Image Coregistration Errors Research Articles

A Novel Automated Method for Point-Based Change Detection Increases Estimation Accuracy of Breeding Pairs in Waterbird Colonies

The number of breeding pairs is an essential indicator for assessing waterbird colony status. Accurate estimates require distinguishing stationary adults (likely to be breeders on nests) from nonstationary adults (likely to be loafing or foraging breeders or non-breeders). High-resolution multi-temporal aerial imagery obtained from manned aircraft, satellites, or unmanned aircraft systems (UAS) can be analyzed to determine bird movements and derive estimates of the number of stationary adults, which corresponds to the number of active nests, and thereby breeding pairs. Automated detection of stationary objects, such as nesting waterbirds, can be complicated by small positional changes of objects, either because of image co-registration errors or because the object slightly shifts position. A non-parametric, point-based approach was developed to distinguish stationary birds from moving birds using sequences of either two or three consecutive remotely-sensed images. This approach was tested with simulated data and during a case study of nesting American White Pelicans (Pelecanus erythrorhynchos). In both cases, the non-parametric point-based approach had higher accuracy than other established methods such as ground counts. Using two consecutive images had higher sensitivity (correct classification of stationary birds) while using three consecutive images had higher specificity (correct classification of nonstationary birds). This novel, multi-temporal nearest-neighbor method is most useful when positional shifts of stationary animals is low between consecutive images.

PD-0942 Intrafraction error analysis of homemade mouth-bite masks in linac-based SRS for brain metastasis

Purpose or Objective To assess the intrafraction accuracy of a frameless stereotactic linac-based radiosurgery (SRS) for brain metastases (BM) using a homemade mouth-bite thermoplastic mask in combination with cone-beam computed tomography (CBCT) and six-degrees of freedom (6-DOF) couchtop. Materials and Methods Covid-19 restrictions resulted in significant delays in the supply of the dedicated devices for BM SRS. A frameless approach using a homemade mouth-bite thermoplastic mask was implemented to offer BM SRS in a period of limited mobility (figure 1). All patients were immobilized and treated at the same institution with 6 MV FFF VMAT radiosurgery, with a 2 mm isotropic expansion from the GTV to the PTV. Before treatment delivery, patients underwent a low-dose CBCT to check position accuracy. Through image co-registration, translational (x, y, z) and rotational errors (roll, pitch, and yaw) were determined. The 6-DOF couchtop was used to automatically relocate the patient with sub-millimetric precision. Immediately after irradiation, patients underwent a second CBCT to verify any translational or rotational errors occurring during therapy (intrafraction motion). $Φg Results From February 2020 to January 2021 sixteen patients (29 lesions) received BM SRS (14-21 Gy). The whole procedure, from the first CBCT scan to the end of irradiation and subsequent CBCT, required a median time of 9 minutes [6-13]. Mean translational error was 0.0 ± 0.3 mm [-0.7;0.9] in lateral direction, and -0.1 ± 0.5 mm [-1.4;1.0] in longitudinal direction. A 2.2 mm maximum shift was recorded on the vertical axis, nevertheless the mean translation error was 0.1 ± 0.6 mm. Roll, pitch and yaw registered a mean value of -0.1 ± 0.3° [-0.8;0.2], 0.0 ± 0.2° [-0.8;0.2], and -0.1 ± 0.3° [-0.8;0.4], respectively. The results are summarized in figure 2. Conclusion This study demonstrated that homemade mouth-bite thermoplastic masks combined with CBCT, 6-DOF couchtop, and fast FFF treatment delivery allow minimal intra-fraction uncertainties in BM SRS, confirming the accuracy of PTV margin definition. The results are comparable to the declared specifics of SRS dedicated commercial frameless mask, while improving patient's comfort compared to the invasive fixed frame. However, the moulding procedure requires improved RTT skills and expertise. Our findings show that SRS can be safely delivered in radiotherapy units without dedicated expensive installations, such as CyberKnife and Gamma Knife, thus reducing patient mobility according to Covid-19 restrictions.

Dual MRI-SPECT imaging system realized with Timepix pixel detector inserted into MR small animal scanner

Although MRI and SPECT are routine tools in medical imaging individually, the combination of these two modalities in multimodal imaging is still unexplored. By hybridizing high-resolution anatomical with functional imaging, combined MRI-SPECT could be a valuable addition to existing medical imaging methods, reducing overall scanning time and image co-registration errors. In contrast to PET, SPECT offers a cost-efficient range of applicable radioisotopes. This proof-of-principle study shows a modified experimental MRI-SPECT insert system consisting of an MR-compatible SPECT unit with hybrid semiconductor detectors Timepix. The insert system is equipped with CdTe pixelated sensors, tungsten collimators and a radiofrequency coil (RF). To increase the number of projections acquired, the setup is also equipped with an electromagnetic step motor Microcon SX 16-0503 for a sample rotation. Measurements were performed our own invented multimodal (1H/99mTc) inhomogeneous phantom filled with a liquid 99mTc radiotracer, emitting 140.5 keV γ-rays inside the RF coil by the Bruker BioSpec 47/20 (4.7 T) MR animal scanner. For the evaluation of measured data, SPECT-back projection software was developed in Matlab. Our results pave the way for a preclinical MRI-SPECT insert system. This research was performed in the framework of the Medipix Collaboration.

Geometric inaccuracy and co-registration errors for CT, DynaCT and MRI images used in robotic stereotactic radiosurgery treatment planning

PurposeTo measure the combined errors due to geometric inaccuracy and image co-registration on secondary images (dynamic CT angiography (dCTA), 3D DynaCT angiography (DynaCTA), and magnetic resonance images (MRI)) that are routinely used to aid in target delineation and planning for stereotactic radiosurgery (SRS). MethodsThree phantoms (one commercial and two in-house built) and two different analysis approaches (commercial and MATLAB based) were used to quantify the magnitude of geometric image distortion and co-registration errors for different imaging modalities within CyberKnife’s MultiPlan treatment planning software. For each phantom, the combined errors were reported as a mean target registration error (TRE). The mean TRE’s for different intramodality imaging parameters (e.g., mAs, kVp, and phantom set-ups) and for dCTA, DynaCTA, and MRI systems were measured. ResultsOnly X-ray based imaging can be performed with the commercial phantom, and the mean TRE ± standard deviation values were large compared to the in-house analysis using MATLAB. With the 3D printed phantom, even drastic changes in treatment planning CT imaging protocols did not greatly influence the mean TRE (<0.5 mm for a 1 mm slice thickness CT). For all imaging modalities, the largest mean TRE was found on DynaCT, followed by T2-weighted MR images (albeit all <1 mm). ConclusionsThe user may overestimate the mean TRE if the commercial phantom and MultiPlan were used solely. The 3D printed phantom design is a sensitive and suitable quality assurance tool for measuring 3D geometric inaccuracy and co-registration errors across all imaging modalities.

A Robust Radial Velocity Estimation Method for FDA-SAR

In multi-channel synthetic aperture radar-ground moving target indication (SAR-GMTI), most radial velocity estimation methods are based on the phase difference between channels. However, the image coregistration and channel phase errors will have a severe impact on the phase difference between channels. Moreover, it deteriorates the performance of the moving target radial velocity estimation. To solve this problem, a robust radial velocity estimation method is proposed using a frequency diverse array-SAR (FDA-SAR) in this letter. By introducing the step frequency, the interferometric phase among channels is a linear function of the Doppler frequency. The radial velocity of moving targets is embedded in the first-order term of the linear function. Meanwhile, the first-order term does not include channel phase error terms. Therefore, the accurate velocity of moving targets is estimated by the first-order coefficient which is solved by the least-squares fitting method. Afterward, according to the analysis and derivation, the proposed method is robust on the condition of image coregistration error. At last, simulations and data analysis illustrate the effectiveness of the proposed method.

Compensation of array lens effects for improved co-registration of passive acoustic mapping and B-mode images for cavitation monitoring.

Passive acoustic mapping (PAM) techniques offer a simple means of spatio-temporal cavitation monitoring during therapeutic ultrasound procedures. Implementation with a conventional diagnostic ultrasound system allows natural integration of PAM with B-mode imaging. However, the refracting properties of diagnostic array lenses may introduce PAM image registration errors that could lead to inaccuracies in treatment monitoring and guidance. To address these concerns, this paper presents lens characterization of two different array designs, analytical estimation of lens-induced source mapping errors in simple media, and experimental demonstration and correction of lens effects, reducing the depth-averaged image co-registration errors to no more than 0.52 mm.

Robust Radial Velocity Estimation Based on Joint-Pixel Normalized Sample Covariance Matrix and Shift Vector for Moving Targets

The clutter suppression and target radial velocity estimation are essential in the ground moving target indication processing with multichannel synthetic aperture radar (SAR) systems. In reality, the heterogeneous clutter, the image coregistration error, and channel mismatch will remarkably decline the estimation performance of the target radial velocity. To address these issues, a robust radial velocity estimation algorithm is proposed in this letter. Based on the joint-pixel signal model, the joint-pixel normalized sample covariance matrix (JPNSCM) is employed to mitigate the effect of heterogeneous clutter, and the shift vector determined by JPNSCM is used to obtain the actual target steering vector. Then, the adaptive matched filtering algorithm is adopted to estimate the target radial velocity. Compared with traditional estimation algorithms, the proposed method obtains better performance in both simulations and real SAR data experiments.

Assessment of image co-registration accuracy for frameless gamma knife surgery.

Image co-registration is used in frameless gamma knife radiosurgery (GKSRS) to assign a stereotactic coordinate system and verify patient setup before irradiation. The accuracy of co-registration with cone beam computed tomography (CBCT) images of a Gamma Knife IconTM (GK Icon) was assessed, and the effects of the region of co-registration (ROC) were studied. CBCT-to-CBCT co-registration is used for patient setup verification, and its accuracy was examined by co-registering CBCT images taken at various configurations with a reference CBCT series. The accuracy of stereotactic coordinate assignment was investigated by co-registering stereotactic CT images with CBCT images taken at various configurations. An anthropomorphic phantom was used, and the coordinates of fifteen landmarks inside the phantom were measured. The co-registration accuracy between stereotactic magnetic resonance (MR) and CBCT images was evaluated using images from forty-one patients. The positions of the anterior and posterior commissures were measured in both a fiducial marker-based system and a co-registered system. To assess the effects of MR image distortions, co-registration was performed with four different ranges, and the accuracy of the results was compared. Co-registration between CBCT images gave a mean three-dimensional deviation of 0.2 ± 0.1 mm. The co-registration of stereotactic CT images with CBCT images produced a mean deviation of 0.5 ± 0.2 mm. The co-registration of MR images with CBCT images resulted in the smallest three-dimensional difference (0.8 ± 0.3 mm) when a co-registration region covering the skull base area was applied. The image co-registration errors in frameless GKSRS were similar to the imaging errors of frame-based GKSRS. The lower portion of the patient’s head, including the base of the skull, is recommended for the ROC.

A New Portable Hybrid Camera for Fused Optical and Scintigraphic Imaging: First Clinical Experiences.

Although portable gamma cameras (PGCs) have been helpful to depict sentinel nodes (SNs), sometimes nuclear physicians or surgeons have difficulties to interpret PGC images because of the lack of anatomical information. The aim of the present study was to develop and clinically test the prototype of a new portable hybrid camera (PHC), which adds optical to γ-imaging. In 2 hospitals, the existing PGC (Sentinella S102; Oncovision) was upgraded with an optical module (BB2-08S2C-25; Point Grey Research) to build a PHC. Preoperative PHC overview images (positioned at 15 cm distance) and close-up image (position at <5 cm distance) were obtained from 25 patients (12 melanoma, 2 oral cavity, and 11 breast cancer) after conventional lymphoscintigraphy. Errors in the optical image coregistration were evaluated with a 5-mm accuracy for each patient. Conventional lymphoscintigraphy and the close-up PHC images depicted 55 SNs in total. In the PHC overview images, the optical module offered fused optical and γ-imaging indicating the image field of view and anatomical SN locations. Average optical image coregistration errors were 1.0 cm (range, 0-2.0 cm). Fused optical and γ-imaging with the prototype PHC is technically feasible and helpful for the image interpretation. The optical image visualizes the γ-image field of view, enabling SN localization in an anatomical context in a preoperative setting; however, for the operating room, the use of its optical component needs to be additionally adjusted.

Accuracy of applicator tip reconstruction in MRI-guided interstitial 192Ir-high-dose-rate brachytherapy of liver tumors

Background and purposeTo evaluate the reconstruction accuracy of brachytherapy (BT) applicators tips in vitro and in vivo in MRI-guided 192Ir-high-dose-rate (HDR)-BT of inoperable liver tumors. Materials and methodsReconstruction accuracy of plastic BT applicators, visualized by nitinol inserts, was assessed in MRI phantom measurements and in MRI 192Ir-HDR-BT treatment planning datasets of 45 patients employing CT co-registration and vector decomposition. Conspicuity, short-term dislocation, and reconstruction errors were assessed in the clinical data. The clinical effect of applicator reconstruction accuracy was determined in follow-up MRI data. ResultsApplicator reconstruction accuracy was 1.6±0.5mm in the phantom measurements. In the clinical MRI datasets applicator conspicuity was rated good/optimal in ⩾72% of cases. 16/129 applicators showed not time dependent deviation in between MRI/CT acquisition (p>0.1). Reconstruction accuracy was 5.5±2.8mm, and the average image co-registration error was 3.1±0.9mm. Vector decomposition revealed no preferred direction of reconstruction errors. In the follow-up data deviation of planned dose distribution and irradiation effect was 6.9±3.3mm matching the mean co-registration error (6.5±2.5mm; p>0.1). ConclusionApplicator reconstruction accuracy in vitro conforms to AAPM TG 56 standard. Nitinol-inserts are feasible for applicator visualization and yield good conspicuity in MRI treatment planning data. No preferred direction of reconstruction errors were found in vivo.

A Unifying Method to Derive Electron Density From MRI for Radiation Therapy Treatment Planning

MRI has a number of advantages over CT as a primary modality for patient simulation and treatment planning in radiation therapy. Due to its superior soft tissue contrast, MRI significantly improves the accuracy and reliability of target delineation. An MRI based simulation will eliminate systematic image co-registration errors, reduce cost and simplify clinical workflow, with the additional benefit of zero ionizing radiation. However, one key bottleneck problem still remains, which is the lack of electron density information in MRI. The purpose of this work is to develop a reliable method to derive electron density from MRI. We adopt a probabilistic Bayesian approach for electron density mapping based on T1-weighted MRI. The probabilistic approach allows us to explicitly recognize the various sources of uncertainties (such as deformable registration and density mapping) and incorporate them into the analysis. We utilize a head and neck patient with co-registered CT/MRI as the atlas. For each voxel, we compute two conditional probabilities or likelihood functions: (1) electron density given its MR image intensity (T1 value), and (2) electron density given its geometry or position in a reference anatomy, obtained by deformable image registration between the MRI of the test patient and atlas. The two sources of information (image intensity and spatial location) are combined into a unifying posterior probability density function, using the Bayesian formalism. The mean value of the posterior or its mode (value that corresponds to the maximum probability) gives the electron density. To evaluate the proposed method, we calculated the mean absolute HU error between the estimated and true CT, as well as area under the ROC curve for bone detection (HU > 200). The performance was compared with a global intensity approach based on T1 value and no density correction (set whole head to water). The proposed technique significantly reduced the errors in electron density estimation, with a mean absolute HU error of 117, compared with 232 for the intensity approach and 273 without density correction. For bone detection, the proposed method had an accuracy of 87%, with a sensitivity of 90% and specificity of 90% (AUC = 94%). In comparison, the AUC for bone detection is only 70% and 50% using the intensity approach and without density correction, respectively. The proposed unifying method provides accurate electron density estimation and bone detection from MRI of the head with highly heterogeneous regions. This paves the way for accurate dose calculation and generating reference images for patient setup in MRI-based treatment planning.

Quantification of rat brain SPECT with 123I-ioflupane: evaluation of different reconstruction methods and image degradation compensations using Monte Carlo simulation

SPECT studies with 123I-ioflupane facilitate the diagnosis of Parkinson’s disease (PD). The effect on quantification of image degradations has been extensively evaluated in human studies but their impact on studies of experimental PD models is still unclear. The aim of this work was to assess the effect of compensating for the degrading phenomena on the quantification of small animal SPECT studies using 123I-ioflupane. This assessment enabled us to evaluate the feasibility of quantitatively detecting small pathological changes using different reconstruction methods and levels of compensation for the image degrading phenomena. Monte Carlo simulated studies of a rat phantom were reconstructed and quantified. Compensations for point spread function (PSF), scattering, attenuation and partial volume effect were progressively included in the quantification protocol. A linear relationship was found between calculated and simulated specific uptake ratio (SUR) in all cases. In order to significantly distinguish disease stages, noise-reduction during the reconstruction process was the most relevant factor, followed by PSF compensation. The smallest detectable SUR interval was determined by biological variability rather than by image degradations or coregistration errors. The quantification methods that gave the best results allowed us to distinguish PD stages with SUR values that are as close as 0.5 using groups of six rats to represent each stage.

Robust Radial Velocity Estimation of Moving Targets Based on Adaptive Data Reconstruction and Subspace Projection Algorithm

In practice, inevitable image coregistration error and channel phase mismatch will significantly degrade the estimation performance of the target radial velocity in the ground moving target indication processing with multichannel synthetic aperture radar (SAR) systems. To overcome this problem, a new radial velocity estimation method using the subspace projection (SP) algorithm with adaptive data reconstruction is proposed in this letter. Based on the joint-pixel signal model, the Wiener weight vector is used to reconstruct the multichannel data vector of the pixel containing the moving target. Then, the SP algorithm is adopted to deal with the radial velocity estimation with the reconstructed single “snapshot” data. The validity and robustness are verified by both simulations and real SAR data experiments.

Robust differential interferometric synthetic aperture radar deformation phase estimation method based on covariance matrix information of multiple pixels

Differential interferometric synthetic aperture radar (D-InSAR) is a powerful technique to detect ground deformation, and the deformation estimation accuracy is directly affected by the D-InSAR phase accuracy. Coregistration of synthetic aperture radar (SAR) images and/or interferograms is one of the key steps in the D-InSAR flowchart, and the coregistration errors inevitable between SAR images will greatly decrease the D-InSAR phase estimation accuracy. In this study, the authors propose a robust approach to estimate the deformation phase by using the central pixel and its neighbouring pixels simultaneously, which is coupled with the signal subspace fitting or the covariance matrix fitting. The method can accomplish image auto-coregistration and suppress the D-InSAR phase noise together. Furthermore, compared with the required coregistration accuracy (no less than 1/10 pixel) for the conventional D-InSAR processing, the required image and/or interferogram coregistration error of the proposed method can reach 1 pixel, which greatly releases the district limitation on coregistration in D-InSAR processing. The performance is investigated by using simulated data and real ENVISAT ASAR data.

Cone-beam Computed Tomography-guided Stereotactic Liver Punctures: A Phantom Study

Images from computed tomography (CT), combined with navigation systems, improve the outcomes of local thermal therapies that are dependent on accurate probe placement. Although the usage of CT is desired, its availability for time-consuming radiological interventions is limited. Alternatively, three-dimensional images from C-arm cone-beam CT (CBCT) can be used. The goal of this study was to evaluate the accuracy of navigated CBCT-guided needle punctures, controlled with CT scans. Five series of five navigated punctures were performed on a nonrigid phantom using a liver specific navigation system and CBCT volumetric dataset for planning and navigation. To mimic targets, five titanium screws were fixed to the phantom. Target positioning accuracy (TPECBCT) was computed from control CT scans and divided into lateral and longitudinal components. Additionally, CBCT-CT guidance accuracy was deducted by performing CBCT-to-CT image coregistration and measuring TPECBCT-CT from fused datasets. Image coregistration was evaluated using fiducial registration error (FRECBCT-CT) and target registration error (TRECBCT-CT). Positioning accuracies in lateral directions pertaining to CBCT (TPECBCT = 2.1 ± 1.0 mm) were found to be better to those achieved from previous study using CT (TPECT = 2.3 ± 1.3 mm). Image coregistration error was 0.3 ± 0.1 mm, resulting in an average TRE of 2.1 ± 0.7 mm (N = 5 targets) and average Euclidean TPECBCT-CT of 3.1 ± 1.3 mm. Stereotactic needle punctures might be planned and performed on volumetric CBCT images and controlled with multidetector CT with positioning accuracy higher or similar to those performed using CT scanners.

Ground Moving Target Detection and Velocity Estimation Based on Spatial Multilook Processing for Multichannel Airborne SAR

A robust method is addressed here for ground moving target detection and velocity estimation. This method is proposed by forming multilook Doppler beam sharpening (DBS) images with different illuminated angles which are divided by slow time segment in the azimuth direction. Compared with only a single looking angle, the detection probability with multiple looking angles can be improved under the same condition. For the clutter rejection procedure, we describe an approach which utilizes the fast time information and therefore has the robustness to image coregisrtration error. After clutter rejection and target detection, a new series of single-look and multilook velocity estimation approaches are developed in the presence of channel mismatch and image coregistration error. The robustness and practicability of the proposed algorithm are verified by processing the measured data of the experiment with three-aperture airborne radar.

Clinical Application of In-Room Positron Emission Tomography for In Vivo Treatment Monitoring in Proton Radiation Therapy

The purpose of this study is to evaluate the potential of using in-room positron emission tomography (PET) for treatment verification in proton therapy and for deriving suitable PET scan times. Nine patients undergoing passive scattering proton therapy underwent scanning immediately after treatment with an in-room PET scanner. The scanner was positioned next to the treatment head after treatment. The Monte Carlo (MC) method was used to reproduce PET activities for each patient. To assess the proton beam range uncertainty, we designed a novel concept in which the measured PET activity surface distal to the target at the end of range was compared with MC predictions. The repositioning of patients for the PET scan took, on average, approximately 2 minutes. The PET images were reconstructed considering varying scan times to test the scan time dependency of the method. The measured PET images show overall good spatial correlations with MC predictions. Some discrepancies could be attributed to uncertainties in the local elemental composition and biological washout. For 8 patients treated with a single field, the average range differences between PET measurements and computed tomography (CT) image-based MC results were <5 mm (<3 mm for 6 of 8 patients) and root-mean-square deviations were 4 to 11 mm with PET-CT image co-registration errors of approximately 2 mm. Our results also show that a short-length PET scan of 5 minutes can yield results similar to those of a 20-minute PET scan. Our first clinical trials in 9 patients using an in-room PET system demonstrated its potential for invivo treatment monitoring in proton therapy. For a quantitative range prediction with arbitrary shape of target volume, we suggest using the distal PET activity surface.

T1/T2*-weighted MRI provides clinically relevant pseudo-CT density data for the pelvic bones in MRI-only based radiotherapy treatment planning

Background and purpose. In radiotherapy (RT), target soft tissues are best defined on MR images. In several cases, CT imaging is needed only for dose calculation and generation of digitally reconstructed radiographs (DRRs). Image co-registration errors between MRI and CT can be avoided by using MRI-only based treatment planning, especially in the pelvis. Since electron density information can not be directly derived from the MRI, a method is needed to convert MRI data into CT like data. We investigated whether there is a relationship between MRI intensity and Hounsfield unit (HU) values for the pelvic bones. The aim was to generate a method to convert bone MRI intensity into HU data surrogate for RT treatment planning. Material and methods. The HU conversion model was generated for 10 randomly chosen prostate cancer patients and independent validation was performed in another 10 patients. Data consisted of 800 image voxels chosen within the pelvic bones in both T1/T2*-weighted gradient echo and CT images. Relation between MRI intensity and electron density was derived from calibrated HU-values. The proposed method was tested by constructing five “pseudo”-CT series. Results. We found that the MRI intensity is related to the HU value within a HU range from 0 to 1400 within the pelvic bones. The mean prediction error of the conversion model was 135 HU. Dose calculation based on the pseudo-CT images was accurate and the generated DRRs were of good quality. Conclusions. The proposed method enables generation of clinically relevant pseudo-CT data for the pelvic bones from one MRI series. It is simpler than previously reported approaches which require either acquisition of several MRI series or T2* maps with special imaging sequences. The method can be applied with commercial clinical image processing software. The application requires segmentation of the bones in the MR images.

On robust beamforming for InSAR phase estimation

In InSAR signal processing, the performance of the interferometric phase estimation degrades in the case of image coregistration errors and spatial steering vector errors. In this letter, a method of interferometric phase estimation based on robust beamforming is introduced. The proposed method can take advantage of the optimal weighted joint data (OWJD) vector and of the robust adaptive beamformer to auto-coregister the SAR images and mitigate the effect of the spatial steering error on the interferometric phase estimation. Theoretical analysis and experimental results show that the method can provide accurate estimation of the interferometric phase in the presence of the coregistration error and spatial steering vector error. Numerical results on the simulated and real data demonstrate the effectiveness and robustness of the proposed method.

Robust moving targets detection and velocity estimation using multi-channel and multi-look SAR images

For the sake of the robustness to ground moving targets indication (GMTI) for synthetic aperture radar (SAR) in heterogeneous environment, a new approach using multi-channel and multi-look SAR images is presented. This approach has been shown to attain improved suppression of monostatic clutter over its counterpart ( the multi-channel and single-look algorithm). In this approach, the joint data vector is utilized which has robustness to image coregistration and deleterious system errors in practice. Consequently, a series of radial velocity estimation approaches are presented. The estimation accuracy can be improved by the multi-look algorithms. Experimental airborne SAR data illustrates the effectiveness of the proposed GMTI schemes. It is shown that, compared with conventional single-look algorithms, the multi-look approach can obtain better clutter cancellation performance and more accurate velocity estimation.