Target-to-clutter Ratio Research Articles

Cluster Adaptive Matching Pursuit for Multipolarization Through-Wall Radar Imaging

Among various types of sensors, through-wall radar is a promising sensor that can penetrate obstacles and sense the targets behind them. In this article, by exploiting the common sparsity property of the multipolarization images, we propose a cluster adaptive matching pursuit (CAMP) algorithm for multipolarization through-wall radar imaging (TWRI). Our proposed CAMP algorithm is developed from compressive sensing (CS) theory. In the proposed CAMP algorithm, the common sparsity among the multipolarization images is utilized through a multiple-measurement vector (MMV) model. The target area is obtained through a support set. We model the support set as a combination of several clusters, thus the number of clusters and the pattern of each cluster are two important factors that can describe the support set. To obtain the pattern of a cluster that corresponds to a certain target in the imaging scene, we propose an adaptive clustering strategy based on the backtracking scheme. On this basis, the number of clusters is adaptively selected through a two-layer matching pursuit method. Different from conventional matching pursuit algorithms that often use atom methods, the support set is acquired cluster by cluster in the two-layer matching pursuit method, which accelerates the speed of acquiring the support set. Experimental results show that our proposed algorithm can locate targets accurately in TWRI with a high target-to-clutter ratio (TCR) performance.

A Novel Technique for Contrast Target Detection in Through-the-Wall Radar Images

Through-the-wall radar images suffer from strong clutter due to the presence of dielectric walls and environmental noise. Several approaches have been explored to reduce clutter for detecting targets with similar dielectric constants. However, in real-time scenarios, targets may not have similar dielectric constants. Therefore, it is necessary to develop a technique for detecting contrast targets—that is, targets with different dielectric constants behind a wall in the same scene. This paper proposes a novel technique that combines time gating and Gaussian mixture model based low-rank approximation to detect contrast targets present in the same scene. The proposed method was compared to existing clutter reduction techniques in terms of target-to-clutter ratio (TCR). The experimental results showed that the proposed approach yielded highest TCR (130.1).

PolSAR Ship Detection Based on Azimuth Sublook Polarimetric Covariance Matrix

Polarimetric synthetic aperture radar (PolSAR) is an important tool for marine remote sensing. In the past two decades, subaperture decomposition is considered as an alternative method to extract ship targets, especially in complex conditions where the detection based on intensity performs not well. The introduction of polarization information can enhance the detection ability of radar. The combination of sublook techniques and polarization will lead to more precise target extraction. The first methods jointly using information of polarization and sublook is the polarimetric internal Hermitian product (Pol-IHP), which nowadays has been corrected as polarimetric Hermitian internal product (Pol-HIP). However, it is limited by the number of sublooks and recently developed as generalized multi-sublooks correlation (GMC). In this study, a novel sublook polarimetric covariance matrix (SPCM) is established, based on which a sublook-polarimetric whitening filter (Sub-PWF) is further proposed for ship detection. The SPCM represents a given pixel using a high-dimensional covariance matrix which utilizes the spectral correlation in imaging progress and the scattering characteristics of different polarization channels. The superiority of Sub-PWF based on SPCM is validated via the measured single look complex (SLC) PolSAR data from RadarSAT-2 (RS-2) and GaoFen-3 (GF-3). Results show this new method can improve the target to clutter ratio (TCR) and reduce the clutter fluctuation. The ROC curves in both small and large scenes proves that Sub-PWF can reach ideal detection performance.

Ship Detection of Polarimetric SAR Images Using a Nonlocal Spatial Information-Guided Method

Ship detection of polarimetric synthetic aperture radar (PolSAR) plays an important role in marine monitoring and ocean protection. Over the past years, local spatial information around pixels has been successfully applied to this task. However, few works have been done on PolSAR ship detection using the nonlocal spatial information (NSI). Within this context, we here propose one NSI-guided ship detection method PMR. Briefly speaking, the feature power difference (PD) is first constructed by computing the total power difference between the center pixel <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">c</i> and its most similar nonlocal pixel <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i</i> within a 7×7 window. Then, the polarimetric feature reflection symmetry (RS) is introduced into PD to construct the method PMR (i.e., PD Multiply RS) for further enhancing the target-to-clutter ratio (TCR) and improving the ship detection accuracy. Experiments carried out on three real PolSAR datasets show that, in comparison with some other methods, especially the recently proposed local neighborhood information-based ship detector PWF <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><i>N</i></sub> , PMR is more apt for ship detection. On average, its figure of merit (FoM) and TCR values respectively surpass PWF <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><i>N</i></sub> 0.24 and 12.83 dB.

Region-Based Polarimetric Covariance Difference Matrix for PolSAR Ship Detection

To more effectively detect small ships, in this article, a novel region-based polarimetric covariance difference matrix [RP] is put forward, which mainly consists of two stages. Briefly speaking, in the first stage, a new pixel representation way is proposed to depict the spatial characteristics of pixel, through which the difference information related to pixel’s local region is calculated as well. In the second stage, the global region difference information of pixel is computed. Finally, we construct [RP] via fusing these two different kinds of information together with a balance factor <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$c$ </tex-math></inline-formula> . Meanwhile, considering that the backscattering energy of ships is useful for ship detection, a new intensity-driven polarimetric notch filter (ID-PNF <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">RP</sub> ) is also derived from [RP]. Three different datasets are adopted to evaluate the effectiveness of [RP] and ID-PNF <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">RP</sub> . Experimental results show that: 1) compared with the polarimetric covariance matrix [ <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$C$ </tex-math></inline-formula> ] and the polarimetric covariance difference matrix [ <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$P$ </tex-math></inline-formula> ], [RP] is more suitable for ship detection and 2) compared with the original geometrical perturbation-polarimetric notch filter (GP-PNF) and the total power detector SPAN, the proposed method ID-PNF <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">RP</sub> can better detect small ships with greater figure of merit (FoM) and target-to-clutter ratio (TCR) values.

Joint Polarimetric Subspace Detector Based on Modified Linear Discriminant Analysis

Polarimetric synthetic aperture radar (PolSAR) is widely used in remote sensing and has important applications in the detection of ships. Although many polarimetric detectors have been proposed, they are not well combined. Recently, a polarimetric detection optimization filter (PDOF) was proposed, which performs well in most environments. In this study, a novel subspace form of the PDOF [strict PDOF (SPDOF)] was further developed based on the Cauchy inequality and matrix decomposition theories, enhancing detection performance. Furthermore, a simple method to determine the optimal dimension of the subspace detector based on the trace ratio form was proposed by calculating the area under the receiver operating characteristic (ROC) curve, reaching the best detection performance among the subspaces of the detector. Moreover, to combine different subspace detectors, a modified linear discriminant analysis was proposed and developed for the diagonal loading detector (DLD) based on polarimetric subspaces. The experimental results demonstrate the superiority of these joint polarimetric subspace detectors. Most importantly, DLD solves for previous limitations due to the complex clutter background and achieves a performance comparable to that of the Wishart (Gaussian) distribution, particularly in the low target-to-clutter ratio (TCR) case.

A Two-Stage Method for Ship Detection Using PolSAR Image

Ship detection using polarimetric SAR (PolSAR) images has recently been an active topic in the Earth observation field. There, how to detect small ships is an open and challenging issue. Within this context, we put forward a two-stage ship detection model, by which a novel ship detection method is proposed as well. Briefly, in the first stage, a suppression manipulation is adopted to suppress sea clutter, where the feature SVVS <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><i>O</i></sub> is built on the intensity information with the orientation angle compensation (OAC). In the second stage, an enhancement manipulation is further executed to highlight ships from the suppressed sea clutter, where the features PID (polarimetric intensity difference) and NsD (nonsurface degree) are first constructed with SVVS <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><i>O</i></sub> and a series of theoretical derivations. Then, via fusing PID and NsD together, the two-stage-based method FPAN is proposed to detect ships. To demonstrate its performance, we apply FPAN to four different L-Band PolSAR datasets. Experimental results reveal that, compared to other state-of-the-art methods, especially the DBSP <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><i>CP</i></sub> method, FPAN is more effective in detecting small ships. On average, its figure-of-merit (FoM) and target-to-clutter ratio (TCR) values are, respectively 9.40% and 25.18% greater than those of DBSP <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><i>CP</i></sub> , while the time consumption is just 58.67% of the latter.

Infrared Target Detection in Cluttered Environments by Maximization of a Target to Clutter Ratio (TCR) Metric Using a Convolutional Neural Network

Infrared target detection is a challenging computer vision problem which involves detecting small targets in heavily cluttered conditions while maintaining a low false alarm rate. We propose a network that optimizes a “target to clutter ratio”(TCR) metric defined as the ratio of the output energies produced by the network in response to targets and clutter. A TCR-network (TCRNet) is presented in which the filters of the first convolutional layer are composed of the eigenvectors most responsive to targets or to clutter. These vectors are analytically derived via a closed form optimization of the TCR metric. The remaining convolutional layers are trained using a novel cost function also designed to optimize the TCR criterion. We evaluate the performance of the TCRNet using a public domain medium wave infrared dataset released by the US Army's Night Vision Laboratories, and compare it to the state-of-the-art detectors such as Faster regions with convolutional neural networks (R-CNN) and Yolo-v3. The TCRNet demonstrates state-of-the-art results with greater than 30% improvement in probability of detection while reducing the false alarm rate by more than a factor of two when compared to these leading methods. Experimental results are shown for both day and night time images, and ablation studies are presented which demonstrate the contribution of the first layer eigenfilters, additional convolutional layers, and the benefit of the TCR cost function.

PolSAR Ship Detection Using the Joint Polarimetric Information

In this article, we investigate the scattering components of ships and find that the surface scattering may be the primary scattering for some ships, especially small ships. Meanwhile, the drawbacks of the complete polarimetric covariance difference matrix [CP] are also pointed out in theory. Based on these analyses, two new methods are then constructed to detect the ships. More specifically, the first one RsP is constructed by directly combining the similarity parameter of surface scattering Rs and the power-maximization synthesis (PMS) detector. The second one RsDVH is designed by taking advantage of four different features (i.e., Rs, double-bounce scattering, volume scattering, and helix scattering), which are all derived from the joint polarimetric information that is developed by combing the information of the polarimetric covariance matrix [ $C$ ] and [CP]. Subsequently, the generalized Gamma distribution ( $\text{G}\Gamma \text{D}$ ) is found suitable for characterizing the RsDVH values of the sea clutter. At last, an adaptive constant false-alarm-rate (CFAR) detector developed from RsDVH is proposed for ship detection. To verify the effectiveness of RsP and RsDVH, four polarization synthetic aperture radar (PolSAR) imageries are tested, including one L-band UAVSAR imagery with 19 ships, two L-band AIRSAR imageries with 22 and 53 ships, respectively, and one C-band GF-3 imagery with ten ships. The experimental results show that: 1) the surface scattering is beneficial to detecting ships, especially the ships with prominent surface scatterings; 2) compared with other state-of-the-art methods, RsDVH can more effectively enhance the target-to-clutter ratio (TCR) values of small ships in the case of rough sea surface; and 3) the joint polarimetric information that is put forward and exploited for the first time in this article has a greater potential to help ship detectors improve their detection performances than the traditional polarimetric information included in [ $C$ ].

PolSAR Ship Detection Based on Polarimetric Correlation Pattern

For polarimetric synthetic aperture radar (PolSAR) data, the correlation of two polarization channels is sensitive to the target orientation relative to the sensor’s illumination direction. In this letter, the concept of polarimetric correlation pattern is proposed to explore this scattering diversity. The core idea is to extend the polarimetric correlation from a fixed angle to the rotation domain along the radar line of sight. A set of new polarimetric features are derived, and three features with high target-to-clutter ratio (TCR) are selected for PolSAR ship detection. The proposed ship detection method mainly contains three steps: the features selection, thresholding procedure, and morphological filtering. Experimental studies with Radarsat-2 and GaoFen-3 data validate the advantage of the proposed approach, especially for inshore dense ship discrimination.

Ship Detection From PolSAR Imagery Using the Complete Polarimetric Covariance Difference Matrix

In this paper, we proposed a complete polarimetric covariance difference matrix [ $CP$ ]-based algorithm for ship detection in polarimetric synthetic aperture radar (PolSAR) imagery. To calculate [ $CP$ ], we first developed a scheme to reflect the polarimetric scattering differences between ship pixel (SP) and its neighboring pixels (ISPs) and, then, dividedly accumulated the amplitude and phase differences between SP and ISPs. Compared to the polarimetric covariance difference matrix [ $P$ ] developed in our earlier work, [ $CP$ ] effectively overcomes the drawback of the lack of the phase information in [ $P$ ]. To demonstrate the effectiveness of the proposed algorithm, we applied the [ $CP$ ]-based ship detection algorithm to four PolSAR data sets, including one UAVSAR L-band data set with 21 ships, two AIRSAR L-band data sets with 11 and 22 ships, respectively, and one Radarsat-2 C-band data set with 8 ships. Experimental results show that: 1) the proposed algorithm can effectively detect ships with high target-to-clutter ratio (TCR) values and 2) [ $CP$ ] has a better performance than the traditional polarimetric covariance matrix [ $C$ ] and [ $P$ ] on ship detection. To be more specific, the average TCR value of the proposed algorithm (23.86 dB) is 6.07 and 7.47 dB higher than PNF $_{C}$ (i.e., the geometrical perturbation-polarimetric notch filter) and RS $_{C}$ (i.e., the reflection symmetry method), respectively.

A Ship Detector Applying Principal Component Analysis to the Polarimetric Notch Filter

Ship detection using polarimetric synthetic aperture radar (PolSAR) data has attracted a lot of attention in recent years. Polarimetry can provide information regarding the scattering mechanisms of targets, which helps discriminate between ships and sea clutter. This enhancement is particularly valuable when we aim at detecting smaller vessels in rough sea states. This work exploits a ship detector called the Geometrical Perturbation-Polarimetric Notch Filter (GP-PNF), and it is aimed at improving its performance especially when less polarimetric images are available (e.g., dual-polarimetric data). The idea is to design a new polarimetric feature vector containing more features that are renowned to allow separation between ships and sea clutter. Then, a Principal Component Analysis (PCA) is further used to reduce the dimensionality of the new feature space. Experiments on four real Sentinel-1 datasets are carried out to demonstrate the validity of the proposed method and compare it against other ship detectors. Analyses of the experimental results show that the proposed algorithm can not only reduce the false alarms significantly, but also enhance the target-to-clutter ratio (TCR) so that it can more effectively detect weaker ships.

Underwater Cylindrical Object Detection Using the Spectral Features of Active Sonar Signals with Logistic Regression Models

The issue of detecting objects bottoming on the sea floor is significant in various fields including civilian and military areas. The objective of this study is to investigate the logistic regression model to discriminate the target from the clutter and to verify the possibility of applying the model trained by the simulated data generated by the mathematical model to the real experimental data because it is not easy to obtain sufficient data in the underwater field. In the first stage of this study, when the clutter signal energy is so strong that the detection of a target is difficult, the logistic regression model is employed to distinguish the strong clutter signal and the target signal. Previous studies have found that if the clutter energy is larger, false detection occurs even for the various existing detection schemes. For this reason, the discrete Fourier transform (DFT) magnitude spectrum of acoustic signals received by active sonar is applied to train the model to distinguish whether the received signal contains a target signal or not. The goodness of fit of the model is verified in terms of receiver operation characteristic (ROC), area under ROC curve (AUC), and classification table. The detection performance of the proposed model is evaluated in terms of detection rate according to target to clutter ratio (TCR). Furthermore, the real experimental data are employed to test the proposed approach. When using the experimental data to test the model, the logistic regression model is trained by the simulated data that are generated based on the mathematical model for the backscattering of the cylindrical object. The mathematical model is developed according to the size of the cylinder used in the experiment. Since the information on the experimental environment including the sound speed, the sediment type and such is not available, once simulated data are generated under various conditions, valid simulated data are selected using 70% of the experiment cylinder data. The selected simulated data are used to train the model. Randomly selected experiment cylinder data, which are 70% of the total experimental cylinder data, and the rock measurement data are used to test the model. This process is repeatedly carried out 1000 times. The results show that the proposed method is effective under the circumstance where experimental data are not sufficient and a mathematical model is available for a target.

A Novel Ship Detector Based on the Generalized-Likelihood Ratio Test for SAR Imagery

Ship detection with synthetic aperture radar (SAR) images, acquired at different working frequencies, is presented in this paper where a novel technique is proposed based on the generalized-likelihood ratio test (GLRT). Suitable electromagnetic models for both the sea clutter and the signal backscattered from the ship are considered in the new technique in order to improve the detector performance. The GLRT is compared to the traditional constant false alarm rate (CFAR) algorithm through Monte–Carlo simulations in terms of receiver operating characteristic (ROC) curves and computational load at different bands (S-, C-, and X-). Performances are also compared through simulations with different orbital and scene parameters at fixed values of band and polarization. The GLRT is then applied to real datasets acquired from different sensors (TerraSAR-X, Sentinel-1, and Airbus airborne demonstrator) operating at different bands (S-, C-, and X-). An analysis of the target-to-clutter ratio (TCR) is then performed and detection outcomes are compared with an automatic identification system data when available. Simulations show that the GLRT presents better ROCs than those obtained through the CFAR algorithm. On the other side, results on real SAR images demonstrate that the proposed approach greatly improves the TCR (between 22 and 32 dB on average), but its computational time is 1.5 times slower when compared to the CFAR algorithm.

On the Use of Cross-Correlation between Volume Scattering and Helix Scattering from Polarimetric SAR Data for the Improvement of Ship Detection

Synthetic Aperture Radar (SAR) ship detection is an important maritime application. However, azimuth ambiguities caused by the finite sampling of the Doppler spectrum are often visible in SAR images and are always mistaken as ships by classic detection techniques, like the Constant False Alarm Rate (CFAR). It is known that radar targets and azimuth ambiguities have different characteristics in polarimetric SAR (PolSAR) data, i.e., first ambiguities usually have strong odd- or double-bounce scattering and the maximum amplitude of the first ambiguity in SHV is always considerably smaller than that of the corresponding target for zero or high velocity. On the basis of this characteristics, this paper finds that first ambiguities usually have low volume scattering power relative to ships and almost have no helix scattering by Yamaguchi decomposition. But some residual ambiguities still exit in the volume scattering power and have similar scattering intensity to small ships, and some parts of a ship also have zero helix scattering owing to some physical factors (e.g., ship structure, radar incidence angle, etc.). Thus, for high-precision ship detection, a new ship detection method based on cross-correlation between the volume and helix scattering mechanisms derived from Yamaguchi decomposition is proposed to avoid false alarms caused by azimuth ambiguities and enhance Target-to-Clutter Ratio (TCR) for improving the miss detection rate of small ships. By experiments, it is proved that our method can work effectively and has high detection accuracy.

Target-to-Clutter Ratio Enhancement of Images in Through-the-Wall Radar Using a Radiation Pattern-Based Delayed-Sum Algorithm

In this paper, we compare the quality of images reconstructed by a conventional delayed-sum (DS) algorithm and radiation pattern-based DS algorithm. In order to evaluate the quality of images, we apply the target-to-clutter ratio (TCR), which is commonly used in synthetic aperture radar (SAR) image assessment. The radiation pattern-based DS algorithm enhances the TCR of the image by focusing the target signals and preventing contamination of the radar scene. We first consider synthetic data obtained through GprMax2D/3D, a finite-difference time-domain (FDTD) forward solver. Experimental data of a 2-GHz bandwidth stepped-frequency signal are collected using a vector network analyzer (VNA) in an anechoic chamber setup. The radiation pattern-based DS algorithm shows a 6.7-dB higher TCR compare to the conventional DS algorithm.

A novel infrared small moving target detection method based on tracking interest points under complicated background

Infrared moving target detection is an important part of infrared technology. We introduce a novel infrared small moving target detection method based on tracking interest points under complicated background. Firstly, Difference of Gaussians (DOG) filters are used to detect a group of interest points (including the moving targets). Secondly, a sort of small targets tracking method inspired by Human Visual System (HVS) is used to track these interest points for several frames, and then the correlations between interest points in the first frame and the last frame are obtained. Last, a new clustering method named as R-means is proposed to divide these interest points into two groups according to the correlations, one is target points and another is background points. In experimental results, the target-to-clutter ratio (TCR) and the receiver operating characteristics (ROC) curves are computed experimentally to compare the performances of the proposed method and other five sophisticated methods. From the results, the proposed method shows a better discrimination of targets and clutters and has a lower false alarm rate than the existing moving target detection methods.

A Family of Spectral Target Signature Transforms: Relationship to the Past, New Transforms, and Sensitivity Tests

Previous studies introduced, examined, and tested a variety of registration-free transformations, specifically the diagonal, whitening/dewhitening, and target CV (covariance) transformations, that temporally evolve spectral signatures under varying conditions. Target spectral signatures were transformed using imagery of spatially overlapping regions from datasets collected at different times. This letter shows that these previously studied registration-free transformations can be described using a single general equation and form a subset of a family of valid transforms. Further, this letter finds only a small reduction in target detection results from using nonoverlapping but similar regions to transform target signatures in matched filter searches. The diagonal, whitening/dewhitening, and target CV transformations appear in this formulation as having integral exponent parameter n (n = 0, 1, 2, respectively). These transformed target signatures, used in matched filter searches, were tested on images taken from two very different data collects using different sensors, targets, and backgrounds. In one dataset, the transform was applied to multispectral images taken from an airborne longwave infrared sensor binned to 30 bands. The other dataset used images of a variety of targets that were collected using broad-band, bore-sighted, staring array sensors sensitive to visible, shortwave infrared, midwave infrared, and longwave infrared wavelengths. Transformation performance was tested using target-to-clutter ratio (TCR) and receiver operator characteristic (ROC) curve. Transforms with exponents between 0 and 2 yielded the largest TCR and remained relatively constant. Optimal exponents for the transformation were target dependent for both datasets. Further, only minimal reduction in TCR was observed for transforms (O < n < 2) that had no shared overlapping regions, although the areas used to transform the spectral signatures shared similar content (trees, roads, etc.).

Object detection by using "whitening/dewhitening" to transform target signatures in multitemporal hyperspectral and multispectral imagery

Changes in atmosphere, ground conditions, scene temperature, solar illumination, and sensor response can significantly affect the detected multispectral and hyperspectral data. Using uncorrected spectral target signatures in spectral matched filter searches therefore results in target detection with concomitant high false-alarm rates due to changes in multispectral and hyperspectral images. This letter introduces the use of the whitening/dewhitening (WD) transform to help correct target spectral signatures under varying conditions. An important feature of this transform is that it does not require subpixel registration between images collected at two distinct times. The transform was tested on images taken from two very different data collects using different sensors, targets, and backgrounds. In one dataset, the transform was applied to hyperspectral images taken from airborne longwave infrared sensor binned to 30 bands and the other data collect used images of a variety of tanks, trucks, calibration panels that were collected using bore-sighted broadband visible, shortwave infrared, midwave infrared, and longwave infrared staring array sensors. Target spectral signatures were transformed using imagery of spatially overlapping regions from datasets collected at different times and processed using the whitening and then dewhitening transform (inverse of a whitening transform). Use of the WD transform yielded a large target-to-clutter ratio (TCR) and was compared to the TCR derived from other transforms that approximated the cross-covariance matrix. In addition, the WD-transformed signatures applied in a matched filter search found targets (some concealed behind vegetative foliage or underneath camouflage) with low false-alarm rates as shown in a receiver operator characteristic curve. This letter demonstrates that the WD transform enhances searches for concealed targets in multisensor and hyperspectral data.

Eigenvector method for maximum-likelihood estimation of phase errors in synthetic-aperture-radar imagery

We develop a maximum-likelihood (ML) algorithm for estimation and correction (autofocus) of phase errors induced in synthetic-aperture-radar (SAR) imagery. Here, M pulse vectors in the range-compressed domain are used as input for simultaneously estimating M − 1 phase values across the aperture. The solution involves an eigenvector of the sample covariance matrix of the range-compressed data. The estimator is then used within the basic structure of the phase gradient autofocus (PGA) algorithm, replacing the original phase-estimation kernel. We show that, in practice, the new algorithm provides excellent restorations to defocused SAR imagery, typically in only one or two iterations. The performance of the new phase estimator is demonstrated essentially to achieve the Cramer–Rao lower bound on estimation-error variance for all but small values of target-toclutter ratio. We also show that for the case in which M is equal to 2, the ML estimator is similar to that of the original PGA method but achieves better results in practice, owing to a bias inherent in the original PGA phase estimation kernel. Finally, we discuss the relationship of these algorithms to the shear-averaging and spatial correlation methods, two other phase-correction techniques that utilize the same phase-estimation kernel but that produce substantially poorer performance because they do not employ several fundamental signal-processing steps that are critical to the algorithms of the PGA class.