Resolution Synthetic Aperture Radar Research Articles

The Application of High Resolution SAR in Mountain Area of Karst Tobacco Leaf Area Index Estimation Model

Wang, K.; Zhou, Z.; Liao, J., and Fu, Y., 2015. The application of high resolution SAR in mountain area of Karst tobacco leaf area index estimation model.Karst mountain is cloudy and rainy weather, surface broken, and land distribution is not concentrated, crop inter-planting, which cause the conventional remote sensing monitoring methods cannot meet the need of tobacco real-time monitoring. In order to realize the real-time monitoring of modern tobacco agriculture, study on the selection of high resolution synthetic aperture radar was applied in the mountainous area of Karst tobacco monitoring model. Guizhou qingzhen national modern tobacco agriculture base as the research area, in growth and maturity of tobacco, analysis different polarization of HH/VV and the bands HH/VV radar brightness value and the tobacco leaf area index correlation, construct the linear regression model and the two order polynomial model to inversion tobacco leaf area index. The results show that different polarization combinations of two kinds of model SAR brightness and LAI have better fitting degree, a linear HH/VV model inversion accuracy ratio, which achieve 91%. The results of the study can be timely access to tobacco growth status information, to provide data support and policy decision for tobacco monitoring and yield estimation.

Acceleration Model Analyses and Imaging Algorithm for Highly Squinted Airborne Spotlight-Mode SAR with Maneuvers

Synthetic aperture radar (SAR) imaging is usually performed along a straight path. In practice, variations from the ideal caused by maneuvers with acceleration are inevitable. The main problem is the complex signal processing, particularly in the highly squinted spotlight mode. In this paper, acceleration model is analyzed in more details. Impacts are obtained how the acceleration influences the Doppler parameters and space-variant terms, including the SAR resolution and azimuth bandwidth. According to the special signal properties of the acceleration case, an approximation of the range history is made to avoid using the method of series reversion (MSR) to obtain an accurate spectrum. Based on the high-accurate spectrum, a spotlight algorithm with a modified two-step approach is given. Simulation results show the effectiveness of the modified algorithm.

SAR Image Enhancement Based On Fractional Fourier Transform

Fractional Fourier transform (FrFT) is a kind of generalized Fourier transform and linear frequency modulation signals can be well focused after FrFT. Meanwhile motion error is an important factor affecting SAR (synthetic aperture radar) resolution, aiming at the problem that the effect of error elimination is not obvious in processing non-stationary mo- tion error using the conventional SAR imaging algorithm, FrFT is combined with two-step motion compensation tech- nique and wavenumber domain imaging algorithm in this paper. Simulation results and imaging results of real SAR data show that the proposed algorithm indeed eliminates the influence of motion error effectively. (the real SAR data provided by Institute of Electronics, Chinese Academy of Sciences).

Accuracy assessment of GPS navigation augmented by SAR and LiDAR-derived Digital Elevation Models

Remotely sensed Digital Elevation Models (DEM) can be used to augment a standalone Global Positioning System (GPS) by adding an extra range observation which measures the distance to the Earth centre. This method so called height aiding can reduce the number of GPS satellites required to get a 3D position fix from four to three and hence improve the performance of the GPS navigation algorithm in terms of accuracy, reliability and availability. Up until now, the accuracy of height aided GPS navigation using higher resolution Synthetic Aperture Radar (SAR) and Light Detection and Ranging (LiDAR)-derived elevation data has not been fully evaluated in a broad spectrum of navigation scenarios. This article provides a robust and accurate analysis on how much range error is introduced by height aiding using 5 m spacing SAR and 1 m spacing LiDAR-derived DEMs under in-car and personal navigation situations. Based on the experimental results obtained from both dynamic and static tests, suggestions have been made on what level of vertical and positional accuracy can be achieved as well as the related DEM quality issues for navigation purposes.

Power Spectra of Very High Resolution SAR Amplitude Images of Urban Areas

With the launch of COSMO/SkyMed and TerraSARX missions, very high resolution (VHR) Synthetic Aperture Radar (SAR) images of urban areas have become routinely available. In particular, in the spotlight acquisition mode, COSMO/SkyMed SARs are able to obtain a resolution even better than one meter. Accordingly, in principle a huge amount of information on objects present in the urban scenario can be extracted from such images; however, due to the involved interaction between incident electromagnetic wave and imaged scene, direct interpretation of VHR SAR images is not straightforward, and different methods to analyze them are being developed by the scientific community. In this paper, we explore the potentiality of using the power spectral density (PSD) of VHR SAR amplitude images of urban areas for information extraction. First, we propose a theoretical model of the PSD of such images, and then we test the validity of the developed model on COSMO/SkyMed spotlight SAR images of urban areas. Finally, some suggestions are provided on how the parameters of the obtained PSD can be exploited to extract useful information from VHR SAR amplitude images of urban areas.

SAR image Bayesian compressive sensing exploiting the interscale and intrascale dependencies in directional lifting wavelet transform domain

Compressive Sensing (CS) provides a new solution to reduce the huge amount of data for the transmission and storage of high resolution synthetic aperture radar (SAR) images. To improve the CS perfo...

Robust river boundaries extraction of dammed lakes in mountain areas after Wenchuan Earthquake from high resolution SAR images combining local connectivity and ACM

River boundaries extraction from SAR imagery is valuable for flood monitoring and damage assessment. Several rivers, parts of which include dammed lakes caused by landslides and rock avalanches triggered by the 2008 Wenchuan Earthquake, were taken as a case study for robust extraction. In this paper, a novel state-of-the-art approach for automated river boundaries extraction using high resolution synthetic aperture radar (SAR) intensity imagery is presented. The key of our approach lies in the combined usage of local connectivity feature of the river and a region-based active contours model (ACM) in a variational level set framework to differentiate between river and the background. First, sub-patched intensity thresholding segmentation is applied to SAR imagery. Pixels with intensities below the threshold are selected as potential river pixels while the others are potential background pixels. Second, potential river pixels are divided into several connected regions, considering that the river is a big connected region, only relatively bigger regions with similar contrast value are retained as the regions of interest (ROI) while others are noise due to pixel-level decision approach in the first step or shadows due to mountains terrain. Third, the ROI and their contours are regarded as local region and the initial contours to refine the river boundaries, which are used to reduce the scene complexity of ACM and its sensitivity to initial situation, respectively. A novel ACM driven by local image fitting (LIF) energy is presented and used for river boundaries extraction for the first time, which is not only robust against inhomogeneity widely spread in SAR imagery but also can work with efficiency without the need of re-initialization during iteration compared to traditional ACM. The proposed approach was tested on numerous high resolution airborne SAR images containing connected rivers or dammed lakes obtained by Chinese domestic radar system after Wenchuan Earthquake. For the overall dataset, the average commission error, omission error and root mean squared error were 6.5%, 3.3%, and 0.51, respectively. The average computational time for 4000 by 4000 image size was 21min using a PC-based MATLAB platform. Our experimental results demonstrate that the proposed approach is robust and effective.

Conceptual study of lunar-based SAR for global change monitoring

As an active microwave remote sensing imaging sensor, Synthetic Aperture Radar (SAR) plays an important role in earth observation. Here we establish a SAR system based on the platform of the moon. This will aid large-scale, constant, and long-term dynamic Earth observations to better meet the needs of global change research and to complement the space borne and airborne earth observations. Lunar-based SAR systems have the characteristics of high resolution and wide swath width. The swath width could be thousands of kilometers in the stripe mode and it could cover 40% of earth’s surface with 10 meters or even higher spatial resolution in the scanning mode. Using the simplified observation model, here we quantitatively analyze the spatial resolution and coverage area of lunar-based SAR and simulate the observation on the Qinghai-Tibet plateau and the Amazon plain. The results show that this system could provide near 100% daily coverage of the Qinghai-Tibet plateau, whereas 40% to 70% daily coverage of the Amazon plain. Lunar-based SAR could provide large-scale, long-term and stable time series data in order to support future research of global change.

Who launched what, when and why; trends in global land-cover observation capacity from civilian earth observation satellites

This paper presents a compendium of satellites under civilian and/or commercial control with the potential to gather global land-cover observations. From this we show that a growing number of sovereign states are acquiring capacity for space based land-cover observations and show how geopolitical patterns of ownership are changing. We discuss how the number of satellites flying at any time has progressed as a function of increased launch rates and mission longevity, and how the spatial resolutions of the data they collect has evolved. The first such satellite was launched by the USA in 1972. Since then government and/or private entities in 33 other sovereign states and geopolitical groups have chosen to finance such missions and 197 individual satellites with a global land-cover observing capacity have been successfully launched. Of these 98 were still operating at the end of 2013. Since the 1970s the number of such missions failing within 3years of launch has dropped from around 60% to less than 20%, the average operational life of a mission has almost tripled, increasing from 3.3years in the 1970s to 8.6years (and still lengthening), the average number of satellites launched per-year/per-decade has increased from 2 to 12 and spatial resolution increased from around 80m to less than 1m multispectral and less than half a meter for panchromatic; synthetic aperture radar resolution has also fallen, from 25m in the 1970s to 1m post 2007. More people in more countries have access to data from global land-cover observing spaceborne missions at a greater range of spatial resolutions than ever before. We provide a compendium of such missions, analyze the changes and shows how innovation, the need for secure data-supply, national pride, falling costs and technological advances may underpin the trends we document.

Motion error correction approach for high-resolution synthetic aperture radar imaging

An innovative data-based motion compensation approach is proposed for the high- resolution synthetic aperture radar (SAR). The main idea is to extract the displacements in line-of-sight direction and the range-dependent phase errors from raw data, based on an instanta- neous Doppler rate estimate. The approach is implemented by a two-step process: (1) the correction of excessive range cell migration; (2) the compensation of range-dependent phase errors. Experimental results show that the proposed method is capable of producing high-res- olution SAR imagery with a spatial resolution of 0.17 × 0.2 m 2 (range× azimuth )i n Ku band. © 2014 Society of Photo-Optical Instrumentation Engineers (SPIE) (DOI: 10.1117/1.JRS.8.083664)

SIGRI Project: Products Validation Results

The pilot project SIGRI (Sistema Integrato per la Gestione del Rischio Incendi) of the Italian Space Agency aims at developing an integrated system for the management of wildfires. The system provides satellite-based products capable of assisting in all phases of fire combat activities: forecast, detection, damage assessment, and recovery. The SIGRI goals were achieved by implementing consolidated methodologies, and/or developing innovative tools and methods for the automated analysis of multispectral, Synthetic Aperture Radar, and non-EO data. This paper focuses on the testing and validation of algorithms developed during the final phase of SIGRI. The validation of the products generated is an important phase in which their potential is assessed, and algorithms are calibrated. We discuss the results of the validation process of the main chain of unsupervised products: Modified Fire Probability Index by multispectral moderate resolution data (prevention), early detection of fire Hot Spots by multispectral very-high temporal resolution data (response), and identification and mapping of Burn Scars (damage assessment) by high-spatial resolution electro-optical and Synthetic Aperture Radar data.

Change Detection and Analysis of High Resolution Synthetic Aperture Radar Images Based on Bag-of-words Model

Change Detection and Analysis of High Resolution Synthetic Aperture Radar Images Based on Bag-of-words Model

SAR image Bayesian compressive sensing exploiting the interscale and intrascale dependencies in directional lifting wavelet transform domain

Compressive Sensing (CS) provides a new solution to reduce the huge amount of data for the transmission and storage of high resolution synthetic aperture radar (SAR) images. To improve the CS performance, in this work we propose directional lifting wavelet transform (DLWT) as a sparse representation for SAR image CS. Then a Bayesian-based SAR image CS reconstruction algorithm in DLWT domain is proposed. To further improve the reconstruction performance, an accurate prior probability model is proposed which fully exploits interscale attenuation and intrascale directional clustering properties of the DLWT coefficients; and a Bayesian inference via Markov Chain Monte Carlo (MCMC) sampling is employed to recover the image׳s wavelet coefficients and SAR image. Experimental results show that the proposed DLWT Tree-Direction-Clustering Compressive Sensing (DLWT-TDC-CS) can achieve the best reconstruction performance at sampling rates from 0.5 to 0.9 compared with various state-of-the-art CS reconstruction algorithms. DLWT-based CS reconstruction outperforms DWT-based CS reconstruction due to the improved sparse representation.

Detection of flooded urban areas in high resolution Synthetic Aperture Radar images using double scattering

Flooding is a particular hazard in urban areas worldwide due to the increased risks to life and property in these regions. Synthetic Aperture Radar (SAR) sensors are often used to image flooding because of their all-weather day–night capability, and now possess sufficient resolution to image urban flooding. The flood extents extracted from the images may be used for flood relief management and improved urban flood inundation modelling.A difficulty with using SAR for urban flood detection is that, due to its side-looking nature, substantial areas of urban ground surface may not be visible to the SAR due to radar layover and shadow caused by buildings and taller vegetation. This paper investigates whether urban flooding can be detected in layover regions (where flooding may not normally be apparent) using double scattering between the (possibly flooded) ground surface and the walls of adjacent buildings. The method estimates double scattering strengths using a SAR image in conjunction with a high resolution LiDAR (Light Detection and Ranging) height map of the urban area. A SAR simulator is applied to the LiDAR data to generate maps of layover and shadow, and estimate the positions of double scattering curves in the SAR image.Observations of double scattering strengths were compared to the predictions from an electromagnetic scattering model, for both the case of a single image containing flooding, and a change detection case in which the flooded image was compared to an un-flooded image of the same area acquired with the same radar parameters. The method proved successful in detecting double scattering due to flooding in the single-image case, for which flooded double scattering curves were detected with 100% classification accuracy (albeit using a small sample set) and un-flooded curves with 91% classification accuracy. The same measures of success were achieved using change detection between flooded and un-flooded images. Depending on the particular flooding situation, the method could lead to improved detection of flooding in urban areas.

Detection and monitoring of facilities exposed to subsidence phenomena via past and current generation SAR sensors

The identification of facilities in areas affected by subsidence phenomena represents a fundamental activity in processes dealing with land management. For this kind of phenomena, the analyses may be hampered by the lack of official subsidence zoning maps because of the wide extension of the affected areas. This is mainly due to the costs necessary for measurements and surveys to be carried out via conventional in situ techniques which can turn out to be unaffordable for the authorities in charge of land management. In this regard, during the last decade the use of remote sensing data, such as medium resolution synthetic aperture radar (SAR) images processed via differential interferometry algorithms (DInSAR), has proven its benefits for the detection and monitoring of facilities (i.e., buildings and infrastructures) in subsiding areas. Currently, the improved resolution and coverage of the ultimate generation SAR sensors seem very promising for consequence analyses of facilities, although displacement time series are still limited for long-term studies. In this paper, analyses of DInSAR data acquired via both medium (ERS-ENVISAT) and high (COSMO-SkyMed) resolution sensors are carried out over a densely urbanized flat area in southern Italy so as to show how the appropriate use of DInSAR data at different scales can valuably help in the detection and monitoring of damageable facilities.

Urban Footprint Processor—Fully Automated Processing Chain Generating Settlement Masks From Global Data of the TanDEM-X Mission

The German TerraSAR-X add-on for Digital Elevation Measurement (TanDEM-X) mission (TDM) collects two global data sets of very high resolution (VHR) synthetic aperture radar (SAR) images between 2011 and 2013. Such imagery provides a unique information source for the identification of built-up areas in a so far unique spatial detail. This letter presents the novel implementation of a fully automated processing system for the delineation of human settlements worldwide based on the SAR data acquired in the context of the TDM. The proposed Urban Footprint Processor (UFP) includes three main processing stages dedicated to: i) the extraction of texture information suitable for highlighting regions characterized by highly structured and heterogeneous built-up areas; ii) the generation of a binary settlement layer (built-up, non-built-up) based on an unsupervised classification scheme accounting for both the original backscattering amplitude and the extracted texture; and iii) a final post-editing and mosaicking phase aimed at providing the final Urban Footprint (UF) product for arbitrary geographical regions. Experimental results assess the high potential of the TDM data and the proposed UFP to provide highly accurate geo-data for an improved global mapping of human settlements.

Ratio-Detector-Based Feature Extraction for Very High Resolution SAR Image Patch Indexing

With the advent of very high resolution (VHR) synthetic aperture radar (SAR) images, local content description is becoming a critical issue for indexing. Conventional SAR image analysis techniques, like segmentation and pixel-level classification, are likely to fail as high-level semantic description should be considered for better discrimination. Therefore, we propose to use image-patch-based analysis method for SAR image interpretation. Inspired by ratio edge detector, in this letter, a new feature extraction method represented by the mean ratios in different directions is proposed for VHR SAR image content characterization. Based on the mean ratio, two simple yet powerful and robust features are proposed for SAR image patch indexing. One is the bag-of-word model using not only the basic statistics, i.e., local mean and variance, but also the mean ratios in different directions. The second one is an adaptation of the Weber local descriptor to SAR images by substituting the gradient with the ratio of mean differences in vertical and horizontal directions. To evaluate the proposed features, image patch indexing based on active learning using a SAR image database consisting of high-resolution TerraSAR-X patches is performed. Comparison with the state-of-the-art features, particularly texture features, has shown improved performance for SAR image categorization.

A Hierarchical Approach to Change Detection in Very High Resolution SAR Images for Surveillance Applications

The availability of very high resolution (VHR) synthetic aperture radar (SAR) images, which can be acquired by satellites over the same geographical area with short repetition interval, makes the development of effective unsupervised change detection (CD) techniques very important. This paper proposes a hierarchical approach to CD in VHR SAR images for addressing surveillance applications, where VHR data are acquired with high temporal resolution (e.g., one image every few days). The proposed approach is based on two concepts: exploitation of a multiscale technique for a preliminary detection of areas containing changes in backscattering at different scales (hot spots) and explicit modeling of the semantic meaning of changes by using both the intrinsic SAR image properties (e.g., acquisition geometry and scattering mechanisms) and the available prior information. In order to illustrate the effectiveness of the proposed approach, a problem of freight traffic surveillance is addressed considering two data sets. Each of them is made up of a pair of multitemporal VHR SAR images acquired by the COSMO-SkyMed (COnstellation of small Satellites for the Mediterranean basin Observation) constellation in spotlight mode. Each data set defines a complex CD problem due to both the presence of a variety of changes on the ground and the complexity of object backscattering. Experimental results point out the effectiveness of the proposed approach.

A Change Detection Approach to Flood Mapping in Urban Areas Using TerraSAR-X

Very high resolution synthetic aperture radar (SAR) sensors represent an alternative to aerial photography for delineating floods in built-up environments where flood risk is highest. However, even with currently available SAR image resolutions of 3 m and higher, signal returns from man-made structures hamper the accurate mapping of flooded areas. Enhanced image processing algorithms and a better exploitation of image archives are required to facilitate the use of microwave remote-sensing data for monitoring flood dynamics in urban areas. In this paper, a hybrid methodology combining backscatter thresholding, region growing, and change detection (CD) is introduced as an approach enabling the automated, objective, and reliable flood extent extraction from very high resolution urban SAR images. The method is based on the calibration of a statistical distribution of “open water” backscatter values from images of floods. Images acquired during dry conditions enable the identification of areas that are not “visible” to the sensor (i.e., regions affected by “shadow”) and that systematically behave as specular reflectors (e.g., smooth tarmac, permanent water bodies). CD with respect to a reference image thereby reduces overdetection of inundated areas. A case study of the July 2007 Severn River flood (UK) observed by airborne photography and the very high resolution SAR sensor on board TerraSAR-X highlights advantages and limitations of the method. Even though the proposed fully automated SAR-based flood-mapping technique overcomes some limitations of previous methods, further technological and methodological improvements are necessary for SAR-based flood detection in urban areas to match the mapping capability of high-quality aerial photography.

Dayton IEEE section places a display case at the wright brothers institute Tech^Edge innovation and collaboration center

The Dayton Section Chairman, Dr. Rob Ewing, and Secretary, Dr. Steve Hary, unveiled the IEEE Dayton Display Case at a ceremony held at the Wright Brothers Institute's Tech^Edge facility on December 4 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">th</sup> , 2012. The purpose of the display case is to highlight the many Dayton based IEEE member's contributions to the field of electrical engineering. Mr. Les McFawn, the Director of the Wright Brothers Institute, welcomed attendees and gave a presentation highlighting the significant capabilities of the Innovation and Collaboration Center, a facility that aims to bring together diverse joint teams working toward rapid solutions to pressing national and regional needs. Erwin Gangl, the current AESS Awards Chairman, discussed the contributions of seven local recipients of the AESS Pioneer Award in its 64 year history. They were: 1979 - Peter R. Murray - for Pilotless Aircraft 1987 - Rudy Stampel - for Weather Satellite Automatic Picture Transmission 1992 - Bill Eppers - for Laser Systems 1993 - Bill Bahret - for Stealth Technology 2003 - Bill Brown & Russ Boario - for Development in fine Resolution Synthetic Aperture Radar 2004 - Erwin Gangl - for Mil-Std-1553 Multiplex Data Bus Development