海洋表面油膜的合成孔径雷达图像检测

论述了海洋油气藏烃类渗漏在海洋表面形成的浮油膜特征，并讨论了海面油膜的存在对海面短重力波的影响和海洋合成孔径雷达（ＳＡＲ）图像的后向散射系数σ°的调制，及油膜在ＳＡＲ图像上的特征，探讨了对ＳＡＲ图像进行处理以降低雷达斑点噪声，增强图像上油膜特征的方法。指出了ＳＡＲ图像用于检测海面油膜的可行性。试图建立利用ＳＡＲ图像检测海洋油气藏烃类渗漏形成的海面油膜的依据。

工程地质信息化和自动成图技术研究

为实现工程地质信息化管理,以SQL Server 2008数据库为平台,建立地质信息数据库,对庞大的地质勘测信息实施科学存储和统一管理。针对钻孔柱状图通常采用传统的手工绘制、效率低而且不易修改等问题,以VS2010为开发工具,利用地质信息数据库系统,开发了钻孔柱状图的自动成图系统和用户友好型界面。针对生成的钻孔柱状图不能够根据钻孔长度与图框关系智能分段等问题,提出了分段算法,实现了钻孔柱状图自动分段显示的功能,并以乌江构皮滩工程为依托,对新方法的有效性和可行性进行验证。结果表明:变孔径成图算法解决了变孔径钻孔柱状图的显示问题,生成能反应实际情况的变孔径钻孔柱状图;该方法成图方便快捷,图形美观协调,满足实际工作的需要。研究成果对工程地质信息化具有重要的参考价值。

一种用于射电天文图像重建的自适应尺度算法

为了解决在成像过程中反卷积点扩展函数所带来的模糊效应,提出一种新的图像重建算法.新算法利用高斯函数分解模型,能够有效解决基于Delta函数分解模型所引起的问题,并在重建延展源和致密源方面具备更好的效果.通过VLA模拟观测数据对算法进行测试,并与现有的2类图像重建算法进行比较,结果表明,新算法能够有效平衡现有的反卷积算法的计算复杂度和性能问题,能对图像进行更加精确建模,具有更好的图像重建性能.

38~×变倍比宽波段电视镜头光学系统设计

针对大视场高变倍比宽波段光学系统的特点,运用换根方式机械补偿光学系统设计技术和h-hp图及孔径图分析法,实现了38×变倍比,解决了大视场宽波段系统的初、高级像差难以平衡的难题,同时提出运用凸轮程序公式编程计算不齐焦段的补偿组实际位移量,对Code V的凸轮曲线设计值进行修正的方法,保证了高变倍比光学系统的齐焦性。

SAR IMAGE RECOGNITION BASED ON MULTI-ASPECT OF SHADOW INFORMATION

The traditional synthetic aperture radar（SAR） image recognition techniques focus on the electro magnetic （EM） scattering centers, ignoring the important role of the shadow information on the SAR image recognition. It is difficult to classify targets by the shadow information independently, because the shadow shape is dependent on the radar aspect angle, the depression angle and the resolution. Moreover, the shadow shapes of different targets are similar. When the multiple SAR images of one target from different aspects are available, the performance of the target recognition can be improved. Aimed at the problem, a multi-aspect SAR image recognition technique based on the shadow information is developed. It extracts shadow profiles from SAR images, and takes chain codes as the feature vectors of targets. Then, feature vectors on multiple aspects of the same target are combined with feature sequences, and the hidden Markov model （HMM） is applied to the feature sequences for the target recognition. The simulation result shows the effectiveness of the method.

Underwater topography detection of Taiwan Shoal with SAR images

Under suitable conditions of tidal current and wind, underwater topography can be detected by synthetic aperture radar (SAR) indirectly. Underwater topography SAR imaging includes three physical processes: radar ocean surface backscattering, the modulation of sea surface short wave spectrum by the variations in sea surface currents, and the modulation of sea surface currents by the underwater topography. The first process is described usually by Bragg scattering theory because the incident angle of SAR is always between 20°-70°. The second process is described by the action balance equation. The third process is described by an ocean hydrodynamic model. Based on the SAR imaging mechanism for underwater topography, an underwater topography SAR detection model and a simplified method for its calculation are introduced. In the detection model, a two-dimensional hydrodynamic model – the shallow water model is used to describe the motion of tidal current. Due to the difficulty of determining the expression of SAR backscattering cross section in which some terms can not be determined, the backscattering cross section of SAR image used in the underwater topography SAR detection is pro-processed by the simulated SAR image of the coarse-grid water depth to simplify the calculation. Taiwan Shoal, located at the southwest outlet of Taiwan Strait, is selected as an evaluation area for this technique due to the occurrence of hundreds of sand waves. The underwater topography of Taiwan Shoal was detected by two scenes of ERS-2 SAR images which were acquired on 9 January 2000 and 6 June 2004. The detection results are compared with in situ measured water depths for three profiles. The average absolute and relative errors of the best detection result are 2.23 m and 7.5 %, respectively. These show that the detection model and the simplified method introduced in the paper is feasible.

Monitoring cyanobacteria-dominant algal blooms in eutrophicated Taihu Lake in China with synthetic aperture radar images

Monitoring algal blooms by optical remote sensing is limited by cloud cover.In this study,synthetic aperture radar(SAR) was deployed with the aim of monitoring cyanobacteria-dominant algal blooms in Taihu Lake in cloudy weather.The study shows that dark regions in the SAR images caused by cyanobacterial blooms damped the microwave backscatter of the lake surface and were consistent with the regions of algal blooms in quasi-synchronous optical images,confirming the applicability of SAR for detection of surface blooms.Low backscatter may also be associated with other factors such as low wind speeds,resulting in interference when monitoring algal blooms using SAR data alone.After feature extraction and selection,the dark regions were classified by the support vector machine method with an overall accuracy of 67.74%.SAR can provide a reference point for monitoring cyanobacterial blooms in the lake,particularly when weather is not suitable for optical remote sensing.Multi-polarization and multi-band SAR can be considered for use in the future to obtain more accurate information regarding algal blooms from SAR data.

MULTI-REGION SEGMENTATION OF SAR IMAGE BY A MULTIPHASE LEVEL SET APPROACH

In this letter,a multiphase level set approach unifying region and boundary-based infor-mation for multi-region segmentation of Synthetic Aperture Radar(SAR)image is presented.Anenergy functional that is applicable for SAR image segmentation is defined.It consists of two termsdescribing the local statistic characteristics and the gradient characteristics of SAR image respectively.A multiphase level set model that explicitly describes the different regions in one image is proposed.The purpose of such a multiphase model is not only to simplify the way of denoting multi-region by levelset but also to guarantee the accuracy of segmentation.According to the presented multiphase model,the curve evolution equations with respect to edge curves are deduced.The multi-region segmentationis implemented by the numeric solution of the partial differential equations.The performance of theapproach is verified by both simulation and real SAR images.The experiments show that the proposedalgorithm reduces the speckle effect on segmentation and increases the boundary alignment accuracy,thus correctly divides the multi-region SAR image into different homogenous regions.

Inverse synthetic aperture radar imaging based on sparse signal processing

Based on the measurement model of inverse synthetic aperture radar (ISAR) within a small aspect sector,an imaging method was presented with the application of sparse signal processing.This method can form higher resolution inverse synthetic aperture radar images from compensating incomplete measured data,and improves the clarity of the images and makes the feature structure much more clear,which is helpful for target recognition.The simulation results indicate that this method can provide clear ISAR images with high contrast under complex motion case.

Super-resolution reconstruction of synthetic-aperture radar image using adaptive-threshold singular value decomposition technique

A super-resolution reconstruction approach of （SVD） technique was presented, and its performance was radar image using an adaptive-threshold singular value decomposition analyzed, compared and assessed detailedly. First, radar imaging model and super-resolution reconstruction mechanism were outlined. Then, the adaptive-threshold SVD super-resolution algorithm, and its two key aspects, namely the determination method of point spread function （PSF） matrix T and the selection scheme of singular value threshold, were presented. Finally, the super-resolution algorithm was demonstrated successfully using the measured synthetic-aperture radar （SAR） images, and a Monte Carlo assessment was carried out to evaluate the performance of the algorithm by using the input/output signal-to-noise ratio （SNR）. Five versions of SVD algorithms, namely 1 ） using all singular values, 2） using the top 80% singular values, 3） using the top 50% singular values, 4） using the top 20% singular values and 5） using singular values s such that S2≥/max（s2）/rinsNR were tested. The experimental results indicate that when the singular value threshold is set as Smax/（rinSNR）1/2, the super-resolution algorithm provides a good compromise between too much noise and too much bias and has good reconstruction results.

A method of automatic shape depiction and information extraction for oceanic eddies in SAR images

Synthetic aperture radar （SAR） provides a large amount of image data for the observation and research of oceanic eddies. The use of SAR images to automatically depict the shape of eddies and extract the eddy information is of great significance to the study of the oceanic eddies and the application of SAR eddy images. In this paper, a method of automatic shape depiction and information extraction for oceanic eddies in SAR images is proposed, which is for the research of spiral eddies. Firstly, the skeleton image is got by the skeletonization of SAR image. Secondly, the logarithmic spirals detected in the skeleton image are drawn on the SAR image to depict the shape of oceanic eddies. Finally, the eddy information is extracted based on the results of shape depiction. The sentinel 1 SAR eddy images in the Black Sea area were used for the experiment in this paper. The experimental results show that the proposed method can automatically depict the shape of eddies and extract the eddy information. The shape depiction results are consistent with the actual shape of the eddies, and the extracted eddy information is consistent with the reference information extracted by manual operation. As a result, the validity of the method is verified.

A NEW UNSUPERVISED CLASSIFICATION ALGORITHM FOR POLARIMETRIC SAR IMAGES BASED ON FUZZY SET THEORY

In this letter, a new method is proposed for unsupervised classification of terrain types and man-made objects using POLarimetric Synthetic Aperture Radar (POLSAR) data. This technique is a combi-nation of the usage of polarimetric information of SAR images and the unsupervised classification method based on fuzzy set theory. Image quantization and image enhancement are used to preprocess the POLSAR data. Then the polarimetric information and Fuzzy C-Means (FCM) clustering algorithm are used to classify the preprocessed images. The advantages of this algorithm are the automated classification, its high classifica-tion accuracy, fast convergence and high stability. The effectiveness of this algorithm is demonstrated by ex-periments using SIR-C/X-SAR (Spaceborne Imaging Radar-C/X-band Synthetic Aperture Radar) data.

STUDY ON THE TECHNIQUE TO DETECT TEXTURE FEATURES IN SAR IMAGES

This letter studies on the detection of texture features in Synthetic Aperture Radar (SAR) images. Through analyzing the feature detection method proposed by Lopes, an improved texture detection method is proposed, which can not only detect the edge and lines but also avoid stretching edge and suppressing lines of the former algorithm. Experimental results with both simulated and real SAR images verify the advantage and practicability of the improved method.

SAR IMAGE ENHANCEMENT BASED ON BEAM SHARPENING TECHNIQUE

A major problem encountered in enhancing SAR image is the total loss of phase information and the unknown parameters of imaging system. The beam sharpening technique, combined with synthetic aperture radiation pattern estimation provides an approach to process this kind of data to achieve higher apparent resolution. Based on the criterion of minimizing the expected quadratic estimation error, an optimum FIR filter with a symmetrical structure is designed whose coefficients depend on the azimuth response of local isolated prominent points because this response can be approximately regarded as the synthetic aperture radiation pattern of the imaging system. The point target simulation shows that the angular resolution is improved by a ratio of almost two to one. The processing results of a live SAR image demonstrate the validity of the method.

Road network extraction in classified SAR images using genetic algorithm

Due to the complicated background of objectives and speckle noise, it is almost impossible to extract roads directly from original synthetic aperture radar(SAR) images. A method is proposed for extraction of road network from high-resolution SAR image. Firstly, fuzzy C means is used to classify the filtered SAR image unsupervisedly, and the road pixels are isolated from the image to simplify the extraction of road network. Secondly, according to the features of roads and the membership of pixels to roads, a road model is constructed, which can reduce the extraction of road network to searching globally optimization continuous curves which pass some seed points. Finally, regarding the curves as individuals and coding a chromosome using integer code of variance relative to coordinates, the genetic operations are used to search global optimization roads. The experimental results show that the algorithm can effectively extract road network from high-resolution SAR images.

Repair wind field in oil contaminated areas with SAR images

In this paper,we compared the normalized radar cross section in the cases of oil spill,biogenic slicks,and clean sea areas with image samples made from 11-pixel NRCS average,and determined their thresholds of the NRCS of the synthetic aperture radar. The results show that the thresholds of oil and biogenic slicks exhibit good consistency with the corresponding synthetic aperture radar images. In addition,we used the normalized radar cross section of clean water from adjacent patches of oil or biogenic slicks areas to replace that of oil or biogenic slicks areas,and retrieve wind field by CMOD5.n and compare wind velocity mending of oil and biogenic slicks areas with Weather Research and Forecasting modeled data,from which the root mean squares of wind speed(wind direction) inversion are 0.89 m/s(20.26°) and 0.88 m/s(7.07°),respectively. Therefore,after the occurrence of oil spill or biogenic slicks,the real wind field could be repaired using the method we introduced in this paper. We believe that this method could improve the accuracy in assessment of a real wind field on medium and small scales at sea,and enhance effectively the monitoring works on similar oil or biogenic slicks incidents at sea surface.

IMPROVED SYNTHETIC APERTURE SONAR MOTION COMPENSATION COMBINED DPCA WITH SUB-APERTURE IMAGE CORRELATION

Estimation precision of Displaced Phase Center Algorithm(DPCA) is affected by the number of displaced phase center pairs,the bandwidth of transmitting signal and many other factors.Detailed analysis is made on DPCA's estimation precision.Analysis results show that the directional vector estimation precision of DPCA is low,which will produce accumulating errors when phase cen-ters' track is estimated.Because of this reason,DPCA suffers from accumulating errors seriously.To overcome this problem,a method combining DPCA with Sub Aperture Image Correlation(SAIC) is presented.Large synthetic aperture is divided into sub-apertures.Micro errors in sub-aperture are estimated by DPCA and compensated to raw echo data.Bulk errors between sub-apertures are esti-mated by SAIC and compensated directly to sub-aperture images.After that,sub-aperture images are directly used to generate ultimate SAS image.The method is applied to the lake-trial dataset of a 20 kHz SAS prototype system.Results show the method can successfully remove the accumulating error and produce a better SAS image.

Extracting Eco-hydrological Information of Inland Wetland from L-band Synthetic Aperture Radar Image in Honghe National Nature Reserve, Northeast China

Taking a typical inland wetland of Honghe National Nature Reserve (HNNR), Northeast China, as the study area, this paper studied the application of L-band Synthetic Aperture Radar (SAR) image in extracting eco-hydrological information of inland wetland. Landsat-5 TM and ALOS PALSAR HH backscatter images were first fused by using the wavelet-IHS method. Based on the fused image data, the classification method of support vector machines was used to map the wetland in the study area. The overall mapping accuracy is 77.5%. Then, the wet and dry aboveground biomass estimation models, including statistical models and a Rice Cloudy model, were established. Optimal parameters for the Rice Cloudy model were calculated in MATLAB by using the least squares method. Based on the validation results, it was found that the Rice Cloudy model produced higher accuracy for both wet and dry aboveground biomass estimation compared to the statistical models. Finally, subcanopy water boundary information was extracted from the HH backscatter image by threshold method. Compared to the actual water borderline result, the extracted result from L-band SAR image is reliable. In this paper, the HH-HV phase difference was proved to be valueless for extracting subcanopy water boundary information.

MONO-PULSE RADAR 3-D IMAGING TECHNIQUES FOR TARGET IN STEPPED TRACKING MODE

A method for mono-pulse radar 3-D imaging in stepped tracking mode is presented and the amplitude linear modulation of error signals in stepped tracking mode is analyzed with its compensation method followed, so the problem of precisely tracking of target is solved. Finally the validity of these methods is proven by the simulation results.

InSAR Kalman Filter Phase Unwrapping Algorithm Based on SRTM DEM

PU （phase unwrapping） is the key step and important problem in DEM （digital elevation model） extraction and the measurement of surface deformation of InSAR （Interferometric synthetic aperture radar）. The CKFPUA （conventional Kalman filter phase unwrapping algorithm） can obtain reliable results in the flat terrain areas, but it caused error transmission not making the accurate inversion of surface deformation information in the steep terrain. Considering this situation, so it needs to introduce topographic information for guiding phase unwrapping. Here the 90 m resolution DEM data have been used and it is obtained by SRTM （shuttle radar topography mission） measured jointly by NASA （National Aeronautics and Space Administration） and NIMA （National Imaging Mapping Agency） of U.S. Department of Defense. This paper presents a SD-KFPUA （Kalman filter phase unwrapping algorithm） based on SRTM DEM. With SRTM DEM directing InSAR image to implement phase unwrapping, the speed and accuracy are improved. By analyzing with the conventional Kalman filter phase unwrapping algorithms, it is shown that the proposed method can achieve good results in particular to improve unwrapping accuracy in the low coherence region.