences the error in a specific cloud quantity retrieval.

The study of interactions between aerosol particles, atmospheric dynamics and clouds and their resulting corresponding indirect effects on precipitation and radiative transfer demand new measurement strategies combining the strength of lidar, radar, and in-situ instrumentation. To match this challenge the Leipzig Aerosol and Cloud Remote Observations System (LACROS) has been set up at TROPOS, combining the strengths of a unique set of active and passive remote sensing and in-situ measurement systems.

ESA and JAXA plan to launch a satellite called EarthCARE (Earth Clouds, Aerosols and Radiation Explorer). The Cloud Profiling Radar (CPR), which will be the first millimeter-wave Doppler radar in space, is installed on this satellite as one of main sensors to observe clouds. This paper describes the design results and PFM performance of EarthCARE CPR.

The Global Precipitation Measurement (GPM) mission will advance the measurement of global precipitation, making possible high spatial resolution precipitation measurements. GPM will provide the first opportunity to calibrate measurements of global precipitation across tropical, mid-latitude, and polar regions. The GPM mission has the following scientific objectives: (1) Advance precipitation measurement capability from space through combined use of active and passive remote-sensing techniques; (2) Advance understanding of global water/energy cycle variability and fresh water availability; (3) Improve climate prediction by providing the foundation for better understanding of surface water fluxes, soil moisture storage, cloud/precipitation microphysics and latent heat release in the Earth's atmosphere; (4) Advance Numerical Weather Prediction (NWP) skills through more accurate and frequent measurements of instantaneous rain rates; and (5) Improve high impact natural hazard (flood/drought, landslide, and hurricane hazard) prediction capabilities. The GPM mission centers on the deployment of a Core Observatory carrying an advanced radar / radiometer system to measure precipitation from space and serve as a reference standard to unify precipitation measurements from a constellation of research and operational satellites. GPM, jointly led with the Japan Aerospace Exploration Agency (JAXA), involves a partnership with other international space agencies including the French Centre National d’Etudes Spatiales (CNES), the Indian Space Research Organisation (ISRO), the U.S. National Oceanic and Atmospheric Administration (NOAA), the European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT), and others. The GPM Core Observatory is currently being prepared for shipment to Japan for launch. Launch is scheduled for February 2014 from JAXA’s Tanegashima Space Center on an H-IIA 202 launch vehicle.

The problem of electromagnetic waves scattering at rough boundaries is of practical interest and has been addressed many times in different papers. Theories for investigation of rough surface scattering primarily two kinds of methods: numerical method and approximate method. As the classic analytical methods cannot calculate the electromagnetic scattering characteristics at Low Grazing Angle (LGA) accurately, in this paper, a novel method is presented by utilizing the Radar Cross Section (RCS) of the low grazing two-dimensional sea surface based on the triangles-based Physical Optics (PO) method. Firstly, the Monte Carlo method is applied to simulate the two-dimension rough sea surface in different wind speeds based on the PM sea spectrum. Then, the sea surface is generally meshed by 1/8~1/10 length of the incident wave. Secondly, the complex permittivity of the sea surface is calculated by two-Debye method and compared with the experiment. The physical optical is used to calculate the backscattering coefficient of the random rough sea surface. Considering the problem of low grazing, it is especially sampled more densely between the scattering angles 70°~90°. Then the self-shadow and inter-shadow of the sea surface at low grazing angle is taken into account, the Z-BUFFER method is used to judgment of the shadow effect. The numerical result is compared with the FEKO and good agreement is obtained. As the frequency increasing, the sea surface will have more triangles to be calculated, it will take more time. Finally, we propose a novel graphic processing unit (GPU)-accelerated decoding system. The result is 68.96 faster than its CPU counterpart.

At present, the civil aviation airports use the surface surveillance radar monitoring and positioning systems to monitor the aircrafts, vehicles and the other moving objects. Surface surveillance radars can cover most of the airport scenes, but because of the terminals, covered bridges and other buildings geometry, surface surveillance radar systems inevitably have some small segment blind spots. This paper presents a monocular vision imaging technology model for airport surface surveillance, achieving the perception of scenes of moving objects such as aircrafts, vehicles and personnel location. This new model provides an important complement for airport surface surveillance, which is different from the traditional surface surveillance radar techniques. Such technique not only provides clear objects activities screen for the ATC, but also provides image recognition and positioning of moving targets in this area. Thereby it can improve the work efficiency of the airport operations and avoid the conflict between the aircrafts and vehicles. This paper first introduces the monocular visual imaging technology model applied in the airport surface surveillance and then the monocular vision measurement accuracy analysis of the model. The monocular visual imaging technology model is simple, low cost, and highly efficient. It is an advanced monitoring technique which can make up blind spot area of the surface surveillance radar monitoring and positioning systems.

The proposed method aims to reduce the spurious echoes in weather radar images collected at Melbourne radar site, using parameters from 2D-WTMM method based on the continuous wavelet transform, and including the PNN probabilistic neural network for the classification of pixels into two types of echoes : precipitation or parasite. Indeed, we propose the introduction of parameters related to wavelet transform skeletons, these parameters are proportional to the image texture roughness, anisotropy and the distance of separation between non-zero radar echoes cells and give good separation between rain and non-rain echoes. Radar image is first segmented with Voronoi’s cells according to the spatial distribution of Holder exponents. By comparing with a direct method of classification which takes into account only one parameter at a time by using a threshold, it was found that the combination of these three parameters with PNN approach improves the final results in terms of preserving precipitation echoes and elimination of weather radar clutter. Initial results show approximately the removal of 98% of clutter and preservation of 97% of precipitation echoes.

The Earth, Clouds, Aerosols and Radiation Explorer (EarthCARE) mission is joint mission between Europe and Japan for the launch year of 2015. Mission objective is to improve scientific understanding of cloud-aerosol-radiation interactions that is one of the biggest uncertain factors for numerical climate and weather predictions. The EarthCARE spacecraft equips four instruments such as an ultra violet lidar (ATLID), a cloud profiling radar (CPR), a broadband radiometer (BBR), and a multi-spectral imager (MSI) to observe aerosols, clouds and their interactions simultaneously from the orbit. Japan aerospace exploration agency (JAXA) is responsible for development of the CPR that will be the first space-borne W-band Doppler radar. The CPR is defined with minimum radar sensitivity of -35dBz, radiometric accuracy of 2.7 dB, and Doppler velocity measurement accuracy of 1m/s. These specifications require highly accurate pointing technique in orbit and high power source with large antenna dish. JAXA and National Institute of Information and Communications Technology (NICT) have been jointly developed this CPR to meet these requirements. In addition, new ground calibration technique is also being progressed for the launch of EarthCARE/CPR. This evaluation method will also be the first use for spacecraft as well as Doppler cloud radar. This paper shows the summary of the CPR design and verification status, and activity status of development of ground calibration method with a few results of experiment using current space-borne cloud radar (CloudSat, NASA).

The Tropical Rainfall Measuring Mission (TRMM) has been providing reliable global precipitation data since its launch in 1997. It is expected that good handover to the Global Precipitation Measurement (GPM) mission at around early 2014 and GPM is expected to operate 5 years and to accumulate a reliable global long precipitation record. Currently, the dual frequency precipitation radar (DPR), one of the major instruments onboard GPM core satellite, has been developed. Although about twenty years global precipitation record will be obtained by the end of the GPM mission, there are still high expectation for the longer precipitation record from the viewpoints of climate change monitoring, evaluation of the numerical prediction models on global warming, and so on. Even the precipitation information becomes more important. For these reasons, future precipitation measurement mission is started to study targeting the successive observation to GPM. Mission requirements are gathered from GPM science community and are consolidated to the mission concept during this study also the potential users and their expected requirements are defined. The most important scientific target is the cloud-precipitation processes study, which is one of the uncovered topics in GPM mission. To fulfill this requirement and potential users requirements, cloud and precipitation observation capability is required for this mission. Considering the technology evolution, cloud radar (with current technology) or high sensitivity precipitation radar can achieve the requirements. Preliminary feasibility study (accommodativeness to spacecraft) was done with the help from JAXA’s space application program system engineering office.

ALOS-2 and ALOS-3 will succeed to radar and optical mission of Advanced Land Observing Satellite “Daichi” which had contributed to cartography, regional observation, disaster monitoring, and resources surveys for more than 5 years until its termination of operation in May 2011. ALOS-2 carries the state-of-the-art L-band Synthetic Aperture Radar (SAR) called PALSAR-2 which succeeds to the ALOS/PALSAR with enhanced performance in both high resolution (1m * 3m at finest in the Spotlight mode) and wide swath (up to 490km in the ScanSAR wide mode). Wider bandwidth and shorter revisit time will give better conference for INSAR data analysis such as crustal deformation and deforestation. The Proto Flight Model of ALOS-2 including PALSAR-2 is under integration and testing at JAXA’s Tsukuba Space Center. ALOS-3 carries the optical sensor called PRISM-2 which succeeds to the ALOS/PRISM mission with enhanced performance in high resolution (0.8 m), wide swath (50 km) and high geo-location accuracy. PRISM-2 will acquire stereo pair images with two telescopes for stereo mapping and precise Digital Surface Models. It is also considered to carry Hyper-spectral Imager Suite (HISUI), which is developed by the Ministry of Economy, Trade and Industry (METI) of Japan. JAXA has conducted the phase-A study on ALOS-3 spacecraft and mission instruments, with prototype testing of key components. This paper describes an overview of ALOS-2 and ALOS-3.

EarthCARE is the Earth Clouds Aerosols and Radiation Explorer mission. The project is the result of a collaboration between ESA and JAXA. The satellite will carry four instruments: an atmospheric lidar (ATLID), a cloud profiling radar (CPR) provided by JAXA, a multi spectral imager (MSI) and a broadband radiometer (BBR). The payload elements will operate in synergy to provide a better understanding of clouds and aerosols and their impact on the Earth’s climate. An important aspect of the mission is that, for the first time, the suite of instruments will make near simultaneous observations of the same cloud/aerosol scene, The presentation will describe the satellite and its mission and will make particular reference to the design and current development status of the payload.

In this presentation we will discuss the performance of classification and retrieval algorithms for spaceborne cloud and precipitation radars such as the Global Precipitation Measurement mission [1] Dual-frequency Precipitation Radar (GPM/DPR) [2], and notional radar for the Aerosol/Clouds/Ecosystem (ACE) [1] mission and related concepts. Spaceborne radar measurements are simulated either from Airborne Precipitation Radar 2 nd Generation (APR-2, [3]) observations, or from atmospheric model outputs via instrument simulators contained in the NASA Earth Observing Systems Simulators Suite (NEOS 3 ). Both methods account for the three dimensional nature of the scattering field at resolutions smaller than that of the spaceborne radar under consideration. We will focus on the impact of nonhomogeneities of the field of hydrometeors within the beam. We will discuss also the performance of methods to identify and mitigate such conditions, and the resulting improvements in retrieval accuracy. The classification and retrieval algorithms analyzed in this study are those derived from APR-2’s Suite of Processing and Retrieval Algorithms (ASPRA); here generalized to operate on an arbitrary set of radar configuration parameters to study the expected performance of spaceborne cloud and precipitation radars. The presentation will highlight which findings extend to other algorithm families and which ones do not.

This paper proposes a novel approach to building change detection in Very High Resolution (VHR) Synthetic Aperture Radar (SAR) images. The proposed approach is based on three concepts: i) the selection of the proper scale of representation; ii) the extraction of information on changes associated with increase and decrease of backscattering at the selected building scale (hot-spots); and iii) the exploitation of the expected backscattering proprieties of buildings to detect new and fully destroyed buildings. Experimental results obtained on a data set made up of two COSMO-SkyMed (CSK®) spotlight images acquired in 2009 over the city of L'Aquila (Italy) before and after the earthquake that hit the region, demonstrated that the proposed approach allows an accurate identification of destroyed buildings while presents a low rate of false alarms.

An FM-CW radar at 94 GHz has been equipped with antennas with high azimuthal gain and a broad beam in elevation and mounted on a rotating pedestal. The radar covers a range interval of 500 m. A change detection algorithm is used to discriminate invading persons from the background. Tracking of non-stationary target objects is maintained by Kalman filtering. The RCS of detected objects is determined using a pre-calibration against a precision corner reflector at a defined range. The measured RCS is compared with a threshold value to discriminate between dangerous and nondangerous persons. Additional information from a video camera is necessary for a first discrimination between target classes.

In the framework of a project [1] dealing with the enforcement of harbor security, a radar based luggage and parcel scanner was developed, with the inherent characteristics of the use of non ionizing radiation and thus without any health hazards for the involved people. The development of hardware and imaging software, based upon near field synthetic aperture algorithms, and fast mechanical scanning is described and first results of high resolution images of threat objects within parcels are shown.

The Dual-frequency Precipitation Radar (DPR) on the Global Precipitation Measurement (GPM) core spacecraft is being developed by Japan Aerospace Exploration Agency (JAXA) and National Institute of Information and Communications Technology (NICT). The GPM is a follow-on mission of the Tropical Rainfall Measuring Mission (TRMM). The objectives of the GPM mission are to observe global precipitation more frequently and accurately than TRMM. The frequent precipitation measurement about every three hours will be achieved by some constellation satellites with microwave radiometers (MWRs) or microwave sounders (MWSs), which will be developed by various countries. The accurate measurement of precipitation in mid-high latitudes will be achieved by the DPR. The GPM core satellite is a joint product of National Aeronautics and Space Administration (NASA), JAXA and NICT. NASA is developing the satellite bus and the GPM microwave radiometer (GMI), and JAXA and NICT are developing the DPR. JAXA and NICT are developing the DPR through procurement. The contractor for DPR is NEC TOSHIBA Space Systems, Ltd. The configuration of precipitation measurement using an active radar and a passive radiometer is similar to TRMM. The major difference is that DPR is used in GPM instead of the precipitation radar (PR) in TRMM. The inclination of the core spacecraft is 65 degrees, and the flight altitude is about 407 km. The non-sun-synchronous circular orbit is necessary for measuring the diurnal change of rainfall similarly to TRMM. The DPR consists of two radars, which are Ku-band (13.6 GHz) precipitation radar (KuPR) and Ka-band (35.55 GHz) precipitation radar (KaPR). According to the different detectable dynamic ranges, The KaPR will detect snow and light rain, and the KuPR will detect heavy rain. In an effective dynamic range in both KuPR and KaPR, drop size distribution information and more accurate rainfall estimates will be provided by a dual-frequency algorithm. The proto-flight test for DPR have finished in February 2012 and DPR integration on GPM core spacecraft was successfully completed in May 2012. The status of proto-flight model of DPR will be presented.

Directional ocean wave spectra can describe the energy distribution of ocean wave and play an important role in oceanography. Synthetic aperture radar (SAR) can measure wave spectra based on a nonlinear mapping model between the image spectra (or cross spectra) and wave spectra. Due to the complexity of variable estimation and wave number cut off in azimuth direction in the mapping process, some scientists have been endeavoring to improve the retrieval of wave spectra. The paper proposes a method for the retrieval, which don’t depend on any external information except wind direction. It separates into two parts. In part 1, the spectra for real image or the cross spectra for complex image are calculated. And the modulation transfer function (MTF) is estimated including real aperture radar (RAR) MTF and velocity bunching. Then the wave spectra are retrieved by direct dividing based on the quasi-linear relation. The 180° ambiguity is removed from the imaginary part of cross spectra or the wind direction. The significant wave height (Hs), peak wave wavelength (L) and peak wave direction (D) are extracted from the retrieval. If the angle between the D and the azimuth direction is higher than 45°, the retrieval is finished. If the angle is lower than 45°, we go on retrieving. In part 2, the first guess spectra are needed to compensate the lost wave information, which can be parametrically constructed based on the retrieved wave spectra in part 1. Finally the wave spectra are iteratively retrieved from the first guess spectra based on the nonlinear relation. The Envisat ASAR images are used to validate the method. In case 1, the RMSE between this method and SARTool on D, L, Hs is 7.6°, 19.7 m, 0.18 m respectively. In case 2, the RMSE between this method and Jason-1 on Hs is 0.5 m.

In this paper the new simulator of unmanned aerial vehicle is described. The application is capable of generating 3D environment based on graphical map input, and the vehicle motion is controlled by the Vector Field Histogram method, using radar heads as perception data source.