New applications of nonlinear chirp scaling (NLCS) have been found in processing monostatic and bistatic SAR data acquired in the strip-map mode. When the effects in impulse response broadenings due to quadratic range migration are negligible for short radar wavelengths, a time domain range cell migration correction (RCMC) can be applied. After the correction, the FM rates of targets confined in a range gate will differ from each other. A nonlinear chirp perturbation function can be applied to each range gate to equalize the targets' FM rates before azimuth compression. This algorithm is analyzed in the paper and is supported by point target simulation experiments.

New applications of nonlinear chirp scaling in SAR data processing

In this paper, an extended nonlinear chirp scaling (ENLCS) algorithm for focusing synthetic aperture radar data acquired at high resolution and highly squint angle is proposed. The whole processing of the ENLCS consists of the following three steps. First, a linear range walk correction is used to remove the linear component of target range cell migration (RCM) and to mitigate the range-azimuth coupling of the 2-D spectrum. Second, a bulk second range compression (SRC) is performed in the 2-D frequency domain for compensating the residual RCM, SRC term, and higher order range-azimuth coupling terms. Third, a modified azimuth NLCS (ANLCS) operation is applied to equalize the azimuth frequency modulation rate for azimuth compression. By adopting higher order approximation processing and by properly selecting the scaling coefficients, the proposed modified ANLCS operation has better accuracy and little image misregistration. The overall focusing procedure of the ENLCS algorithm only involves fast Fourier transform and complex multiplication, which means easier implementation and higher efficiency. The experimental results with simulated data prove the effectiveness of the proposed algorithm.

Extended Nonlinear Chirp Scaling Algorithm for High-Resolution Highly Squint SAR Data Focusing

A novel speckle noise reduction algorithm based on a combination of Anisotropic Diffusion (AD) filtering and Interval Type-II fuzzy system was developed for reducing speckle noise in Optical Coherence Tomography (OCT) images. Unlike regular AD, the Interval Type-II fuzzy based AD algorithm considers the uncertainty in the calculated diffusion coefficient and appropriate adjustments to the coefficient are made. The new algorithm offers flexibility in optimizing the trade-off between the two image metrics: signal-to-noise (SNR) and Edginess, which are directly related to the structure of the imaged object. Application of the Interval Type-II fuzzy AD algorithm to OCT tomograms acquired in-vivo from a human finger tip and human retina show reduction in the speckle noise with very little edge blurring and about 13 dB and 7 dB image SNR improvement respectively. Comparison with Wiener, Adaptive Lee and regular Anisotropic Diffusion filters, applied to the same images, demonstrates the superior performance of the fuzzy Type-II AD algorithm in terms image SNR and edge preservation metrics improvement.

Interval type-II fuzzy anisotropic diffusion algorithm for speckle noise reduction in optical coherence tomography images.

A novel speckle reduction technique based on soft thresholding of wavelet coefficients using interval type II fuzzy system was developed for reducing speckle noise in Optical Coherence Tomography images. The proposed algorithm is an extension of a recently published method for filtering additive Gaussian noise by use of type I fuzzy system. Unlike type I, interval type II fuzzy based thresholding filter considers the uncertainty in the calculated threshold and the wavelet coefficient is adjusted based on this uncertainty. A single parameter controls the signal-to-noise (SNR) improvement. Application of this novel algorithm to optical coherence tomograms acquired in-vivo from a human finger tip show reduction in the speckle noise with little edge blurring and image SNR improvement of about 10dB. Comparison with adaptive Wiener and adaptive Lee filters, applied to the same image, demonstrated the superior performance of the fuzzy type II algorithm in terms of image metrics improvement.

Speckle noise reduction algorithm for optical coherence tomography based on interval type II fuzzy set

An extended two-step focusing approach (ETSFA) for processing the squinted spotlight synthetic aperture radar (SAR) data is proposed in this paper. The effect of the squint angle on the azimuth coarse focusing is analyzed and discussed. Based on the analysis results, a nonlinear shift preprocessing method is introduced, which can completely remove the squint angle impacts on the azimuth coarse focusing. Furthermore, based on the squinted spotlight SAR imaging model and the preprocessed echo data, derivations of the azimuth coarse focusing with the deramping-based technique and precise focusing with the modified Stolt-based technique are carried out in detail. Moreover, to produce an acceptable image by the proposed ETSFA for high-resolution (<; 3 m) squinted spotlight SAR with large scene, a subscene processing method is introduced. The experimental results on simulated data prove the validity of the whole analysis and the proposed methods.

Extended Two-Step Focusing Approach for Squinted Spotlight SAR Imaging

High-squint angle synthetic aperture radar (SAR) imaging has applications in specific situations such as providing looking angle-dependent scattering information and revisiting an area of interest. It is very difficult for current SAR-imaging algorithms to be used directly in high-squint configurations. This is due to the large range-cell migration and the nonnegligible cubic-phase term in the signal. In this paper, an improved algorithm called the time-varying step-transform algorithm is proposed. It uses different chirp rates to deramp the signal in different subapertures to alleviate the range-focus problem. The interval between subapertures is also varied according to the deramping chirp rate. The nonnegligible cubic-phase term is included in the focusing procedure. The performance of the proposed algorithm in high-squint-angle mode is excellent. By reducing the subaperture length as the squint angle is increased, the performance of the algorithm is almost independent of squint angle. The proposed algorithm is also flexible for multilook processing and motion-error compensation.

Time-varying step-transform algorithm for high squint SAR imaging

Speckle noise in synthetic aperture radar (SAR) images reduces target feature detection. Two of the better known post-processing speckle suppression filters are the Lee (1986) and Kuan (1987) filters. The authors present modifications that have been made to the Lee and Kuan filters, such that there is an improvement in speckle suppression in regions of large back-scattering variations. Improvement over the original algorithms are illustrated using SEASAT images of Flevoland (Netherlands). Quantitative measures of performance for various types simulated terrain are also presented.

https://ieeexplore.ieee.org/iel3/3772/11016/00516246.pdf

Adaptive filtering algorithms for SAR speckle reduction

Interests in slow target imaging lead us to the synthetic aperture radar (SAR) simulation and performance evaluation of the dual-channel displaced phase center antenna (DPCA) clutter suppression technique for SAR processing. The Doppler chirp signature of a moving target signal is altered by the DPCA and has to be restored by a correction factor. Suppressed clutter powers in signal-to-clutter ratios (SCR) the improvement factors (IF) are measured in this numerical experiment and presented. This investigation offers a guide to the SCR requirement on the next-in-line processors such as parameter estimation.

Simulated DPCA performance for dual-channel SAR processing

In this paper, a data converting algorithm (DCA) that is capable of transforming synthetic aperture radar (SAR) data collected in strip-mapping mode to spotlight mode is presented. Spotlight image formation algorithms (IFA) such as the range migration algorithm (RMA) and the polar format algorithm can then process the converted SAR data. The principle behind the DCA is to compensate for the difference in the spectral properties of the Doppler spectrum between the two modes of SAR imaging. The compensating equation is derived from the difference of the phase of the demodulating signal equations for the two different modes. The DCA has been implemented on a SUN workstation and used to process real (DLR ESAR-2) data. The results of the simulations are presented.

A stripmap to spotlight data converting algorithm

Interferometric phase data acquired using a pair of synthetic aperture radar (SAR) sensors, need to be phase unwrapped before useful information can be extracted. This proves to be difficult because measurements tend to be corrupted by additive random phase noise. A common way to reduce random phase noise before phase unwrapping, is to make use of a coherent averaging or mean filter. Unfortunately, noise reduction is achieved at the expense of a loss in spatial resolution. The authors propose adopting the technique of using local statistics, to adaptively suppress the noise in the wrapped interferometric image. This technique has been applied to multiplicative SAR speckle noise filtering and shown to have good feature retention capabilities. However, because the local statistics (LS) filter is essentially designed for non-directional (noncircular) data, modifications have to be made before it can be applied in the wrapped-phase domain. For this purpose, an angular complement compensation technique is devised and circular variances are used. In instances when the local statistics of the signal can no longer be assumed stationary i.e. dense fringes with spacing in the order of the window size, the non-stationary phase signal is first removed using a two-dimensional multiple-signal classification (MUSIC) algorithm. The result is a noise-filtered wrapped interferogram with retained spatial resolution, independent of the density of the fringes. Quantitative evaluation using synthetic fringe patterns and computed residues, reveals that the noise reduction capabilities of the proposed LS filter is comparable to the mean filter.

C.B. Zhang

Papers

Time-varying step-transform algorithm for high squint SAR imaging

Adaptive filtering algorithms for SAR speckle reduction

Simulated DPCA performance for dual-channel SAR processing

A stripmap to spotlight data converting algorithm

Phase noise filter for interferometric SAR