Single-channel SAR Research Articles

Improved Hybrid Integration Detection Algorithm of Moving Targets with Fast Radial Velocity Based on Single-channel SAR System

高径向速度目标会产生严重的距离走动并伴随方位失配，方位压缩会使其散焦并弥散在SAR图像中，不易被检测。该文针对高径向速度目标的检测问题，提出一种基于单通道机载SAR的检测方法。该方法通过抽取等效双通道，利用相干对消抑制杂波，并去除动目标的频谱分裂，再运用Dechirp处理和Hough变换积累目标在距离单元内和距离单元间的能量，以获得更大的积累增益。与传统的混合积累方法相比，该方法在抑制杂波的基础上，更好地积累高径向速度目标的能量，从而有效提高该类目标的检测性能。仿真数据和实测数据均验证了该方法的有效性和优越性。

Glacier Velocity Estimation by Means of a Polarimetric Similarity Measure

The contribution of polarimetric synthetic aperture radar (PolSAR) images compared with that of single-channel SAR images in terms of temporal scene characterization has been found and described to add valuable information in the literature. However, despite a number of recent studies focusing on single-polarized glacier monitoring, the potential of polarimetry to estimate the surface velocity of glaciers has not been explored due to the complex mechanism of polarization through glacier/snow. In this paper, a new approach to the problem of monitoring glacier surface velocity is proposed by means of temporal PolSAR images, using a basic concept from information theory, i.e., mutual information (MI). The proposed polarimetric tracking method applies the MI to measure the statistical dependence between temporal polarimetric images, which is assumed to be maximum if the images are geometrically aligned. Since the proposed polarimetric tracking method is very powerful and general, it can be implemented into any kind of multivariate remote sensing data such as multichannel optical and single-channel SAR images. The proposed polarimetric tracking is then used to retrieve the surface velocity of the Aletsch Glacier in Switzerland and the Inylchek Glacier in Kyrgyzstan with two different SAR sensors: the Experimental SAR airborne L-band (fully polarimetric) and Envisat C-band (single-polarized) systems, respectively. The effect of the number of channels (polarimetry) into tracking investigations demonstrated that the presence of snow, as expected, affects the location of the phase center in different polarization and frequency channels, as for the glacier tracking with temporal HH compared to temporal VV channels. In this paper, it is shown how it is possible to optimize these two different contributions, considering the multichannel SAR statistics.

Demonstration of SAR Distortion Correction Using a Ground-Based Multichannel SAR Test Bed

In this paper, a ground-based phased-array radar is used to investigate the ability of a multichannel synthetic aperture radar (MSAR) to produce high-fidelity images of dynamic ocean scenes by correcting the distortions caused by the motion of the water surface itself. The ground-based system, the Naval Research Laboratory Focused Phased Array Imaging Radar (FOPAIR), mimics an MSAR with multiple apertures arrayed in the flight direction by rapidly and repeatedly scanning across a linear array of 64 antenna elements. This generates a virtually unlimited time stack of coherent images in the same way that an airborne MSAR generates multiple images as the antennas fly past the scene. By manipulating a single FOPAIR data set, both undistorted “benchmark” imagery and distorted images corresponding to an airborne MSAR can be generated. More significantly, the time stack of emulated MSAR images can be processed further using the velocity synthetic aperture radar (VSAR) technique to significantly reduce the well-known but seemingly unavoidable distortions caused by surface wave motion. In this paper, VSAR is demonstrated experimentally for the first time using FOPAIR imagery of a small boat. MSAR systems with a wide range of aperture numbers are emulated, including the special case of a two-aperture system, commonly known as an along-track interferometric SAR. The results emphasize that VSAR processing does not require a long surface coherence time to produce fine-resolution imagery, unlike a single-channel SAR. The results also illustrate some limitations of VSAR as a means to measure velocity and produce high-fidelity imagery of dynamic ocean scenes.

GLACIER SURFACE MONITORING BY MAXIMIZING MUTUAL INFORMATION

Abstract. The contribution of Polarimetric Synthetic Aperture Radar (PolSAR) images compared with the single-channel SAR in terms of temporal scene characterization has been found and described to add valuable information in the literature. However, despite a number of recent studies focusing on single polarized glacier monitoring, the potential of polarimetry to estimate the surface velocity of glaciers has not been explored due to the complex mechanism of polarization through glacier/snow. In this paper, a new approach to the problem of monitoring glacier surface velocity is proposed by means of temporal PolSAR images, using a basic concept from information theory: Mutual Information (MI). The proposed polarimetric tracking method applies the MI to measure the statistical dependence between temporal polarimetric images, which is assumed to be maximal if the images are geometrically aligned. Since the proposed polarimetric tracking method is very powerful and general, it can be implemented into any kind of multivariate remote sensing data such as multi-spectral optical and single-channel SAR images. The proposed polarimetric tracking is then used to retrieve surface velocity of Aletsch glacier located in Switzerland and of Inyltshik glacier in Kyrgyzstan with two different SAR sensors; Envisat C-band (single polarized) and DLR airborne L-band (fully polarimetric) systems, respectively. The effect of number of channel (polarimetry) into tracking investigations demonstrated that the presence of snow, as expected, effects the location of the phase center in different polarization, such as glacier tracking with temporal HH compared to temporal VV channels. Shortly, a change in polarimetric signature of the scatterer can change the phase center, causing a question of how much of what I am observing is motion then penetration. In this paper, it is shown that considering the multi-channel SAR statistics, it is possible to optimize the separate these contributions.

Tropical Dry Deciduous Forest Stand Variable Estimation Using SAR Data

Optical remote sensing data have been extensively used to derive biophysical properties that relate forest type and composition. However, stand density, stand height and stand volume cannot be estimated directly from optical remote sensing data owing to poor sensitivity between these parameters and spectral reflectance. The ability of microwave energy to penetrate within forest vegetation makes it possible to extract information on both the crown and trunk components from radar data. The type of polarization employed determines the radar response to the various shapes and orientations of the scattering mechanisms within the canopy or trunk. This study mainly presents experimental results obtained with airborne E-SAR using polarimetric C and L bands over the tropical dry deciduous forest of Chandrapur Forest Division, Maharashtra. A detailed documentation of the relationship between SAR C & L bands backscattering and forest stand variables has been provided in the present study through linear correlation. Linear correlation of the single channel SAR derived estimates with the field measured means show a good correlation between L HV backscattering coefficient with stand volume (r2 = 0.71) and L HH backscattering coefficient with stand density (r2 = 0.75). The results imply that SAR data has significant potential for stand menstruation in operational forestry.

Radar and Optical Images Fusion Using Stripmap SAR Data with Multilook Processing

Radar and Optical Images Fusion Using Stripmap SAR Data with Multilook Processing The paper presents the real-life data results of SAR and optical images data fusion. The fusion has been carried out for SAR images obtained in stripmap SAR mode using multilook processing with different methods of final image creation. The aim of the fusion was to enhance the target recognition capabilities on the Earth surface for a simple single-channel SAR receiver.

The Method of Sub-apertures Space-frequency Processing for the Single-channel SAR Moving Target Detection

该文提出了一种基于单通道多子孔径空频自适应处理的运动目标检测方法：首先给出了基于方位频谱划分获取子孔径的处理过程，构造出类似于多通道的子图像。详细分析了多子孔径空时等效信号的原理，将不同合成中心时刻的子孔径序列所获得的回波信号等效成同一时刻不同空间位置所获得的回波信号，利用时延差异来获取空间信息，建立起后续处理的2维信号模型。在此基础上，结合多通道杂波抑制的思想，提出了采用多子孔径间空频自适应处理实现杂波抑制与运动目标检测的算法。最后，仿真实验验证了该方法的有效性。

A genetic expectation-maximization method for unsupervised change detection in multitemporal SAR imagery

In this paper, we present a novel, automatic and unsupervised change-detection approach to the analysis of single-channel single-polarization multitemporal SAR images. The statistical parameters of the changed and unchanged classes, which are assumed to follow a generalized Gaussian (GG) distribution in the analysed log-ratio image, are explicitly estimated by the expectation-maximization (EM) algorithm initialized with a robust strategy based on genetic algorithms (GAs). In addition, the proposed approach integrates two further processing capabilities. The first one intends to cope with the problem of the automatic detection of multiple changes in the scene. This is carried out by modelling the log-ratio image histogram with a multimodal GG mixture whose number of components is estimated basing on the Bayesian information criterion (BIC). The second processing capability allows exploitation of spatial contextual information in the change detection process through a Markovian formulation. Results obtained on both simulated and real data are reported and discussed.

On the Design and Evaluation of Multiobjective Single-Channel SAR Image Segmentation Algorithms

Multiobjective segmentation algorithms are based on an objective function, consisting of two or more terms, that is minimized by using an optimization algorithm. The objective terms represent differing segmentation objectives, the most popular of which are statistical likelihood of pixel values and smoothness of segment boundaries. Many assumptions are built into the objective function, and we present a case study based on the algorithm of Stewart to demonstrate the importance of analyzing algorithm characteristics to test the validity of hidden assumptions. We develop a set of simulated test images and a novel segmentation performance metric for use with simulated data. An innovative aspect of the Stewart algorithm (SA) is the probability of false alarm (PFA) model used to weight the objective terms. This is intended to dynamically balance the terms as the algorithm progresses. The PFA model is only valid for false edges, and we have shown that the number of selected true edges increases as segmentation evolves, making the theoretical weight model increasingly invalid. In addition, we found problems with several other algorithm assumptions. We tested algorithm performance against a fixed-weight version. We found that the performance of the SA was worse than a fixed-weight version. Thus, while the two-term objective function algorithm does deliver reasonable performance for multilook data, the fixed-weight version gives better performance. While these results hold only for simulated data, we believe that the experimental results indicate the need for a more powerful approach to multiobjective synthetic aperture radar segmentation.

A New Application for PolSAR Imagery in the Field of Moving Target Indication/Ship Detection

Recent analysis of a polarimetric synthetic aperture radar (PolSAR) velocity experiment has shown that azimuth ambiguities can provide useful information for the detection of moving targets with an across-track velocity component in PolSAR images using cross-polarization clutter cancellation. This paper examines this phenomenon using theoretical analysis and simulations and illustrates its application with experimental results. It is shown that, for reciprocal scatterers, the phase difference between HV and VH images is zero, whereas for HV and VH ambiguities, the phase difference is pi. For land applications, clutter cancellation is essential for the detection of moving targets in PolSAR images. For maritime applications, it can be used to distinguish images from ambiguities. However, it is important to note that the underlying physical reasons for the performance of clutter cancellation are entirely different from ground moving target indication along-track interferometric SAR. It is also shown that cross-pol clutter cancellation is related to filtering of a double-rate-sampled single-channel SAR signal.

Detection of invisible moving targets in foliage penetration ultra-wide-band synthetic aperture radar images

Based on clean techniques and data reconstruction, a novel method to detect invisible moving targets in a foliage penetration ultra-wide-band synthetic aperture radar (FOPEN UWB SAR) image is proposed. The clean techniques are used to suppress clutter inside the region of interest (ROI) and the data reconstruction is used to reject clutter outside the ROI. Radar experimental results shows that a 10-dB gain of signal-to-clutter ratio is obtained in the range compression data domain and the invisible moving targets are detected only using a single-channel FOPEN UWB SAR.

Segmentation-based joint classification of SAR and optical images

The authors devise a new data fusion technique for classification that exploits the information contained in single-channel SAR and optical images optimally. In fact, even though good classification performance can be achieved by using multiple optical channels, it is shown that classification results from a single optical channel are not appreciably better than those obtained with a single SAR image. The impact of fusion of SAR and optical images is investigated quantitatively. To characterise the limits that can be achieved, both lower and upper bounds to classification performance are introduced, corresponding respectively to single-pixel classification and joint classification of all pixels in the regions defined by the ground truth. First, an optimised technique for single-channel image segmentation followed by maximum likelihood (ML) classification is proposed. A significant performance improvement is demonstrated by classifying the homogeneous regions identified by segmentation, instead of single pixels or even small windows of 3×3 pixels. Indeed, the result consistently approaches the upper bound. Next, the proposed ML segmentation technique is extended to exploit the information available in SAR and optical images jointly to define the best set of segments. The segmented SAR and optical images are then used together to obtain the best possible classification of the segments, yielding a significant performance improvement with respect to using either sensor alone. Classification performance is again significantly better than with single pixels, or small windows, and approaches the upper bound.

Information fusion for estimation of summer MIZ ice concentration from SAR imagery

The authors define the concept of information fusion and show how they used it to estimate summer sea ice concentration in the marginal ice zone (MIZ) from single-channel SAR satellite imagery. They used data about melt stage, wind speed, and surface temperature to generate temporally-accumulated information, and fused this information with the SAR image, resulting in an interpretation of summer MIZ imagery. They also used the results of previous classifications of the same area to guide and correct future interpretations, thus fusing historical information with imagery and nonimagery data. They chose to study the summer MIZ since summer melt conditions cause classification based upon backscatter intensity to fail, as the backscatter of open water, thin ice, first-year ice, and multiyear ice overlap to a large degree. This makes it necessary to fuse various information and data to achieve proper segmentation and automated classification of the image. Their results were evaluated qualitatively and showed that their approach produces very good ice concentration estimates in the summer MIZ.