基于预测矢量量化的图像压缩研究

针对预测器与矢量量化器的联合设计问题,通过分析常用的开环法与闭环法的设计特点,给出了结合二者优点的半闭环设计法并通过仿真验证了其有效性。实验结果显示,该方法设计的预测矢量量化器具有更灵活的设计方式和较好图像重建质量。     

VLSI architecture for SAR data compression

As a step towards a real-time signal aperture radar (SAR) correlator, custom very large scale integration (VLSI) architectures are developed. Considering the extremely short word length of the data, we derive three architectures with massive parallelism in bit space. Unlike frequency methods, no. degradation is introduced during convolution. Optimized for time and space, they are highly suited to VLSI implementation, and a small architecture with 80 taps operating at 10 MHz has been built using an FPGA     

Coarse quantization for data compression in coherent location systems

When emitter location systems measure time-difference-of-arrival (TDOA) and differential Doppler (DD) by coherently cross-correlating the signal pairs, data compression techniques are needed to facilitate data transfer of one of the signals to the receiving site of the other signal. Two block-adaptive quantization schemes are analyzed here to determine their impact on the signal-to-noise ratio (SNR) of the quantized signal as well as on the post-correlation SNR. Comparisons are made between two approaches: quantization of the real/imaginary (R/I) components or the magnitude/phase (M/P) components. For the M/P approach, a rule is derived for optimally allocating the bits between the magnitude and phase. The M/P approach provides better post-quantization/precorrelation SNR for most signals; however, when the SNR of the signal not being quantized is small, the post-correlation SNR can be largely unaffected by the quantization. In that case, there is little difference between R/I and M/P, even under the most favorable scenario for M/P.     

Support vector machines for SAR automatic target recognition

Algorithms that produce classifiers with large margins, such as support vector machines (SVMs), AdaBoost, etc, are receiving more and more attention in the literature. A real application of SVMs for synthetic aperture radar automatic target recognition (SAR/ATR) is presented and the result is compared with conventional classifiers. The SVMs are tested for classification both in closed and open sets (recognition). Experimental results showed that SVMs outperform conventional classifiers in target classification. Moreover, SVMs with the Gaussian kernels are able to form a local “bounded” decision region around each class that presents better rejection to confusers     

SAR image formation via semiparametric spectral estimation

A new algorithm, referred to as the SPAR (Semiparametric) algorithm, is presented herein for target feature extraction and complex image formation via synthetic aperture radar (SAR). The algorithm is based on a flexible data model that models each target scatterer as a two-dimensional (2-D) complex sinusoid with arbitrary unknown amplitude and constant phase in cross-range and with constant amplitude and phase in range. By attempting to deal with one corner reflector, such as one dihedral or trihedral, at a time, the algorithm can be used to effectively mitigate the artifacts in the SAR images due to the flexible data model. Another advantage of SPAR is that it can be used to obtain initial conditions needed by other parametric target feature extraction methods to reduce the total amount of computations needed. Both numerical and experimental examples are provided to demonstrate the performance of the proposed algorithm     

OGSRN: Optical-guided super-resolution network for SAR image

Although Convolutional Neural Networks(CNNs) have significantly improved the development of image Super-Resolution(SR) technology in recent years, the existing SR methods for SAR image with large scale factors have rarely been studied due to technical difficulty. A more efficient method is to obtain comprehensive information to guide the SAR image reconstruction.Indeed, the co-registered High-Resolution(HR) optical image has been successfully applied to enhance the quality of SAR image due to it...     

A novel method for multi-angle SAR image matching

Multi-angle synthetic aperture radar(SAR) image matching is very challenging, because the same object may cause different backscattering patterns, heavily depending on the radar incident angle. A technique based on the relations between the invariant positions of ground targets among the reference and sensed images is proposed to accommodate the nonmatching patterns. It involves a target extraction using wavelet coefficient fusion, as well as a geometric voting matching routine for searching the matched control points(CPs) in the reference and sensed images, respectively. To accelerate the speed of the search, a robust, rapidly corresponding CPs determination strategy is exploited by utilizing the global spatial transformation model, as well as a procedure of outlier removal to ensure the desired accuracy. Meanwhile, the positions of the matched point pairs are relocated using mutual information. The final warping of the images according to the CPs is performed by using a polynomial function. The results show the possibility of matching multi-angle SAR images in general cases.     

Efficient ATR using compression

We examine various model-based automatic target recognition (MBATR) classifiers to investigate the utility of model-catalog compression realized via signal-vector quantization (VQ) and feature extraction. We specifically investigate the impact of various compression rates and common automatic target recognition (ATR) scenario variations such as noise and occlusion through simulations on high-range resolution (HRR) radar and synthetic aperture radar (SAR) data. For this data, we show that significant computational savings are possible for modest decreases in classification performance.     

基于SAR图像匹配结果可信度评价的INS/SAR自适应Kalman滤波算法

在惯性导航系统（INS）/合成孔径雷达（SAR）组合导航系统中,SAR图像易受斑点噪声的影响,图像匹配的精度对整个导航系统精度的影响十分明显,能够准确地分析SAR图像匹配过程中的误差特性,利用有效的图像匹配信息辅助INS进行组合定位尤为重要。针对上述问题,在加权Hausdorff距离匹配算法的基础上,对影响SAR图像匹配精度的因素进行了分析,提出了一种基于模糊推理的匹配结果可信度评价准则,经过可信度筛选,将有效的匹配信息与INS进行组合;对合理范围内的匹配误差变化引起量测噪声统计特性发生变化,进而导致Kalman滤波精度下降的问题,研究采用改进的Sage-Husa自适应滤波算法对量测噪声方差阵进行动态调整,使其更加接近系统的当前状态。搭建仿真验证平台对所提算法进行了验证,结果表明,该算法能够在合理的匹配误差范围内,有效地筛选出可信的图像匹配结果,相比常规Kalman滤波算法,显著地提升了INS/SAR组合导航系统水平方向的定位精度。     

Nonlinear amplitude compression in magnetic resonance imaging:quantization noise reduction and data memory saving

Dynamic range improvement in magnetic resonance imaging (MRI) by nonlinear amplitude compression is proposed. Quantization noise reduction by a factor of 10 to 20 is confirmed by a numerical simulation, provided an appropriate nonlinear transform is chosen. The piecewise-linear transform is the best nonlinear transform studied so far. Implementing amplitude compression requires no additional hardware in a usual MRI system if the low spatial frequency lines of the magnetic resonance (MR) signal are selectively prescanned through an attenuator. The method will be particularly effective in 3-D imaging of large objects. MR angiography, and imaging of solids     

一种基于MLS的SAR图像点特征提取方法

针对合成孔径雷达图像点特征提取问题,提出了一种基于MLS的SAR图像点特征提取方法.首先利用提出的基于超像素关联分析的SAR图像分割算法得到二值分割图像,再把该二值图像和SAR图像进行点乘运算,获取到含有强度信息的目标区域,然后采用移动最小二乘法(MLS)对目标区域进行曲面拟合,根据设定的点特征判决规则,最后提取出SAR图像的峰值、脊和谷等多种点特征.基于MSTAR数据的实验结果表明了该方法的有效性和准确性.     

SAR data focusing using seismic migration techniques

The focusing of synthetic-aperture-radar (SAR) data using migration techniques quite similar to those used in geophysics is treated. The algorithm presented works in the ω-k_x domain. Because time delays can be easily accommodated with phase shifts that increase linearly with ω, range migration poses no problem. The algorithm is described in plane geometry first, where range migration and phase history can be exactly matched. The effects of the sphericity of the Earth, of the Earth's rotation, and of the satellite trajectory curvature are taken into account, showing that the theoretically achievable spatial resolution is well within the requirements of present day and near future SAR missions. Terrestrial swaths as wide as 100 km can be focused simultaneously with no serious degradation. The algorithm has been tested with synthetic data, with Seasat-A data, and with airplane data (NASA-AIR). The experimental results fully support the theoretical analysis     

A non-local vectorial total variational model for multichannel SAR image speckle suppression

This paper aims at the multichannel synthetic aperture radar(SAR)image speckle reduction.This paper proposes a novel energy minimized regularization model for multichannel image denoising,which is an extension of the non-local total variational model for gray-scale image.It contains two terms,namely the vectorial data fidelity term and the non-local vectorial total variation term.The latter is constructed by high-dimensional non-local gradient that contains the structure information of the multichannel image.The existence and the uniqueness of the solution of the model are proved.A fixed point iterative algorithm is designed to acquire the solution of this model.The convergence property of this algorithm is proved as well.This model is applied to the multipolarimetric and multi-temporal RADARSAT-2 images despeckling.The result shows that this model performs better than the original vectorial total variational model on texture preserving.     

Remote data sensing using SAR and harmonic reradiators

In sensor networks distributed over large areas, communication by means of active transmitters on sensor nodes is inherently energy expensive and poses a significant bottleneck to achieve a long battery life. We propose modulated reradiation of radar illumination as a means to transmit information from a group of sensors to an airborne radar. This puts the communications energy burden on the radar transmitter rather than on the sensor nodes, thus increasing their battery lifetimes. To distinguish the sensor return from the clutter return, the modulation on the sensors is done by switching a nonlinear load on the sensor antenna and processing the harmonic reradiation. We present techniques to transmit information from the sensors, which use stripmap mode synthetic aperture radar (SAR) ideas to decode the information and to simultaneously obtain a geographic map of the sensor locations.     

SAR ATR performance using a conditionally Gaussian model

A family of conditionally Gaussian signal models for synthetic aperture radar (SAR) imagery is presented, extending a related class of models developed for high resolution radar range profiles. This signal model is robust with respect to the variations of the complex-valued radar signals due to the coherent combination of returns from scatterers as those scatterers move through relative distances on the order of a wavelength of the transmitted signal (target speckle). The target type and the relative orientations of the sensor, target, and ground plane parameterize the conditionally Gaussian model. Based upon this model, algorithms to jointly estimate both the target type and pose are developed. Performance results for both target pose estimation and target recognition are presented for publicly released data from the MSTAR program     

Calibration of complex polarimetric SAR imagery using backscattercorrelations

A technique for calibration of multipolarization synthetic-aperture radar (SAR) imagery is described. If scatterer reciprocity and lack of correlation between co- and cross-polarized radar echoes (for azimuthally symmetric distributed targets) are assumed, the effects of signal leakage between the radar data channels can be removed without the use of known ground targets. If known targets are available, all data channels can be calibrated relative to one another and radiometrically as well. The method is verified with simulation and application to airborne SAR data     

Accuracy of using point targets for SAR calibration

The peak and integral methods for radiometric calibration of a synthetic aperture radar (SAR) using reference point targets are analyzed. Both calibration methods are shown to be unbiased, but the peak method requires knowledge of the equivalent rectangle system resolution which is sensitive to system focus. Exact expressions for the RMS errors of both methods are derived. It is shown that the RMS error resulting from the peak method is always smaller than or equal to that from the integral method for a well-focused system. However, for robust radiometric calibration of SAR, or when nonlinear phase errors are present, the integral method is recommended, because it does not require detailed knowledge of the impulse response and the resulting RMS error is not dependent on system focus     

Automatic target recognition using enhanced resolution SAR data

Using advanced technology, a new automatic target recognition (ATR) system has been developed that provides significantly improved target recognition performance compared with ATR systems that use conventional synthetic aperture radar (SAR) image-processing techniques. This significant improvement in target recognition performance is achieved by using a new superresolution image-processing technique that enhances SAR image resolution (and image quality) prior to performing target recognition. A computationally efficient two-level implementation of a template-based classifier is used to perform target recognition. The improvement in target recognition performance achieved using superresolution image processing in this new ATR system is quantified     

Doppler aliasing reduction in SAR imagery using stepped-frequency waveforms

A new technique for eliminating Doppler aliasing artifacts in synthetic aperture radar (SAR) imagery is presented. The technique uses continuous transmission of stepped-frequency (SF) waveforms to generate a simple, but effective, approximation of a two-dimensional |sinc|² ambiguity function. This approach ensures aliasing artifacts are placed at nulls in a Doppler filter, effectively removing them from the imagery. Various methods of generating SF waveforms are introduced and their performance is quantified. Finally, simulated SAR imagery shows elimination of more than 99% of the aliased energy