This PDF file contains the front matter associated with SPIE Proceedings Volume 8536, including the Title Page, Copyright information, Table of Contents, Introduction, and Conference Committee listing.

In this work we propose two pixel-wise change detection techniques for unsupervised network infrastructure monitoring in SAR imagery applications. The first algorithm is inspired by a well known algorithm, named RX, proposed to deal with anomaly detection in optical images. The second algorithm is a statistical based procedure, which exploits a nonparametric approach for estimating the probability density function of the image pair. In order to test and validate the proposed methods, we analyze a spot light amplitude COSMO-SkyMed image pair at one-meter spatial resolution acquired on a complex urban scenario. Experimental results obtained on the available dataset are presented and discussed.

This paper describes the most recent achievements in speckle reduction of COSMO-SkyMed (CSK ®) synthetic aperture radar (SAR) data. An advanced multresolution despeckling filter, based on undecimated wavelet transform (UDWT) and maximum a-posteriori (MAP) estimation has been specialized and optimized to CSK ® data, both single- and multilook. The tradeoff between performances and computational complexity has been investigated: Laplacian-Gaussian and generalized Gaussian (GG) priors for MAP estimation in UDWT domain differ by one order of magnitude in computation cost. The former are more complex but yield the best results attainable with a Bayesian estimation carried out in the UDWT domain. Pre-processing of point targets and segmentation of wavelet planes has been exploited to effectively handle the heterogeneity of the data. The effects of multilooking have been investigated. Starting from single-look complex (SLC) data, the spatial correlation coefficients (CC) of speckle and the equivalent number of looks (ENL) of all products have been theoretically calculated. It is proven that, besides having an inherently better radiometric quality, multilooked products exhibit a lower spatial correlation of speckle than single-look products, thereby better falling under the assumption of uncorrelated speckle, exploited by the majority of model-based despeckling filters, included those used in the present work. The effects of spatial resampling have been investigated as well. Unlike MAP filters in spatial domain (e.g. the Gamma-MAP filter), MAP filters in wavelet domain are little sensitive to resampling, because the fundamental hypotheses on which they rely are not violated because of resampling. Comparisons with the state of the art are also provided and shown to be more than favorable. Besides traditional supervised methods to evaluate the quality of despeckling, a novel procedure, fully automated, based on bivariate analysis of noisy and denoised image has been devised. Its results agree both with visual analysis and with manual measurements.

The Multi-Chromatic Analysis (MCA) uses interferometric pairs of SAR images processed at range sub-bands located at different spectrum positions, and explores the phase trend of each pixel in the frequency domain. The phase of stable scatterers evolves linearly with the sub-band central wavelength, the slope being proportional to the absolute optical path difference. Consequently, both phase uwrapping and height computation can be performed on a pixel by pixel basis without spatial integration. Recently the technique has been used to derive ground elevation by processing interferometric pairs acquired in Spotlight mode by both TerraSAR-X and COSMO-SkyMed satellite missions. However, further potential applications are possible. In particular, this work is aimed at experimenting the use of MCA for measuring the optical path between the SAR sensor and the scene by processing a single SAR acquisition. In this configuration, the slope of the phase trend along frequencies depends on the full optical path. In order avoid liasing, we adopted a processing scheme which consists in subtracting from the SAR image phase a term proportional to the distance computed through inverse geocoding. Assuming negligible the positioning errors, the validation of this approach can be performed by comparing the distance measured by MCA with the atmospheric delay computed through analytical models. We carried out a feasibility study aimed at evaluating the maximum value for the errors in satellite and target positions, allowed to perform the reliable validation. Then, in order to reduce the error in the target positions and to guarantee good phase stability, we selected SAR acquisitions which include artificial corner reflectors to be used for MCA processing and the following validation procedure. We present results obtained by exploiting two corner reflectors visible within two TerraSAR-X images acquired in Spotlight mode over Venice Lagoon.

When the angle covered by the target as seen by the sensor is sufficiently narrow, the ISAR imaging technique can be viewed as the tomographic reconstruction process of a synthetic image, which maps on space coordinates the energy backscattered from the target along different observation lines. As an alternative to the Fourier slice based techniques, in this paper a map reconstruction method from its two-dimensional (2-D) Laguerre Gauss (LG) expansion is presented. The LG expansion was already used for efficiently analyzing the orientation and the cross profile of linear patterns in images within a Maximum Likelihood approach. In particular, LG basis functions and expansion coefficients rotate by a simple phase shift, proportional to the rotation angle and the circular harmonic order. In addition, the LG expansion is linearly related to the 2-D Hermite Gauss (2-D HG) expansion, which allows for efficient numerical realizations and a compact model of the returns of Ultra Wide Band (UWB) waveforms, naturally suited for this high resolution scheme. The LG or the 2-D HG truncated expansion of the map is finally computed by the linear Least Squares interpolation of a set of matched filter outputs and used for reconstructing the backscattering map. Several system aspects of the LG ISAR method are discussed and computer experiments with tight concentrations of point scatterers illuminated by UWB pulse trains are presented.

In this paper we first highlight a new approach to the analysis of compact polarimetric imaging radar data, based on decomposition theory. We then use a time series of Radarsat-2 quadpol data acquisitions collected over the autumn and winter of 2011/12 for a calibrated forest test site near Hinton in Alberta, Canada, which contains a mixed forest, seminatural vegetation and mountainous terrain environment. This data is collected in the new wide-swath quadpol mode FQW of Radarsat-2, which matches the wider range swath capability of any future compact mode. This data is first used to simulate compact mode using circular polarization transmit and dual linear receive and the co-registered multitemporal stack then employed for a rule-based classifier to determine land-use types compared against a reference landuse map. We compare the information obtained from compact against a standard dualpol linear transmit and dual linear receive, as proposed for example in the ESA Sentinel missions, to confirm the utility of using circular polarization for enhanced land-use products at C-band.

The goal of this study is to examine the potential of deriving information comparable to quad-polarization synthetic aperture radar (SAR) data from dual-polarization data. Multi-polarization data have shown the potential to increase further the ability of extracting physical quantities of observation targets. Above all, quad-polarization data have more information than others, but they are relatively few in number compared with single or dual-polarization data. Although there are many SAR systems capable of quad-polarization observation, most of them are operated mainly on single or dual-polarization mode because of limited data transfer rate, area of coverage, required resolution, other system restriction, and so on. Thus, there is a certain trade- off between data availability and multi polarization. Therefore, we focused on dual-polarization as a good compromise between single and quad-polarization data. In this study, we investigated possible alternative parameters that can be derived from HH-VV dual-polarization data and can serve as substitutes for cross-polarization component in quad-polarization data. Experiments are performed using the Advanced Land Observation Satellite-Phased Array L-band SAR (ALOS-PALSAR) quad-polarization data. The cross polarization component in the data is used as benchmark for the alternative parameters.

This work focuses on the retrieval of Titan’s dune field characteristics addressing different radar modes. The main purpose of the proposed work is to exploit a possible synergy between SAR and altimeter acquisitions modes to provide information about dune field. Cassini has performed 86 Titan flybys in which several observations of dune fields have been collected in altimetry mode. There are several cases in which SAR and altimeter have been acquired over same areas covered by dune fields, such as during T28 (SAR) and T30 (altimeter) flybys. Altimetry together with SAR data have been used to derive the rms slopes of dunes (large scale) over Fensal area, this information has been employed to calculate SAR incidence angle with respect to dunes. We extracted backscattering coefficients of bright and dark areas detected in the analyzed SAR image in order to evaluate the angular response of scattering. Through the Geometric Optics model we retrieve roughness values (small scale rms slope) for both dune bright and dark areas.

Differential Synthetic Aperture Radar Interferometry (DInSAR) represents a well-established remote sensing technique for the investigation of ground deformation phenomena.Among the DInSAR techniques, the Small BAseline Subset (SBAS) approach exploits ground surface at two mapping scales, low and high resolution, and allows the detection and monitoring of local deformation processes that may affect single buildings or man-made structures in urban areas. This work investigates the capability improvement of the SBAS-DInSAR technique to analyse deformation processes in urban areas by exploiting SAR data acquired by the Cosmo-SkyMed (CSM) constellation in comparison with the results obtained from data of first generation ERS/ENVISAT radar systems of he European Space Agency. In particular, we extracted mean deformation velocity maps as seen by the three different radar systems and, for each coherent pixel, we retrieved the corresponding displacement time series. Our analysis was focused on the Torrino area where independent studies had already revealed significant deformation signals testified by the serious damages on many buildings in the area. Moreover, in order to understand the causes of the CSM observed displacement rates, reaching few cm per year, we also performed a comparative analysis between DInSAR products and independent information derived from electrical resistivity tomography data and geological maps.

We have studied the utility of high resolution synthetic aperture radar for levee monitoring using UAVSAR data collected over the dikes and levees in California’s Sacramento-San Joaquin Delta and the lower Mississippi River. Our study has focused on detecting and tracking changes that are indicative of potential problem spots, namely deformation of the levees, subsidence along the levee toe, and seepage through the levees, making use of polarimetric and interferometric SAR techniques. Here we present some results of those studies, which show that high resolution, low noise SAR imaging could supplement more traditional ground-based monitoring methods by providing early indicators of seepage and deformation.

Detected oil spills are usually classified according to confidence levels. Such levels are supposed to describe the probability that an observed dark feature in the satellite image is related to the actual presence of an oil spill. The Synthetic Aperture Radar (SAR) derived oil spill detection probability estimation has been explored as an intrinsic aspect of oil spill classification, which fundamentally computes the likelihood that the detected dark area is related to an oil spill. However, the SAR based probability estimation should be integrated with additional criteria in order to become a more effective tool for the End Users. As example, the key information for the final users is not the confidence level of the detection “per se” but the alert (i.e. the potential impact of the pollution and the possibility to catch the polluter red-handed) that such detection generates. This topic was deeply discussed in the framework of the R and D European Group of Experts on remote sensing Monitoring of marine Pollution (EGEMP) and a paper was published in 2010. The newly established EMSA CleanSeaNet service (2nd generation) provides the alert level connected to the detection of a potential oil spill in a satellite image based on the likelihood of being an oil spill in combination with impact and culprit information.

In maritime applications involving estimation of radar sea clutter properties, non-sea-clutter targets and transitions between statistically different oceanographic conditions in the estimation window may lead to inaccurate modeling. Referring to mixtures in the estimation window as contamination, this work introduces a novel sea clutter contamination test based on log-cumulants from Mellin kind statistics [1]. It measures the significant deviation in log-cumulant space due to the contamination, and appears to be an effective tool for improving the sea clutter estimation or to be a direct first-stage target detector. The proposed contamination test is examined with real single look complex (SLC) fine resolution quad-polarimetric Radarsat-2 synthetic aperture radar (SAR) measurements, from the Norwegian Sea, under various oceanographic conditions.

The Sentinel-1 mission will offer the opportunity to obtain C-band radar data characterized by short revisit time, thus allowing the generation of frequent soil moisture maps. This paper presents a multitemporal algorithm that exploits such a short revisit time to perform an operational soil moisture mapping. The procedure assumes the availability of a time series of SAR images that is integrated within a retrieval algorithm based on the Bayesian maximum posterior probability statistical criterion. Preliminary results show that the performances of the multitemporal algorithm are better than those provided by a standard monotemporal one. Its implementation can be demanding in terms of computer resource, but a pipeline processing can be implemented in order to fulfill the temporal requirements of the users.

In this work, the characterization and extraction of snowpack parameters from X-band SAR imagery has been addressed. A preliminary sensitivity analysis was carried out by exploiting datasets of snowpack parameters (depth, density, snow grain radius, temperature and wetness) collected from the available meteorological stations on two test sites in the Italian Alps. This is a crucial step, since it provides indications on the sensitivity of the input features (i.e., backscattering coefficients and ancillary data) to variations in the target snow parameters. X-band data has been found to contribute to retrieval of the snow water equivalent under specific conditions, i.e., that the snow cover is characterized by a snow depth of roughly 60-70 cm (snow water equivalent <100-150mm) and with relatively large crystal dimensions. After this phase, the retrieval process is addressed. The method is based on a Neural Network retrieval algorithm trained by using a DRTM electromagnetic model in order to estimate the snow water equivalent. The proposed approach also makes use of the threshold criterion for detecting the wet snow cover extent on which the retrieval cannot be performed. The method has been developed and calibrated on the Cordevole plateau located in the Dolomites, Eastern Italian Alps, where ground data collected by the Avalanche Center in Arabba and meteorological data measured by a network of automatic stations were available. The method was then validated on a second site located in South Tyrol region (Eastern Italian Alps), where also manual and automatic ground measurements of snow parameters were available. The activity was carried out in the framework of two projects funded by the Italian Space Agency (HYDROCOSMO and SNOX) for the exploitation of X-band satellite SAR data for the analysis and characterization of snow in mountain areas.

This study presents an analysis on the retrieval of soil moisture content from medium resolution wide swath SAR images for monitoring regional scale spatial and temporal patterns of this variable in the challenging Alpine environment. The possibility to retrieve soil moisture content from satellite high resolution SAR imagery in Alpine areas was successfully investigated in a previous contribution. The rationale behind this work is the fact that multi-scale and multi-sensor products could lead to a more general and comprehensive understanding of the phenomena at the ground, since different perspectives and trade-offs among spatial and temporal resolution can be exploited. In more detail, the analysis proposed here aims at: i) assessing the effectiveness of the proposed retrieval algorithm when applied to medium resolution wide swath SAR imagery; and ii) investigating the feasibility of mapping spatial patterns and temporal dynamics of soil moisture content at a regional scale. ENVISAT ASAR Wide Swath images acquired over the Alto Adige/Süd Tirol Province during the years 2010-2011 are used for the experimental analysis. Achieved results are compared with ground measurements and meteorological data, indicating good agreement in terms of both spatial distribution and temporal dynamics of estimated soil moisture content values.

Soil moisture retrieval from SAR images is always affected by speckle noise, model errors and uncertainties associated to soil parameters, which impact negatively on the accuracy of soil moisture estimates. A Bayesian approach has been proposed to deal with these issues. As a natural advantage of the Bayesian approach, prior information about soil condition can be easily included. Based on simulations, the effect of prior information has been analyzed. It follows from simulations using the Oh's model that the soil moisture estimator is very sensitivity to the roughness prior.

An empirical algorithm (XWAVE) to derive integrated sea state parameters from TerraSAR-X (TS-X) SAR data is developed and validated using NOAA in-situ buoy wave measurements. The comparison for significant wave height and peak wave length was performed as well for deep water locations in open ocean as well as for coastal areas. The significant wave height validation results show a correlation of 0.93% and a scatter index of 0.21 when using in-situ wave buoy data. Verification of the TS-X derived peak wavelength against in-situ buoy data resulted in a correlation of 0.96 and a scatter index of 0.13. The main highlights of TS-X imagery are a higher resolution of up to 1m, when compared to conventional C-band SAR data and a reduction of non-linear imaging effects of a moving target by lower platform altitude. Thus, ocean waves with wavelength less than 30m are detectable. This makes TS-X particularly useful to observe coastal areas where complex bathymetry strongly impacts the approaching waves. In this paper, TS-X images acquired in different coastal areas are presented, including three cases of the German coast and one case near the coast of the Azores Archipelago in the North Atlantic Ocean. Wave fields are derived from the TS-X imagery using the proposed XWAVE algorithm and compared not only to in-situ buoy wave measurements but also to results of a high resolution numerical wave model. The objective was to study the quality of significant wave height field estimation in the spatial domain in highly variable conditions which are typically dominant in coastal areas. The results show that the empirical XWAVE algorithm allows estimating wave fields from TS-X data with high resolution thus showing the spatial information on wave variations. Therefore it is a new useful tool to characterize sea state in coastal areas by remote sensing.

Marine oil spills are an important environmental problem, and satellite SAR remote sensing have become a valuable tool for the detection and monitoring of these spills. Natural phenomena with similar appearance as oil in SAR images, producing false detections, compose a challenge for oil spill observation services. One such lookalike phenomena is biogenic slicks produced by marine organisms. In this study we evaluate multi-polarization features for oil spill characterization and oil versus biogenic slick discrimination. During large-scale oil-on-water exercises conducted in the North Sea in June 2011 and June 2012, both mineral oil and plant oil were released and imaged by Radarsat-2 in Fine Quad-polarization mode. The plant oil will form a lm resembling biogenic slicks. The mineral oil spill and simulated look-alike are in this study compared based on multi-polarization features, combining the information in HH and VV channels. The polarimetric measurements from 2011 have earlier been analysed, and a potential for discrimination between mineral oil and biogenic slicks is found. The aim of the current study is to repeat the polarimetric analysis on the new independent data set from 2012. Preliminary results of the 2012 data set reveal both internal and between slick type variations, giving support to our previous findings from 2011.

The coastal marine habitat is an important and delicate environment from economical, ecological, political and security point of view, therefore its integrity has to be monitored and preserved from dangerous human activities. Recent studies have demonstrated that the 42% of the Italian Coast is eroding because of the increase of the sea-level height and the reduced solid transport from rivers to sea, hence there is an important requirement for tools capable to provide a synoptic view of the coastal area. COSMO-SkyMed SAR products with their very high resolution and short revisit time, can represent a breakthrough on coastline delineation and mapping, also overcoming the problems related to cloud cover or large extension of the areas. While in remotely sensed imagery including visible bands the specific coastline extraction task may be recognized as not particularly complex, this does not hold for SAR images in which the backscattering from the water can be influenced by different effects due to the wind and the wave modulation, determining a not easy discrimination between sea and land. In this research activity a new automatic technique based on Pulse Coupled Neural Networks (PCNN) has been developed to detect the coastal boundaries, moreover a local tracing procedure exploiting statistical information has been designed to properly extract the coastline. The results have been validated through a GPS survey and an assessment of the real impact of the proposed procedure in coastal mapping application has been carried out.

A brief overview of studies on the problem of marine slicks and their radar signatures carried out at the Institute of Applied Physics RAS is given. Synthetic aperture radar (SAR) slick imagery and physical mechanisms of slick formation due to different oceanic processes are considered, including slicks due to oceanic internal waves, non uniform coastal currents, algae bloom. Results of field studies slicks associated with these processes, and theoretical analysis of physical mechanisms of the slicks in SAR imagery are presented.

The increased amount of available Synthetic Aperture Radar (SAR) images acquired over the ocean represents an extraordinary potential for improving oil spill detection activities. On the other side this involves a growing workload on the operators at analysis centers. In addition, even if the operators go through extensive training to learn manual oil spill detection, they can provide different and subjective responses. Hence, the upgrade and improvements of algorithms for automatic detection that can help in screening the images and prioritizing the alarms are of great benefit. In the framework of an ASI Announcement of Opportunity for the exploitation of COSMO-SkyMed data, a research activity (ASI contract L/020/09/0) aiming at studying the possibility to use neural networks architectures to set up fully automatic processing chains using COSMO-SkyMed imagery has been carried out and results are presented in this paper. The automatic identification of an oil spill is seen as a three step process based on segmentation, feature extraction and classification. We observed that a PCNN (Pulse Coupled Neural Network) was capable of providing a satisfactory performance in the different dark spots extraction, close to what it would be produced by manual editing. For the classification task a Multi-Layer Perceptron (MLP) Neural Network was employed.

A polarimetric model to observe metallic targets at sea in dual-polarimetric Synthetic Aperture Radar (SAR) data is here first investigated in X-band. The model gives an understanding of the different symmetry properties that characterize sea surface with and without metallic targets in terms of the correlation between like- and cross-polarized channels. Following the sensitivity study, a simple and very effective filter is designed to observe targets at sea. The model is tested on both HH/HV and VV/VH Single look Slant range Complex (SSC) dual-polarimetric TerraSAR-X data and the filter’s outputs are verified against available Automatic Identification System (AIS) ground truth messages.

The signal model of wideband monopulse radar is developed according to spotlight synthetic aperture radar (SAR) geometry to study the three-dimensional (3-D) imaging for ship targets on sea surface. An efficient 3-D monopulse imaging algorithm based on multi-frame one-dimensional (1-D) high resolution range profiles (HRRP) is proposed, where no complex phase compensation is needed. Simulation results validate the algorithm and show that the reconstructed 3-D images represent more detailed backscattering characteristics of the targets, demonstrating promising features for radar recognition.

Synthetic Aperture Radar (SAR) systems represent the most powerful tool to monitor flood events because of their allweather capability that allows them to collect suitable images even in cloudy conditions. The quality of flood monitoring using SAR is increasing thanks to the improved spatial resolution of the new generation of instruments and to the short revisit time of the present and future satellite constellations. In particular, the COSMO-SkyMed mission offers a unique opportunity to obtain all weather radar images characterized by short revisit time. To fully exploit these technological advances, the methods to interpret images and produce flood maps must be upgraded, so that an accurate interpretation of the multitemporal radar signature, accounting for system parameters (frequency, polarization, incidence angle) and land cover, becomes very important. The COSMO-SkyMed system has been activated several times in the last few years in consequence of the occurrence of flood events all over the world in order to provide very high resolution X-band SAR images useful for flood detection purposes. This paper discusses the major outcomes of the experiences gained from using COSMO-SkyMed data for the purpose of near real time generation of flood maps. A review of the mechanisms which determine the imprints of the inundation on the radar images is provided and the approach designed to process the data and to generate the flood maps is also summarized. Then, the paper illustrates a number of significant case studies in which flood events have been monitored through COSMO-SkyMed images. These examples demonstrate the potential of the COSMO-SkyMed system and the suitability of the approach developed for generating the final products, but they also highlight some critical aspects that require further investigations to improve the reliability of the flood maps.

An investigation to the appropriate feature for SAR image registration is conducted. The commonly-used features such as tie points, Harris corner, the scale invariant feature transform (SIFT), and the speeded up robust feature (SURF) are comprehensively evaluated in terms of several criteria such as the geometrical invariance of feature, the extraction speed, the localization accuracy, the geometrical invariance of descriptor, the matching speed, the robustness to decorrelation, and the flexibility to image speckling. It is shown that SURF outperforms others. It is particularly indicated that SURF has good flexibility to image speckling because the Fast-Hessian detector of SURF has a potential relation with the refined Lee filter. It is recommended to perform SURF on the oversampled image with unaltered sampling step so as to improve the subpixel registration accuracy and speckle immunity. Thus SURF is more appropriate and competent for general SAR image registration.

This paper is dedicated to investigate the appropriate parameter retrieval algorithm for feature-based synthetic aperture radar (SAR) image registration. The widely-used random sample consensus (RANSAC) is observed to be instable for its inappropriate estimation strategy and loss function for SAR images. In order to enable a stable and robust registration for SAR, an extended fast least trimmed squares (EF-LTS) is proposed which conducts the registration by least squares fitting at least half of the correspondences to minimize the squared polynomial residuals instead of fitting the minimal sampling set to maximize the cardinality of the consensus set as RANSAC. Experiment on interferometric SAR image pair demonstrates that the proposed algorithm behaves very stably and the obtained registration is averagely better than that by RANSAC in terms of cross-correlation and spectral SNR. By this algorithm, a stable estimation for any kind of 2D polynomial warp model with high robustness and accuracy can be efficiently achieved. Thus EF-LTS is more appropriate for SAR image registration.

In this paper, we use full polarization SAR image and investigation data on site to analysis the oil rigs. Firstly, we do the pauli decomposition, then synthesize the pauli coefficient into RGB image. According different feature of oil target, the polar signature (cross and co-) are calculated. In the pauli RGB image, the oil spill is obvious and almost all the moving ship had the same” azimuth tail”, and this character can be used to moving ship detection. Secondly, the scattering feature of oil rigs was estimated and classification had been done.

In this study, we applied InSAR time series analysis using PALSAR and TerraSAR-X data to Zonguldak Hardcoal Basin in Republic of Turkey in order to monitor mining induced surface displacement. Zonguldak coal area is located along the Black Sea 240 km eastward from Istanbul. Recently, ground deformation caused by underground exploration has come to the surface, and it has been destroying roads and buildings. We utilized PALSAR and TerraSAR-X data for the detail analysis on the recent land subsidence induced by mining activities. PALSAR data in the fine beam mode were obtained from an ascending orbit, TerraSAR-X data in the StripMap mode were from a descending orbit. The vertical and the east-west displacement were calculated by the composition of the deformation vectors of PALSAR and TerraSAR-X. Additionally, the source depth of the main anomaly detected near the campus of Zonguldak Karaelmas University (ZKU) was estimated by vector analysis using the vertical and east-west displacement. As a result, it was approximately 80 to 100 meters under the sea level. On the other hand, the depth of coal production zones is recorded 300 to 560 meters under the sea level. Thus, it was supposed that the anomaly near the university had been caused by not only mining activities but also the other factor in the shallower geological formation. Because some historical documents said that some caves existed in the limestone formation bedded over coal formations and domestic wastewater is injected into these caves, perhaps the cause of land subsidence is considered the expansion of caves’ space resulted from the solution of limestone rocks by the wastewater injection.

Polarimetric Interferometric Synthetic Aperture Radar (Pol-InSAR) technique has been demonstrated its success in the estimation of forest height. Terrain slope is a factor that always affects the estimation accuracy. In this paper, the analysis of terrain influences was carried out in view of polarimetric orientation and local incidence angle shift, respectively. For the former, the relation between forest height estimation error and polarimetric orientation shift was derived by data simulation approach. For the latter, an analytical equation was derived by the theoretical analysis to describe the relation between true and estimated forest height. Then, possible methods for correcting terrain influences were presented, which including: 1) Design airborne experiment flight track along mountain ridge. 2) Utilize Pol-InSAR optimal coherences for forest height inversion if the computational efficiency is not an issue. 3) Revise the estimated forest height from RVoG model inversion, where the range slope can be calculated by InSAR dataset or a priori DEM.

A procedure is proposed to reconstruct the radar cross section (RCS) of interested targets from synthetic aperture radar (SAR) images. Key factors in imaging are considered for exact RCS reconstruction, including image defocusing from target motion, system over-sampling, window function, zero-padding and image calibration. Experimental results for both numerically calculated inverse SAR (ISAR) and spaceborne SAR image demonstrate the effectiveness and accuracy of the proposed technique.

Extraction of curvilinear features from synthetic aperture radar (SAR) images is important for automatic recognition of various targets, such as fences, surrounding the buildings. The bright pixels which constitute curvilinear features in SAR images are usually disrupted and also degraded by high amount of speckle noise which makes extraction of such curvilinear features very difficult. In this paper an approach for the extraction of curvilinear features from SAR images is presented. The proposed approach is based on searching the curvilinear features as an optimum unidirectional path crossing over the vertices of the features determined after a despeckling operation. The proposed method can be used in a semi-automatic mode if the user supplies the starting vertex or in an automatic mode otherwise. In the semi-automatic mode, the proposed method produces reasonably accurate real-time solutions for SAR images.