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Reclaimed airports usually have fragile geological structures and are susceptible to the uneven ground settlements caused by filling-material consolidation, underground construction, and dynamic loading from takeoff and landing of aircrafts. Therefore, deformation monitoring is of great significance to the safe operation of reclaimed airports. This study adopts an improved permanent-scatterer interferometric synthetic-aperture radar strategy to map the spatiotemporal deformation of Shenzhen Bao'an International Airport in China using ascending and descending Envisat/ASAR data acquired from 2007 to 2010 and Sentinel-1 data from 2015 to 2019. The results show that uneven settlements of the airport concentrate in the new reclaimed land. Then we explore the settlement characteristics of each functional area. Furthermore, we separate out the dynamicload settlement of runway No. 2 and confirm the settlements caused by dynamic load. This study provides new ideas for studying deformation in similar fields, and technical references for the future construction of Shenzhen Airport. [ABSTRACT FROM AUTHOR]

The article looks at developments in the use of radar remote sensing. Topics discussed include the basics of radar and interferometric synthetic aperture radar (InSAR) imaging and the revolution of InSAR technology on monitoring natural hazards and characterizing natural resources. Also highlighted are some of the emerging InSAR technologies, including multi-aperture InSAR, SqueeSAR and SAR Tomography.

The article focuses on the use of radar imaging in monitoring the oil spill in the Gulf of Mexico. It notes that radar being a remote sensing instrument has the capability to image the surface at night and below cloud cover. It mentions that the National Aeronautics and Space Administration (NASA) positioned the uninhabited aerial vehicle synthetic aperture radar (UAVSAR) to monitor the coastline for scientific studies.

Multi-Layer Perceptrons (MLP5) are powerful function approxima tars. In the last decades they were successfully applied to many different regression and classification problems. Their characteristics and convergence properties are well studied and relatively well understood, but they were originally designed to work with real-valued data. The main focus of this paper is the classification of polarimetric synthetic aperture radar (POLsAR) data which are a corn plex- valued signal. Instead of using an arbitrarily projection of this complex-valued data to the real domain, the paper proposes the usage of complex-valued MLP5 (Cv-MLP5), which are an extension of MLPs to the complex domain. The paper provides a detailed yet general derivation of the complex backpropagation algorithm and mentions related problems as well as possible solutions. Furthermore, it evaluates the performance of Cv-MLPs in a land-cover classification task in POLSAR images ui~der several learning conditions, and compares the proposed classifier with standard methods. The experimental results show that CV-MLPs are successfully applicable to classification tasks in POLSAR data. They show good convergence properties and a better performance if compared to real-valued MLPS. [ABSTRACT FROM AUTHOR]

Lidar (light detection and ranging) point cloud data contain abundant three-dimensional (3D) information. Dense distribution of scanned points on object surfaces prominently implies surface features. Particularly, plane features commonly appear in a typical lidar dataset of artificial structures. To explore implicitly contained spatial information, this study developed an automatic scheme to segment a lidar point cloud dataset into coplanar point clusters. The central mechanism of the proposed method is a split-and-merge segmentation based on an octree structure. Plane fitting serves as an engine in the mechanism that evaluates how well a group of points fits to a plane. Segmented coplanar points and derived parameters of their best-fit plane are obtained through the process. This paper also provides algorithms to derive various geometric properties of segmented coplanar points, including inherent properties of a plane, intersections of planes, and properties of point distribution on a plane. Several successful cases of handling airborne and terrestrial lidar data as well as a combination of the two are demonstrated. This method should improve the efficiency of object modelling using lidar data. [ABSTRACT FROM AUTHOR]

Delineating management zones is important in agriculture for implementing site-specific practices. We delineated within-field homogeneous zones over a corn and a wheat field using high spatial resolution multi-temporal airborne C-band synthetic aperture radar (SAR) imagery with an object-based fuzzy k-means classification approach. Image objects were generated by a segmentation procedure implemented in eCognition® software, and were classified as basic processing units using SAR data. Results were evaluated using analysis of variance and variance reduction of soil electrical conductivity (EC), leaf area index (MI), and crop yield. The object-based approach provided better results than a pixel-based approach. The variance reduction in LAI, and soil EC varied with SAR acquisition time and incidence angle. Although the variance reduction of yield was not as significant as that of MI and EC, average yield among the delineated zones were different in most cases. The SAR data classification produced interpretable patterns of soil and crop spatial variability, which can be used to infer within-field management zones. [ABSTRACT FROM AUTHOR]

This study evaluated the potential of an object-oriented approach to forest type classification as well as volume and biomass estimation using small-footprint, multiple return lidar data. The approach was applied to coniferous, deciduous, and mixed forest stands in the Virginia Piedmont, U.S.A. A multiresolution, hierarchical segmentation algorithm was applied to a canopy height model (CHM) to delineate objects ranging from 0.035 to 5.632 ha/average object. Per-object lidar point (per return height and intensity) and CHM distributional parameters were used as input to a discriminant classification of 2-class (deciduous-coniferous) and 3-class (deciduous-coniferous-mixed) forest definitions. Lidar point-height-based and CHM classifications yielded overall accuracies of 89 percent and 79 percent, respectively. Volume and biomass estimates exhibited differences of no more than 5.5 percent compared to field estimates, while showing distinctly improved precisions (up to 45.5 percent). There were no significant differences between accuracies for varying object sizes, which implies that reducing the lidar point coverage would not affect classification accuracy. These results lead to the conclusion that a lidar-based approach to forest type classification and volume/biomass assessment has the potential to serve as a single-source inventory tool. [ABSTRACT FROM AUTHOR]

The article reports on GeoSar's interferometric synthetic aperture radar data (IFSAR) as topographic mapping system in equatorial regions. Geosar technology is developed by NASA's Jet Propulsion Laboratory, U.S. Department of Defense's Defense Advanced Research Projects Agency, Califonia Department of Conservation, and Fugro EarthData, Inc. It cam collect data from an altitude of 40,000 ft. at an airspeed of over 350 mph. Its system operates two frequencies namely X-band and P-band whose wavelengths have a bandwidth of 160 MHz. It also maps drainage features, canopies and wetland habitats in equatorial regions.

Each new generation of remote sensing system typically follows the same evolutionary path from the laboratory to everyday use. For example, the first digital film scanners were actually densitometers with engineering grade servo systems driving them in a pattern across the film with fragile, purpose-built software capturing and converting the data. As the technology matures, it evolves through hardened engineering systems, specialty commercial solutions and, if the market will sustain volume, into a commodity. An inevitable parallel is a declining price in the product being produced by the system. However, how does one emulate this necessary evolution when the volume of the technology is too low to allow it to follow this course? Such was the challenge faced by Fugro EarthData as their GeoSAR system matured from a one-off experimental system developed by NASA's Jet Propulsion Laboratory (JPL) into a production system with commercial throughput and reliability expectations. The avenue chosen by Fugro EarthData was to envelop the unique GeoSAR processing algorithms into the GeoCue enterprise work flow management system. [ABSTRACT FROM AUTHOR]

Building boundary is necessary for the real estate industry, flood management, and homeland security applications. The extraction of building boundary is also a crucial and difficult step towards generating city models. This study presents an approach to the tracing and regularization of building boundary from raw lidar point clouds. The process consists of a sequence of four steps: separate building and non-building lidar points; segment lidar points that belong to the same building; trace building boundary points; and regularize the boundary. For separation, a slope based ID bi-directional filter is used. The segmentation step is a region-growing approach. By modifying a convex hull formation algorithm, the building boundary points are traced and connected to form an approximate boundary. In the final step, all boundary points are included in a hierarchical least squares solution with perpendicularity constraints to determine a regularized rectilinear boundary. Our tests conclude that the uncertainty of regularized building boundary tends to be linearly proportional to the lidar point spacing. It is shown that the regularization precision is at 18 percent to 21 percent of the lidar point spacing, and the maximum offset of the determined building boundary from the original lidar points is about the same as the lidar point spacing. Limitation of lidar data resolution and errors in previous filtering processes may cause artefacts in the final regularized building boundary. This paper presents the mathematical and algorithmic formulations along with stepwise illustrations. Results from Baltimore city, Toronto city, and Purdue University campus are evaluated. [ABSTRACT FROM AUTHOR]

This paper presents the potential of integrating radar data features with optical data to improve automatic land-cover mapping. For our study area of St. Louis, Missouri, Landsat ETM+ and Radarsat images are orthorectified and co-registered to each other. A maximum likelihood classifier is utilized to determine different land-cover categories. Ground reference data from sites throughout the study area are collected for training and validation. The variations in classification accuracy due to a number of radar imaging processing techniques are studied. The relationship between the processing window and the land classification is also investigated. In addition, the Landsat images are fused with several combinations of processed radar features. The classification accuracies from the Landsat and radar feature combinations are studied. Our research finds that fusion of multi-sensor data improves the classification accuracy over a single Landsat sensor, although different processing techniques on radar images are required to obtain the best results. In our study, fusion of Landsat images and Radarsat feature combinations from a 13 × 13 entropy window, 9 × 9 data range widow, and 19 × 19 mean filter window achieves the highest overall accuracy improvement (10 percent) over the Landsat images alone. [ABSTRACT FROM AUTHOR]

In the present study we demonstrate the value of the SRTM three arcsecond terrain model for a virtual survey of archaeological sites: the detection and mapping of ancient settlement mounds in the Near East. These so-called ‘tells’ are the result of millennia of occupation within the period from 8000–1000 BC, and form visible landmarks of the world's first farming and urban communities. The SRTM model provides for the first time an opportunity to scan areas not yet surveyed archaeologically on a supra-regional scale and to pinpoint probable tell sites. In order to map these historic monuments for the purpose of settlement-study and conservation, we develop a machine learning classifier which identifies probable tell sites from the terrain model. In a test, point-like elevations of a characteristic tell shape, standing out for more than 5 to 6 m in the DEM were successfully detected (85/133 tells). False positives (327/(600*1200) pixels) were primarily due to natural elevations, resembling tells in height and size. [ABSTRACT FROM AUTHOR]

We analyze Shuttle Radar Topography Mission (SRTM) water surface elevation data to assess the capacity of interferometric radar for future surface water missions. Elevations from three Ohio reservoirs and several Amazon floodplain lakes have standard deviations, interpreted as errors, that are smaller in C-band compared to X-band and are smaller in Ohio than in the Amazon. These trends are also evident when comparing water surface elevations from the Muskingum River in Ohio with those of the Amazon River. Differences are attributed to increased averaging in C-band compared to X-band, greater sensitivity to surface water motion in X-band, and generally larger off-nadir look angles in X-band. Absolute water surface elevations are greater in the C-band DEM for much of the two study areas and yield expected slope values. Height and slope differences are attributed to differing usage of geoids and ellipsoids. These SRTM measurements suggest the great possibility for space-based, laterally-spatial (2D) measurements of water surface elevations. [ABSTRACT FROM AUTHOR]

We produced a landscape scale map of mean tree height in mangrove forests in Everglades National Park (ENP) using the elevation data from the Shuttle Radar Topography Mission (SRTM). The SRTM data was calibrated using airborne lidar data and a high resolution USGS digital elevation model (DEM). The resulting mangrove height map has a mean tree height error of 2.0 m (RMSE) over a pixel of 30 m. In addition, we used field data to derive a relationship between mean forest stand height and biomass in order to map the spatial distribution of standing biomass of mangroves for the entire National Park. The estimation showed that most of the mangrove standing biomass in the ENP resides in intermediate-height mangrove stands around 8 m. We estimated the total mangrove standing biomass in ENP to be 5.6 ⊗ 109 kg. [ABSTRACT FROM AUTHOR]

The Geoscience Laser Altimeter System (GLAS) instrument onboard the Ice, Cloud, and land Elevation Satellite (ICESat) provides a globally distributed elevation data set that is well-suited to independently evaluate the accuracy of digital elevation models (DEMs), such as those produced by the Shuttle Radar Topography Mission (SRTM). We document elevation differences between SRTM C-band 1 and 3 arcsecond resolution DEMs and ICESat 1064 nm altimeter channel elevation data acquired in an areas of variable topography and vegetation cover in the South American Amazon Basin, Asian Tibetan Plateau - Himalayan Mountains, East Africa, western Australia, and the western United States. GLAS received waveforms enable the estimation of SRTM radar phase center elevation biases and variability with respect to the highest (canopy top where vegetated), centroid (distance-weighted average), and lowest (ground) elevations detected within ICESat laser footprints. Distributions of ICESat minus SRTM elevation differences are quantified as a function of waveform extent (a measure of within-footprint relief), SRTM roughness (standard deviation of a 3 ⊗ 3 array of elevation posts), and percent tree cover as reported in the Vegetation Continuous Field product derived from Moderate Resolution Imaging Spectrometer (MODIS) data. SRTM roughness is linearly correlated with waveform extent for areas where percent tree cover is low. The SRTM phase center elevation is usually located between the ICESat highest and lowest elevations, and on average is closely correlated with the ICESat centroid. In areas of low relief and sparse tree cover, the mean of ICESat centroid minus SRTM phase center elevation differences for the five regions examined vary between -3.9 and 1.0 m, and the corresponding standard deviations are between 3.0 and 3.7 m. With increasing SRTM roughness and/or tree cover, the SRTM elevation remains essentially unbiased with respect to the ICESat centroid but the standard deviations of the differences increase to between 20 and 34 m, depending on the region. For the Australia, Amazon, Africa, United States, and Asia regions, including all tree cover and roughness conditions, 90 percent of the SRTM elevations are within 6.9, 11.5, 12.1, 16.8, and 37.1 m of the ICESat centroid, respectively. In vegetated areas, the SRTM elevation on average is located approximately 40 percent of the distance from the canopy top to the ground. The variability of this result increases significantly with increasing SRTM roughness. The results are generally consistent for the five regions examined, providing a method to estimate for any location the correspondence between SRTM elevations and highest, average, and lowest elevations using the globally-available MODIS-derived estimate of tree cover and the measure of SRTM roughness. [ABSTRACT FROM AUTHOR]

The Shuttle Radar Topography Mission (SRTM) generated one of the most-complete high-resolution digital topographic data sets of the world to date. The elevations generated by the on-board C-band sensor represent surface elevations in ‘bare earth’ regions, and the elevations of various scatterers such as leaves and branches in other regions. Elevations generated by a medium-footprint (>10 m diameter) laser altimeter (lidar) system known as NASA's Laser Vegetation Imaging Sensor (LVIS) were used to assess the accuracy of SRTM elevations at study sites of variable relief, and land-cover. Five study sites in Maine, Massachusetts, Maryland, New Hampshire, and Costa Rica were chosen where coincident LVIS and SRTM data occur. Both ground and canopy top lidar elevations were compared to the SRTM elevations. In ‘bare earth ’ regions, the mean vertical offset between the SRTM elevations and LVIS ground elevations varied with study site and was approximately 0.0 m, 0.5 m, 3.0 m, 4.0 m, and 4.5 m at the Maine, Maryland, Massachusetts, New Hampshire, and Costa Rica study sites, respectively. In vegetated regions, the mean vertical offset increased, implying the phase center fell above the ground, and the offset varied by region. The SRTM elevations fell on average approximately 14 m below the LVIS canopy top elevations, except in Costa Rica where they were approximately 8 m below the canopy top. At all five study sites, SRTM elevations increased with increasing vertical extent (i.e., the difference between the LVIS canopy top and ground elevations and analogous to canopy height in vegetated regions). A linear relationship was found sufficient to describe the relationship between the SRTM-LVIS elevation difference and canopy vertical extent. [ABSTRACT FROM AUTHOR]

The Shuttle Radar Topography Mission (SRTM) produced near-global 1″ and 3″ OEMs. The cartographically-derived National Elevation Dataset (NED) provides a mechanism to assess SRTM quality. We compared 12 geomorphometric parameters from SRTM to NED for about 500,000 sample areas over the continental United States. For basic parameters like average elevation or relief, the two data sets correlate very highly. For more derived measures, such as curvature and higher moments (skewness and kurtosis), the correlations are much lower, with some parameters essentially uncorrelated between the two DEMs. Correlations improve after restricting analysis to region with average slopes greater than 5 percent, and the SRTM data set compares more closely to simulated 2″ NED than to 1″ NED. SRTM has too much noise in fiat areas, increasing average slope, while in high relief areas SRTM over smoothes topography and lowers average slopes. The true resolution of 1″ SRTM DEMs proves to be no better than 2″. [ABSTRACT FROM AUTHOR]

Two different digital elevation models (DEM) were derived during the 2000 Shuttle Radar Topography Mission from C- and X-band interferometric radar data. While these two DEMs share several of their properties, they were processed independently. Here, we investigate what can be gained by merging the two DEMs into a single composite DEM for four different test areas. Based on an analysis of the relative differences and the deviations from an absolute reference in one test area, we propose an algorithm for combining the two OEMs optimally. We then compare the composite DEM with both individual DEMs and with a reference of a large number of precise GPS profiles in one test area in southern Germany. We find that in our test areas, the area of missing values is reduced significantly in the composite DEM. Even compared with the more complete C-band DEM, the number of void pixels can be reduced by 22 percent to 53 percent. Also, outlier values resulting from errors in the interferometric phase unwrapping can often be identified and removed in the merging. The deviations of both C- and X-band DEMs from the GPS reference are very similar and well within the accuracy specifications of the global data set. The standard deviation of the difference between the composite DEM and the reference is about 14 percent below that of the original values. Depending on the requirements for completeness and accuracy, merging the two SRTM elevation data sets may provide an important improvement above either of the original DEMs. [ABSTRACT FROM AUTHOR]

The NASA/NGA Shuttle Radar Topography Mission (SRTM) collected interferometric radar data which has been used by the Jet Propulsion Laboratory to generate a near-global topography data product for latitudes smaller than 60°. One of the primary goals of the mission was to produce a data set that was globally consistent and with quantified errors. To achieve this goal, an extensive global ground campaign was conducted by NGA and NASA to collect ground truth that would allow for the global validation of this unique data set. This paper documents the results of this SRTM validation effort using this global data set. The table shown below summarizes our results (all quantities represent 90 percent errors in meters). [ABSTRACT FROM AUTHOR]

The Shuttle Radar Topography Mission (SRTM) successfully acquired terrain elevation data for 80 percent of the Earth's landmass in February 2000. The radar system and data collection scheme designed by NASA's Jet Propulsion Laboratory (JPL) met the global requirements of the U.S. Department of Defense for Level 2 Digital Terrain Elevation Data (DTED®). JPL processed the raw data &to unfinished DTED® 2 and other products that were delivered to two contractors of the National Geospatial-Intelligence Agency. The contractors edited the unfinished DTED® 2, updated the associated products, and generated finished products for distribution. Automated processes were developed by each contractor to identify, delineate and set heights for lakes, rivers, and ocean coastlines in conformance with an extensive set of editing rules created to maintain consistency and uniformity in the final products. The finished DTED® is significantly better than the 16 m vertical accuracy required by the original specification. [ABSTRACT FROM AUTHOR]

The article provides information about the Delta Surface Fill (DSF), a process which replaces the void with fill source posts that are adjusted to the Shuttle Radar Topography Mission (SRTM), an imaging radar system developed by the National Aeronautics and Space Administration and the National Geospatial-Intelligence Agency, values found at the void interface. The DSF method causes the fill to more closely emulate the original SRTM surface while still retaining the useful data the fill contains. The process computes an adjustment of the fill source to the SRTM. The DSF processes was mentioned and offers information on each of the processes. The difference between the DSF and Fill and Feather technique was discussed.

The article presents a review on the origin of the Shuttle Radar Topography Mission project, an advanced imaging radar system in space in partnership with the National Geospatial-Intelligence Agency and the National Aeronautics and Space Administration in the U.S. It mentions several engineers and scientists who have greatly contributed to the success for the development the project including chief engineer Ed Caro, project manager Mike Sander, scientist Gerry Elphingstone and mechanical engineer Bill Layman. The contributions made by the scientists and engineers mentioned was emphasized. It offers a review on how the concept of making the project was made.

The first aim of this paper is to show how the joint use of Digital Surface Models (DSMs) coming from different sources may improve the understanding of an urban environment. More specifically, we consider loser and radar three-dimensional data over the same urban area and show that they can be profitably combined to improve building extraction. We exploit the better vertical and horizontal accuracy of the laser DSM, assumed to be available only for a small area, to ease the deformation of Interferometric Synthetic Aperture Radar (INSAR) DSM with built structures. To achieve this, we propose a method based on subsequent steps of geometrical correction, and mainly on a simple "stretching step" that uses laser data as a reference to adjust INSAR-derived building footprints. We show quantitative results obtained from two different urban areas, using different laser and radar data sets, to assess advantages and drawbacks of the proposed method. [ABSTRACT FROM AUTHOR]

Combining data from different sensors for visual or classification analysis is a common task in remote sensing. The first step is normally to register the images which may be considered geometric integration; the accuracy of this step is important to create a valuable final hybrid image. This paper addresses geometric integration and introduces a new method for automatically registering two dissimilar images, such as, a radar image and an optical image with high accuracy. Pre-registration of the two images to within a specified tolerance is required. In our examples, this tolerance is up to 17 pixels (at the scale of the higher resolution image) and may be achieved by, for example, visually located control points. The described approach then uses large-scale edge gradient contours in a process that automatically locates candidate control points on the contours. The points are selected using a cost function that measures the degree of match between all possible pairs of points. Numerous control points (typically around 50 pairs) are found from matched pairs of gradient contours and used in a global, rubber sheet, polynomial warp to refine the registration. This approach is applied to register a Synthetic Aperture Radar (SAR) image (ERS2, 12.5 m pixels) and a Thematic Mapper (TM) optical image (Landsat-5, 28.5 m pixels) automatically. Several examples with different scene content are shown to validate the approach and discussed in terms of residual registration error and processing efficiency. [ABSTRACT FROM AUTHOR]

Satellite radar interferometry can be used to spatially monitor small vertical ground deformations. When millimeter accuracy is required, the differential interferometry technique is hampered by the ambiguity with atmospheric artifacts. It is also often difficult to obtain a precise evaluation of the kinematic evolution of ground deformations from a set of time, randomly distributed interferograms. We present the results of a least-squares approach coupled with a temporal filtering and applied to a large data set over the City of Paris. The mean deformation rate and a map of areas affected by time, non-linear deformation events are presented. We show that this approach, which provides a chronologically ordered set of phase screens, allows the retrieval of the kinematic parameters of ground deformations as low as 1 to 2 mm per year. Subsiding areas have been detected, and their evolution in time has been quantified. Such an approach can be useful to fully characterize the kinematic evolution of ground deformations in major cities or desertic areas where large areas have a high degree of coherence and where millimeter accuracy is often required. [ABSTRACT FROM AUTHOR]

This article presents one solution to the parameterization problem for automated road feature extraction models. Using a line-length buffer approach, a measure of agreement between extracted road features and reference road features is defined. An automated approach for deriving this agreement index is presented and implemented as a parameterization model. Coupling the implemented parameterization model with an existing transportation feature extraction model is demonstrated. Although the solution was designed for a road feature extraction model (FEM), the conceptual design could be applied to other linear FEMs, such as models for streams, fault lines, and isolines. [ABSTRACT FROM AUTHOR]

The "Wet Tropics World Heritage Area" in Far North Queens- land, Australia consists predominantly of tropical rainforest and wet sclerophyll forest in areas of variable relief Previous maps of vegetation communities in the area were produced by a labor-intensive combination of field survey and air-photo interpretation. Thus, the aim of this work was to develop a new vegetation mapping method based on imaging radar that incorporates topographical corrections, which could be repeated frequently, and which would reduce the need for detailed field assessments and associated costs. The method employed a topographic correction and mapping procedure that was developed to enable vegetation structural classes to be mapped from satellite imaging radar. Eight JERS-1 scenes covering the Wet Tropics area for 1996 were acquired from NASDA under the auspices of the "Global Rainforest Mapping Project. " JERS scenes were geometrically corrected for topographic distortion using an 80 in DEM and a combination of polynomial warping and radar viewing geometry modeling. An image mosaic was created to cover the Wet Tropics region, and a new technique for image smoothing was applied to the JERS texture bands and DEM before a Maximum Likelihood classification was applied to identify major land-cover and vegetation communities. Despite these efforts, dominant vegetation community classes could only be classified to low levels of accuracy (57.5 percent) which were partly explained by the significantly larger pixel size of the DEM in comparison to the JERS image (12.5 m). In addition, the spatial and floristic detail contained in the classes of the original validation maps were much finer than the JERS classification product was able to distinguish. In comparison to field and aerial photo-based approaches for mapping the vegetation of the Wet Tropics, appropriately corrected SAR data provides a more regional scale, all-weather mapping technique for broader vegetation classes. Further work is required to establish an appropriate combination of imaging radar with elevation data and other environmental surrogates to accurately map vegetation communities across the entire Wet Tropics. [ABSTRACT FROM AUTHOR]

This paper presents an integrated remote sensing—GIS approach for identifying areas vulnerable to rapid erosion in Almería Province, Southeast Spain. Earth Resources Satellite (ERS) Synthetic Aperture Radar (SAR) multi-temporal interferometric coherence imagery has been used to detect rapid erosion that causes random changes in the micro-topography of a land surface. These small scale changes result in reduced coherence of the radar signals between the initial and eroded states. However, the detection solely based on coherence imagery is not exclusive because other factors may cause losing coherence as well. A Geographic Information System (GIS) is then used to derive criteria for a set of environmental conditions favourable to rapid erosion from multi-datasets including Landsat Enhanced Thematic Mapper (ETM+) imagery and geological maps. The areas predicted to be most vulnerable to rapid erosion across the imagery are then identified where hard evidence of low coherence coincides with favourable conditions. [ABSTRACT FROM AUTHOR]

A complete, high-resolution coastline of Antarctica, extracted from an orthorectified mosaic of Radarsat-1 synthetic aperture radar (SAR) images through a sequence of automated image processing algorithms, is presented. A locally adaptive thresholding method is used to segment the orthorectified SAR images; while image-object formation and labeling, and edge-tracing techniques are used to process the segmented images into vector-based cartographic products of coastline, defined here as the boundary between continental ice or rock exposures and sea ice covered ocean. The absolute accuracy of planimetric positioning of the resultant coastline is estimated to better than 130 m, and its spatial resolution (25 m) is adequate for supporting cartographic and scientific applications at 1:50,000 scale. This radar-image-derived coastline gives an accurate description of geometric shape and glaciological characteristics of the Antarctic coasts and also provides a precise benchmark for future change-detection studies. [ABSTRACT FROM AUTHOR]

Focuses on the lidar industry in the United States. Association between the rate of technological change and Moore's Law; Advice for firms considering entry into the lidar business; Worldwide installed base of lidar systems; Increase in the airborne lidar data file size.

In this paper hyperspectral (AVIRIS) and radar (AIRSAR) aerial data acquired over urban environments are considered. The information available from each sensor was extracted and merged to improve the 3D profile reconstruction of builtup areas. Two classification schemes were evaluated for AVIRIS data clustering, while the effect of the radar view angle was considered in assessing the quality of the associated digital elevation models. A detailed analysis of what is possible to extract and to what extent these data are useful was also produced, considering precise 2D and 3D ground truth of the UCLA campus. [ABSTRACT FROM AUTHOR]

Multi-temporal NOAA AVHRR and RADARSAT images depicting flood conditions in the Nenjiang and Songhua River Basins during the summer of 1998 were used to monitor the floods and assess the damage. A knowledge-based RBFNN model was developed to extract the dynamic flooding information from AVHRR images. To map the flooded area more accurately, three RADARSAT Scan SAR images acquired at different times were used. Threshold-based image segmentation and texture analysis methods were utilized to process the SAR image, and to extract information on flooding duration and depth. This study shows that the integrated use of different remote sensing platforms images can provide real-time and all-weather monitoring of floods and provide necessary information for flooding control and disaster relief. [ABSTRACT FROM AUTHOR]

The Spaceborne Imaging Radar—C/X-band Synthetic Aperture Radar (SIR-C/X-SAR) mission was a cooperative endeavor of the United States, Germany, and Italy. The SIR-C/X-SAR scientific research program was a large international cooperative program of radar for Earth observation in which 13 countries participated, including China. SIR-C/X-SAR, with the ability to acquire polarimetric SAR and interferometric SAR data, was the first spaceborne radar to operate simultaneously at several frequencies and polarizations, representing the most advanced civilian SAR system for Earth observation. This paper will present some results of the SIR-C/X-SAR program made in China. The emphasis is placed on aerial and ground synchronous experiments with SIR-C/X-SAR overpasses, SAR penetration studies for dry sands, and SIR-C/X-SAR data applications in relevant fields and different areas, e.g., discovering the volcanoes of the Kunlun Mountains, detecting geological features underneath vegetation canopies, and revealing the Great Wall segments of the Ming and Sui dynasties. The paper also introduces the use of the Chinese airborne L-band SAR system, developed by the Chinese High Technology Program, for flood monitoring in 1998. [ABSTRACT FROM AUTHOR]

The article announces that the Intermap Technologies Corp. was awarded top honors in the Photogrammetry/Elevation Data Generation Category at the Management Association of Private Photogrammetric Surveyors (MAPPS) Geospatial Products and Services Excellence Awards Ceremony, held on July 16 in Bretton Woods, New Hampshire. It reports that Intermap's "Torino 2006 IFSAR Mapping Project" was conducted on behalf of the Italian Mapping agency Istituto Geografico Militare (IGM) using the company's proprietary Interferometric Synthetic Aperture Radar (ISFAR) digital mapping technology. It mentions that the Torino 2006 demonstrate an innovative use of their proprietary radar mapping technology to exceed customer demands.

Various widely used radar image processing algorithms require considerable computing resources but can take advantage of a parallel implementation. We focus on the shape-from-shading (Sfs) algorithm in its application to radar images. A given serial version of the SfS algorithm was parallelized and improved to handle large images. We experimented with parallelization techniques such as data decomposition, the manager/worker method, and dynamic load balancing with double buffering. The parallel version of Sfs was ported to two supercomputers: Meiko's CS-2HA and Intel's PARAGON XP/S-A4 distributed memory machines, and to a cluster of workstations (Cow) made up of Silicon Graphics' Indies. Important results concerning the performance of the parallel Sfs implementation with those architectures are presented and comparedto each other, showing that 14 processors can speed up sfs by up to 13 times over the use of a single processor. [ABSTRACT FROM AUTHOR]