Fast detecting and locating groups of targets in high-resolution SAR images

A new algorithm for detecting and locating groups of targets in high-resolution SAR images is presented. Firstly, a global constant false alarm rate (CFAR) detector is utilized to locate the potential target regions. Then, the size, shape, and contrast features of each region are computed for target discrimination based on voting decision. As targets are often deployed in well-defined groups and all targets within one group have a certain relationship in their positions, it is possible to extract the whole target group and diminish clutter false alarms. The experiment results show that the proposed algorithm significantly reduces the regions revisited by an automatic target recognition (ATR) system, and false alarms can be greatly diminished.     

基于NSCT的SAR与可见光图像融合方法

为了更好地将可见光图像与噪声干扰严重的合成孔径雷达图像融合, 提出了一种最大尺度硬阈值去噪的方法, 在此基础上设计了一种融合规则, 根据噪声和信号在NSCT(nonsubsampled Contourlet transform)域的分解系数特性, 将NSCT分解的最大尺首先进行硬阈值去噪, 其他高频尺度与最大尺度对应的像素点取值方式保持一致, 在低频系数采用“简单绝对值取大”的融合规则, 最后进行NSCT逆变换得到融合图像。实验结果表明, 该方法能有效抑制斑点噪声, 并能充分保留源图像重要特征。     

基于高分辨SAR图像区域入射角反演及高度提取方法

分析在无成像参数条件下提取单极化高分辨率合成孔径雷达(SAR)图像中的目标高度信息的方法.高分辨率SAR图像中目标信息丰富,可以解译出目标的多种信息,但是通常得到的图像经过了一定的处理,失去了成像参数.在这种情况下,首先依据目标的SAR成像原理,利用目标的阴影和叠掩推导了目标成像时的区域入射角计算公式,再利用此入射角来反演目标的高度信息.最后,利用MiniSAR图像进行了试验分析.     

A systematic review of comparative studies on ergonomic assessment techniques

This review provides a systematic overview of the comparison of ergonomic assessment techniques’ output in variety of industrial sectors.The relevant publications have been classified into broad categories such as comparison of applied industry sector, trend analysis, ergonomic assessment techniques been compared, comparative studies between two ergonomic assessment techniques, frequently compared techniques, levels of Action categories used. The summary of extracted data from included papers is provided.Authors have compared the results based on observational techniques which are easy to understand and apply. Available publications related to such comparison is limited. Authors have compared and analysed the correlation between the outputs of techniques but only few researchers pointed out the exact reason of variation in outputs applied in the same task. Hence, establishing applicability of each technique required. To identify the causes of variation, the sensitivity study of exposure factor-Task-posture assessment techniques is highly required. There are few techniques which are not yet compared for checking agreement or correlation, serving as a research gap.Authors have studied and published literature review specific to one technique of ergonomic assessment. Few authors have compared the outputs from various ergonomic assessment techniques in particular sector and investigated the correlation between them. No study has been carried out till date to review available literature on comparative study of comparisons made. In this paper, two research gaps have also been identified.     

An efficient neural classification chain of SAR and optical urban images

In this paper a suitable neural classification algorithm, based on the use of Adaptive Resonance Theory (ART) networks, is applied to the fusion and classification of optical and SAR urban images. ART networks provide a flexible tool for classification, but are ruled by a large number of parameters. Therefore, the simplified ART2-A algorithm is used in this paper, and the neural approach is integrated into a classification chain where fuzzy clustering for merging of classes is also considered. The interaction between the two methods leads to encouraging results in less CPU time than classification with fuzzy clustering alone or other classical approaches (ISODATA). Examples of classification are provided using C-band total power AIRSAR data and optical images of Santa Monica, Los Angeles.     

Building extraction based on stereo analysis of high-resolution SAR images taken from orthogonal aspect directions

In this paper high-resolution SAR images of an urban scene are analyzed in order to infer buildings and their heights from the different layover effects in perpendicular taken views. Due to the strong dependence of the appearance of objects on the lighting and viewing direction, it is unlikely that a simple image-matching method would succeed. Instead, higher level object matching is proposed. Here, a knowledge-based approach is applied, a production system. The images are analyzed separately for the presence of certain object groups and their characteristics frequently appearing on buildings, such as salient rows, rectangular structures, or symmetries. The stereo analysis is then accomplished locally by means of productions that combine and match these image objects and infer the height. The approach is tested using real SAR data of an urban scene.     

On the focusing of the ocean swell images produced by a regular and by an interferometric SAR

Focusing of a monochromatic ocean wave image by either a regular or an interferomeric SAR (INSAR) is studied. The model developed simulates the images of both SAR and INSAR for an arbitrary focus setting. The effects of the various operational parameters, i.e., integration and scene coherence times, ocean wave length, direction and amplitude, etc., on the focus sensitivity of both SAR and INSAR is examined. A dimensionless focusing parameter a which depends on the platform velocity and on the azimuthal component of. the phase velocity of the imaged ocean wave is introduced. It is shown that a is the major parameter which determines the image contrast of the regular SAR output, as well as that of the magnitude and the phase components of INSAR. The focus sensitivity, however, is also a function of the imaged ocean wave parameters. The effects of the varying SAR integration time and of the scene coherence time on the contrast of the resulting image is also examined.     

基于改进l1范数和稀疏表示的SAR与司见光图像快速融合算法

针对多尺度融合算法中合成孔径雷达(SAR)与可见光图像融合结果目标信息易缺失、对比度不高的缺点,提出了一种基于改进I1范数和稀疏表示的图像融合算法,以有效保留源图像的目标信息.首先对SAR与可见光图像经支持度变换(SVT)分解得到的低频系数进行过完备稀疏融合,采用改进l1范数的稀疏系数融合规则以有效保留源图像目标信息,并进行零均值化处理提高了算法运行效率,然后利用基于区域能量的高频融合规则,最大化保留边缘纹理等细节信息.实验结果证明了该算法的有效性.     

Extraction of bridges over water from high-resolution optical remote-sensing images based on mathematical morphology

Bridges over water are typical man-made structures on the land’s surface. An accurate extraction of such bridges from high-resolution optical remote-sensing images plays an important role in civil, commercial, and military applications. Considering the complex features of ground objects within high-resolution optical remote-sensing images and the inefficiency of previous methods of bridge extraction with random bridge orientation, direction-augmented linear structuring elements were constructed and applied in this study by using mathematical morphology to identify and extract bridges over water with different orientations. First, the image pre-processing is performed to facilitate the object extraction. Then by using the histogram-based threshold segmentation method, water bodies such as rivers are extracted and described as a binary image. Based on water bodies, the appropriate direction-augmented linear structuring element is then selected. Together with mathematical morphology operations, such as dilation and erosion, potential bridges are extracted by overlay analysis. Assisted by prior knowledge of bridges, false bridges are screened out and post-processing is finally performed to refine the extracted true bridges. This approach was validated with experiments in Shanghai and Beijing, China. The results show that the direction-augmented linear structuring elements are of high precision and have the capability of extracting bridges over water in different directions within the high-resolution optical remote-sensing image, considering both qualitative and quantitative aspects. Therefore, this approach may be useful in updating geographical databases of bridges and facilitating the assessment of bridge damage caused by natural disasters.     

Operational use of ERS-1 SAR images in the Canadian ice monitoring programme

ERS-1 SAR images have been used to monitor sea ice in the Canadian east coast and the Gulf of St. Lawrence since December 1992. Operational ice parameters were obtained, in near real-time, through visual image analysis. The information was incorporated in standard daily operational products. Further analysis was conducted later to evaluate the data utility. The images are remarkably useful in discriminating between new ice and water and in delineating ice edges. Structural information of ice surface is usually smeared. Distinction between young ice and first-year ice types, based on backscatter alone, is not reliable. Rules for image interpretation were developed. They are mainly based on structural information in images, ancillary data on weather, and recent and historical ice records. Limitations on data utilization are explained in relation to the sensor and orbit parameters. An insight into operational requirements and their relation to SAR observations is presented.     

基于级联CNN的SAR图像舰船目标检测算法

针对合成孔径雷达(SAR)图像中舰船目标稀疏的特点,提出一种基于级联卷积神经网络(CNN)的SAR图像舰船目标检测方法.将候选区域提取方法BING与目标检测方法Fast R-CNN相结合,并采用级联CNN设计,可同时兼顾舰船检测的准确率和速度.首先,针对SAR图像中相干斑噪声影响梯度检测的问题,在原有梯度算子的基础上增加平滑算子,并对图像尺寸个数和候选框个数进行适应性改进,使其提取到的候选窗口更快更准;然后,设计级联结构的Fast R-CNN检测框架,前端简单的CNN负责排除明显的非目标区域,后端复杂的CNN对高概率候选区域进行分类和位置回归,整个结构可以保证快速准确地对舰船这种稀疏目标进行检测;最后,设计一种联合优化方法对多任务的目标函数进行优化,使其更快更好地收敛.在SAR图像舰船检测数据集SSDD上的实验结果显示,所提出的方法相比于原始Fast R-CNN和Faster R-CNN检测方法,检测精度从65.2%和70.1%提高到73.5%,每张图像的处理时间从2235ms和198ms下降到113ms.     

Automatic ice motion retrieval from ERS-1 SAR images using the optical flow method

The optical flow method is an approach to motion analysis in the field of computer vision. This method is implemented here to deal with rotational and deformational ice motion, for which the area correlation method shows deficiencies. The results show that the optical flow method has the capacity to cope with the rotation and deformation of an ice cover, while requiring less computing time than the area correlation method. For better representation of the discontinuities of a motion field, a modified version of the optical flow method is presented with the aid of image segmentation. The paper also includes a technique for detecting mean rotation and translation in terms of FFT transformation. In most cases, this technique can simplify and speed up the process of motion retrieval in the Arctic central pack ice area. The algorithm has been applied to fourteen pairs of images acquired in the Arctic Ocean and the Baltic Sea. Three of them are illustrated here to demonstrate the accuracy and other capacities of the algorithm.     

A novel method to reduce speckle in SAR images

A novel three-step method to reduce speckle in Synthetic Aperture Radar (SAR) images is presented. First, an edge detector is used to detect the edge orientations in SAR images. Second, using a data processing method named empirical mode decomposition, the SAR image is smoothed along four directions: horizontal, vertical, left diagonal and right diagonal, respectively. Third, the SAR image is reconstructed such that if the edge direction is horizontal, the pixel smoothed along the horizontal direction is used to reconstruct the image, and so on; if an edge is not detected, the mean of the four directionally smoothed images is used to reconstruct the image. Application of this method to SAR images has shown that it is well balanced in the quality of visual appearance, mean preservation, edge preservation, and reduction of the standard deviation.     

Integration of varying spatial,spectral and temporal high-resolution optical images for individual tree crown isolation

Automated methods for capturing geometric and spectral properties of individual tree crowns are becoming increasingly viable options for use in natural resource planning. Crown isolation techniques are needed that are capable of adapting to the changing availability and resolutions of remotely sensed data. Data integration, or the fusion of two distinct data entities, offers a methodological framework that can compensate for the shortcomings of individual datasets while enhancing their desirable features. This study sought to develop a method of data integration for high-resolution optical images of varying spatial and temporal resolutions to improve the automatic detection and delineation of individual tree crowns. A marker-controlled watershed segmentation (MCWS) algorithm was developed for a 30-cm-per-pixel-side airborne colour infrared (CIR) digital image of a leaf-on apple (Malus spp.) orchard. Three methods of obtaining the markers needed for the MCWS algorithm were tested: (1) manual marker selection, (2) template/correlation selection using the 30-cm CIR image, and (3) template/correlation selection using a 15-cm-per-pixel-side true (TRU) colour leaf-off aerial image. The effectiveness of integrating marker data of different temporal and spatial resolutions with the segmentation process of the CIR image scene was tested. A comparison of crown isolation results using markers derived within the segmented 30-cm CIR digital imagery with results from markers derived from the 15-cm TRU image scene indicated greater accuracies to detect and isolate tree crowns with data integration.     

Estimating parameters of a two-layer stratified ocean from polarity conversion of internal solitary waves observed in satellite SAR images

This paper presents a method for estimating parameters of a two-layer stratified ocean using satellite SAR images. According to weak nonlinearity shallow water theory, internal solitary waves (ISWs) in stratified oceans may be either depression or elevation waves, depending on the sign of the quadratic nonlinearity coefficient in the KdV equation. It has been confirmed that ISWs can convert their polarity when passing through a turning point, where the quadratic nonlinearity coefficient changes sign. For a two-layer stratified ocean, the turning point is located where the upper and lower layer depths are equal. The authors suggest that depression, elevation and broadening ISWs can be discerned according to their different signatures in SAR images. It is also found that a SAR image can record a continuous evolution process from depression to elevation ISWs in its spatial domain, under conditions of a spatially inhomogeneous ocean environment. Therefore, the upper and lower layer depths can be calculated by determining the polarity conversion of ISWs observed in satellite SAR images. Furthermore, the density difference between the upper and lower layers can also be estimated, when the wave speed is known. We extract ocean stratification parameters, including upper layer depth and density difference, from polarity conversion of ISWs observed in a RADARSAT-1 SAR image taken over the northeastern South China Sea. Comparing the estimated results with field measurements, we find that this method can estimate the upper layer depth with considerable success. In estimating the density difference between the upper and lower layers, it also gives a quite reasonable result.     

基于模糊理论的SAR图像海上舰船检测方法研究

舰船检测是合成孔径雷达图像海洋应用的一个重要部分,针对中分辨率近岸海域SAR图像,提出了一种基于模糊理论的海上舰船检测方法。该方法先利用改进的模糊增强算法对图像进行增强处理,以改变图像灰度的分布特性,从而分离图像中海洋区域和陆地区域,并结合最大熵分割法提取海洋背景中包含候选舰船的感兴趣区域,最后,对ROI区域进行分割,提取舰船的特征,并基于模糊推理技术实现对海上舰船目标的检测。     

A statistical and Wiener filtering algorithm based on the curvelet transform for SAR images

Synthetic aperture radar (SAR) images contain many kinds of noise. Speckle noise is multiplicative noise generated by the coherent imaging processes involved in SAR images and brings a great hindrance to the interpretation and application of SAR images, so it is considered the first major kind of noise in SAR images. SAR images also contain other incoherent additive noises generated by other factors, such as Gaussian noise, which are all considered the second major kind of noise. In order to reduce the impact of noise as much as possible, after an in-depth study of SAR imaging and noise-generating mechanism, curvelet transform principle, and Wiener filtering characteristic, a novel filtering method, here called the statistical and Wiener based on curvelet transform (SWCT) method is proposed. The SWCT algorithm processes two different kinds noise based on their properties. Specifically, it establishes a two-tiered filtering framework. For the first kind of noise, the algorithm uses the curvelet transform to decompose the SAR image and uses the statistical characteristics of the SAR image to generate an adaptive filtering threshold of the coefficients of decomposition to recover the original image. Then it filters every sub-band image at each decomposed scale and performs the inverse curvelet transform. The second kind of noise is directly filtered using the Wiener filter in the SWCT algorithm. Using the two-tiered filtering model and fully exploiting statistical characteristics, the SWCT algorithm not only reduces the amount of coherent speckle noise and incoherent noise effectively but also retains the edges and geometric details of the original SAR image. This is very good for target detection, classification, and recognition. Qualitative and quantitative tests were performed using simulated speckle noise, Gaussian noise, and real SAR images. The proposed SWCT algorithm was found to remove noise effectively and the performance of the algorithm was tested and compared to the mean filter, enhanced gamma-MAP (maximum a posterior probability) filter, wavelet transform filter, Wiener filter, and curvelet transform filter. Experiments carried out on real SAR images confirmed that the new method has a good filtering effect and can be used on different SAR images.     

Radiometric correction of SAR images A new correction algorithm

Abstract

Spatial variations of the backscatter coefficient result from synthetic aperture radar imaging systems and their platforms. An adaptive algorithm has been developed to correct the multiplicative variation of the backscatter in the longitudinal (parallel to flight line) and lateral (perpendicular to flight line) directions. The coefficient of variation along the parallel and lateral profiles, which consist of the means of the pixels along these lines perpendicular to the respective profiles, is constant. This implies that the radiometric variations are multiplicative. Because standard correction methods such as polynomial transfer functions give unsatisfactory results, an adaptive correction algorithm was developed to correct these images. The algorithm produces a transfer function by means of a filtering window which moves along the profile in what is essentially a moving mean procedure. However, the length of the window is automatically adjusted as a function of the variation of the profile. The adjustment is based on a calculation of the probability that values to be included or excluded from the window belong to the included population. The effects of the successive correction steps were monitored using several thematic test sites. The resultant images provide both increased quality and quantity of data without any degradation of the statistical properties of the data.     

MERIS potential for ocean colour studies in the open ocean

The interest of space observations of ocean colour for determining variations in phytoplankton distribution and for deriving primary production (via models) has been largely demonstrated by the Coastal Zone Color Scanner (CZCS) which operated from 1978 to 1986. The capabilities of this pioneer sensor, however, were limited both in spectral resolution and radiometric accuracy. The next generation of ocean colour sensors will benefit from major improvements. The Medium Resolution Imaging Spectrometer (MERIS), planned by the European Space Agency (ESA) for the Envisat platform, has been designed to measure radiances in 15 visible and infrared channels. Three infrared channels will allow aerosol characterization, and therefore accurate atmospheric corrections, to be performed for each pixel. For the retrieval of marine parameters, nine channels between 410 and 705nm will be available (as opposed to only four with the CZCS). In coastal waters this should, in principle, allow a separate quantification of different substances (phytoplankton, mineral particles, yellow substance) to be performed. In open ocean waters optically dominated by phytoplankton and their associate detrital matter, the basic information (i.e. the concentration of phytoplanktonic pigments) will be retrieved with improved accuracy due to the increased radiometric performances of MERIS. The adoption of multi-wavelength algorithms could also lead to additional information concerning auxiliary pigments and taxonomic groups. Finally, MERIS will be one of the first sensors to allow measurements of Sun-induced chlorophyll a in vivo fluorescence, which could provide a complementary approach for the assessment of phytoplankton abundance. The development of these next-generation algorithms, however, requires a number of fundamental studies.     

New spectral estimate for SAR imaging of the ocean

On the basis of the theory of microwave scattering from the ocean surface the correlation function of the synthetic aperture radar (SAR) signal intensity was obtained as a sum of two items. The first item is the proper image correlation function, i.e. the correlation function free of speckle noise SAR signal intensity, which has been investigated in many works (mostly by Alpers et al.). The second item describes the speckle structure in the image. It has been shown that at sufficiently large values of the well‐known velocity bunching parameter the speckle energy is significant within the spectral interval, where the spectrum of large ocean waves is concentrated. In this case the speckle noise can not be suppressed efficiently by means of image filtering. Meanwhile, the mentioned second item is nothing other than half of the correlation function of the complex intensity, which is the square of the SAR signal complex amplitude (unlike the usual real intensity, i.e. the square of the modulus). Therefore, the unspeckled image correlation function can be presented as the difference between the real intensity correlation function and half of the complex intensity function. This leads to a new spectral estimate, free of speckle noise, for the SAR image. The corresponding expression for the new estimate is presented.