Multiregion level-set partitioning of synthetic aperture radar images

The purpose of this study is to investigate synthetic aperture radar (SAR) image segmentation into a given but arbitrary number of gamma homogeneous regions via active contours and level sets. The segmentation of SAR images is a difficult problem due to the presence of speckle which can be modeled as strong, multiplicative noise. The proposed algorithm consists of evolving simple closed planar curves within an explicit correspondence between the interiors of curves and regions of segmentation to minimize a criterion containing a term of conformity of data to a speckle model of noise and a term of regularization. Results are shown on both synthetic and real images.     

The effect of median filtering on synthetic aperture radar images

Synthetic aperture radar (SAR) images are subject to intrinsic 'noise', called speckle, over and above any spatial variability due to variations in the properties of the scene. Many noise-reduction techniques have been employed to reduce the effects of this phenomenon. In this note we review the statistical effects of one of the simplest such techniques, the median filter. This filter can be performed almost as rapidly as the mean (box average) filter but has significantly better edge-preserving properties. It is, however, unsuited to images containing significant point- or small-target features. Use of the median filter can introduce significant biases into the data, for example a 25 per cent reduction in an intensity image after 3 by 3 median filtering. This note presents calculations of the size of these biases for the case of homogeneous target areas, fully-developed speckle, and statistically independent looks in multi-look images.     

Surface expressions of bathymetry on SEASAT synthetic aperture radar images

Abstract

This paper identifies three classes of feature observed on SEASAT synthetic aperture radar images that may be related to changes in water depth. The first is refraction of gravity waves. The second is the modulation of the surface roughness as tidal currents flow over changes in the depth of shallow water. The third is the detection of internal waves propagating from steep gradients in deep water.     

Suppression of speckle in synthetic aperture radar images using wavelet

A filter for suppressing speckle in synthetic aperture radar (SAR) images utilizing wavelet is proposed. The filter suppresses speckle by reducing the amplitude of the detail images in wavelet subspaces, while preserving edges by releasing the amplitude reduction around edges; information on edges, contained in the detail images, is utilized for edge detection. Simulations and application to SAR images have shown that the performance of the filter is satisfactory in both smoothing and edge preservation, and in generating visually-natural images as well.     

基于对象的合成孔径雷达影像极化分解方法

面向对象方法已成为全极化合成孔径雷达（SAR）影像处理的常用方法,但是极化分解仍以组成对象的像素为计算单元,针对以像素为单位的极化分解效率低的问题,提出一种面向对象的极化分解方法。通过散射相似性系数加权迭代,获得对象的极化表征矩阵并对其收敛性进行了分析,以对象极化表征矩阵的极化分解代替对象区域内所有像素的分解,提高极化特征获取效率。在此基础上,综合影像对象空间特征,并通过特征选择与支持向量机(SVM)分类进行分析和评价。通过AIRSAR Flevoland影像数据实验表明,面向对象的分解方法能够减少对象极化特征提取的时间,同时提高地物目标的分类精度。相对于监督Wishart方法,提出方法的总体精度和Kappa值分别提高了17%和20%。     

An adaptive filter for speckle suppression in synthetic aperture radar images

Synthetic aperture radar images are generally corrupted by speckle noise. This arises due to the coherent nature of radar echoes used in the image formation and it is often necessary to enhance the image by speckle suppression before data can be used in various applications. To suppress speckle and improve the radar image interpretability a simple filtering technique has been proposed. The filter is adaptive to the variance of pixel intensity in a sliding window and accordingly decides the number of nearest neighbours to the central pixel to replace its intensity with the average intensity of those nearest neighbours. The performance of the filter has been studied for speckle removal in the homogeneous areas and its edge retention capability and compared with some of the widely known speckle filters. The results show that the proposed filter retains edges, removes speckle noise and compares well with other known filters in the literature.     

The use of segmentation and shape recognition techniques in synthetic aperture radar images

This paper discusses the use of shape recognition techniques in the context of Synthetic Aperture Radar (SAR) images. Results of a study using small bright targets in simulated imagery are presented.     

Noise reduction in synthetic aperture radar images using fuzzy logic and genetic algorithm

Synthetic aperture radar (SAR) is a self-illuminating imaging technique; it produces high resolution images in all weather conditions, day and night. SAR images are widely accepted and used by many application scientists. However, the SAR images are corrupted with speckle noise. Speckle noises are caused by random interference of electromagnetic signals scattered by the object surface within one resolution element. The amount of noise and distribution of noise corrupting the image is unpredictable. Conventional noise filters are quantitative in nature; they are not well suited for uncertainty problems. Fuzzy logic is capable of handling uncertainty. In this work, noisy pixels in the images are identified by using fuzzy rules and filtered using fuzzy weighted mean, keeping the healthy pixels unchanged. The optimum value of parameters used in defining fuzzy membership function is determined by using genetic algorithm (GA). Reducing noise and simultaneously preserving image details are the two most desirable characteristics of noise filters. Peak signal-to-noise ratio (PSNR) and edge preserving factor (EPF) are used to evaluate the performance of the proposed fuzzy filter. SAR images affected by varying amounts of speckle noise are used to evaluate the performance. It was observed that the proposed filter suppresses noise and preserves image edges.

     

Change detection in synthetic aperture radar images based on unsupervised artificial immune systems

In this paper, we propose a novel change detection method for synthetic aperture radar images based on unsupervised artificial immune systems. After generating the difference image from the multitemporal images, we take each pixel as an antigen and build an immune model to deal with the antigens. By continuously stimulating the immune model, the antigens are classified into two groups, changed and unchanged. Firstly, the proposed method incorporates the local information in order to restrain the impact of speckle noise. Secondly, the proposed method simulates the immune response process in a fuzzy way to get an accurate result by retaining more image details. We introduce a fuzzy membership of the antigen and then update the antibodies and memory cells according to the membership. Compared with the clustering algorithms we have proposed in our previous works, the new method inherits immunological properties from immune systems and is robust to speckle noise due to the use of local information as well as fuzzy strategy. Experiments on real synthetic aperture radar images show that the proposed method performs well on several kinds of difference images and engenders more robust result than the other compared methods.     

Processing synthetic aperture radar data†

Abstract

Basic operational principles of synthetic aperture radar systems are reviewed, with an emphasis on the data processing requirements. The techniques of image formation for these systems are covered, using either optical or digital techniques. Examples of imagery are shown to illustrate the data processing results. SAR processing capabilities as they exist worldwide are summarized.     

Sparse synthetic aperture radar imaging with optimized azimuthal aperture

To counter the problem of acquiring and processing huge amounts of data for synthetic aperture radar(SAR)using traditional sampling techniques,a method for sparse SAR imaging with an optimized azimuthal aperture is presented.The equivalence of an azimuthal match filter and synthetic array beamforming is shown so that optimization of the azimuthal sparse aperture can be converted to optimization of synthetic array beamforming.The azimuthal sparse aperture,which is composed of a middle aperture and symmetrical bilateral apertures,can be obtained by optimization algorithms(density weighting and simulated annealing algorithms,respectively).Furthermore,sparse imaging of spectrum analysis SAR based on the optimized sparse aperture is achieved by padding zeros at null samplings and using a non-uniform Taylor window.Compared with traditional sampling,this method has the advantages of reducing the amount of sampling and alleviating the computational burden with acceptable image quality.Unlike periodic sparse sampling,the proposed method exhibits no image ghosts.The results obtained from airborne measurements demonstrate the effectiveness and superiority of the proposed method.     

A technique for the identification and analysis of icebergs in synthetic aperture radar images of Antarctica

This paper describes an image analysis technique developed to identify icebergs depicted in synthetic aperture radar images of Antarctica and to determine the outlines of these icebergs. The technique uses a pixel bonding process to delineate the edges of the icebergs. It then separates them from the background water and sea ice by an edge-guided image segmentation process. Characteristics such as centroid position and iceberg area were calculated for each iceberg segment and placed in a file for input to appropriate statistical data analysis software. The technique has been tested on three ERS-1 SAR sub-images in which it succeeded in identifying virtually all segments containing icebergs of size six pixels or larger. The images were first passed through an averaging filter to reduce speckle. This process produced a pixel size of 100m x 100m. As implemented, the technique overestimates iceberg areas by about 20% on average and the detection rate falls off rapidly for icebergs less than six pixels in size. Performance in these areas is expected to improve when additional stages, based on a more detailed analysis of pixel intensity, are implemented.     

Change detection in synthetic aperture radar images based on non-local means with ratio similarity measurement

Image change detection is of widespread interest due to a large number of applications in diverse disciplines. In this study, a novel change detection approach for synthetic aperture radar (SAR) images based on a non-local means algorithm is proposed. A non-local means technique is introduced to generate a difference image by using complete information from a pair of observed images. To take the characteristics of SAR images into account, a new ratio-based relativity measurement between two speckled SAR image patches based on a ratio distance is proposed. Theoretical analysis indicates that the ratio distance is valid for SAR images. The probability density function of the ratio distance is deduced to map the distance into a relativity value. Furthermore, the ratio distance and the probability density function are both parameter-free. The new non-local means technique is successfully applied to extend the classical mean-ratio detector for SAR image detection. Experimental results on real SAR images show that the proposed approach is robust to speckle noise and effective for the detection of change information between multitemporal SAR images.     

Multi-look versus single-look processing of synthetic aperture radar images with respect to ocean wave spectra estimation

Abstract

Full-bandwidth C-band synthetic aperture radar (SAR) data are compared with 7-look and 3-look data. The peak-to-background ratio of the image intensity power spectrum describing the wave detectability is found to be on average 8-9dB higher for the 7-look data and 2-5dB higher for the 3-look data than the single-look data. This is mainly due to the decrease in the speckle noise level when going from single-look to multi-look processing. In addition, look-sum processing is evaluated against spectral-sum processing for various temporal look separations. A significant improvement in image spectral peak contrast is observed for the spectral-sum data versus the look-sum data, with increasing temporal separations between the looks. No such improvement is observed in the corresponding image spectral noise contrast parameter. These observations are in agreement with the spatial misregistration inherent in look-sum data. Finally, the acceleration contribution to the observed aximuth smearing in the spectra is found to be negligible compared with the velocity smearing contribution.     

Urban land use classification using synthetic aperture radar

Abstract

This paper presents several approaches to the use of radar imagery for land use classification of urban and near-urban areas. The use of L(HH) (L band, horizontal transmit and horizontal receive) data is emphasized because it is these types of data obtained by Seasat-A (and in November 1981 by Shuttle radar) which are most generally available. For urban area studies using imaging radar the effect of processing in an off-zero doppler (‘squint’) mode, the presence of large diffuse scatters and the possibility of height measurements are discussed. Each approach provides information and also requires supporting ground truth which are unique to radar remote sensing. For some areas the coupling of data from the microwave portion of the spectrum to the data available in the visible and near visible realms may improve the classification of urban and near-urban land use. However, the radar data are not without their own limitations which may be imposed by either the system or the nature of the imaged scene. A proper knowledge of these limitations can permit us to turn a perceived defect into a decided advantage. The metropolitan area of Los Angeles provides the geographic background for this study.     

SRAD-CNN for adaptive synthetic aperture radar image classification

The performance of synthetic aperture radar (SAR) image classification based on a conventional convolutional neural network (CNN) is limited by a trade-off between immunity to speckle noise and the ability to locate boundaries accurately. Difficulties regarding the accurate location of boundaries are a result of the smoothing effect of the pooling layer. To address this issue, we propose a novel framework called SRAD-CNN for SAR image classification. In this framework, we apply a filtering layer constructed according to prior knowledge of the speckle reducing anisotropic diffusion (SRAD) filter. The filtering layer can not only reduce speckle but also enhance the boundaries. The main parameter that controls the degree of filtering can be optimized adaptively by a backpropagation algorithm. Image patches adaptively filtered by the filtering layer are then put into the CNN layers to assign a label. Due to the effect of the filtering layer, for our proposed SRAD-CNN, both the speckle noise immunity and the sensitivity to boundaries are superior to those of conventional CNN.To confirm the performance of the proposed SRAD-CNN, we conducted experiments using both simulated and real SAR images. The experimental results demonstrated that the parameter of the filtering layer could be optimized adaptively for different scenes, different noise levels, and different image resolutions. The SRAD-CNN outperformed the conventional CNN in both overall classification accuracy and maintenance of boundary accuracy on images with different resolutions and noise levels with limited training samples.     

Building extraction from very high-resolution synthetic aperture radar images based on statistical and structural information fusion

To investigate the limits of building detection from very high-resolution (VHR) synthetic aperture radar (SAR) images, a new method, based on statistical and structural information fusion, is proposed in this paper. The proposed method contains two stages: First, using order statistics constant false alarm rate (OS-CFAR) and power ratio (PR) detectors, a set of detections are made. These detections have different statistical properties, compared to the other objects, and these properties are selected for discriminating buildings from clutters. Second, the morphological analysis is used for increasing the precision of the detection. In this stage, segments, which have the most similarities to buildings in terms of shape and size, are extracted via various structural elements (SEs). The final result is obtained by fusing the two sets of detections. The experimental results on the four real VHR SAR images show that the proposed method has a high detection rate (DR) and low false alarm rate (FAR).     

An improvement method for circular synthetic aperture radar imaging

Circular synthetic aperture radar (CSAR) is the imaging mode when the radar moves along a circular path and the observed area is always covered by the wave beam. It is different from traditional SAR modes (strip-map SAR and spotlight SAR) and has potential advantages such as 360° observation, target recognition, and three-dimensional reconstruction. According to the imaging processing of CSAR, motion error is an important issue affecting the CSAR image quality, but the motion compensatio n (MOCO) method for CSAR is underdeveloped. Accordingly, with detailed analysis the motion error model is established and a data-driven MOCO flow chart for CSAR is proposed. The real CSAR data are used to verify the proposed method.     

Gini index-based land-cover classification using polarimetric synthetic aperture radar

Classification of the Earth's surface types is one of the important remote-sensing applications of radar polarimetry. An unsupervised classification scheme based on the use of entropy and alpha angle is widely used for land-cover classification using multi-polarization radar images. The polarimetric entropy and the alpha angle are used to characterize a target's randomness and scattering mechanism, respectively. Here, we replace the entropy by the Gini index. Evaluation of the Gini index is computationally efficient. It also overcomes the drawback encountered in entropy evaluation, namely, the use of logarithmic operation. We develop and validate an unsupervised classification technique based on the use of the Gini index and alpha angle and show that it performs better than the classic entropy/alpha classification technique. We have also used the Gini/alpha method with anisotropy and complex Wishart distribution to design a complete land-cover classification scheme. The proposed classification scheme performs better than the entropy/alpha land-cover classification scheme.