Weidong Yu

SAR Image Despeckling Using a Space-Domain Filter With Alterable Window

In this letter, a modified bilateral filter suitable for synthetic aperture radar (SAR) image despeckling, named space-domain filter with alterable window (SFAW), is proposed. SFAW features geometric adaptivity, in both spatial and similarity functions, respectively driven by the following: 1) the local coefficient of variation ( CV) and 2) the joint probability density function model of two pixels having the same reflectivity. Tests on synthetic and real SAR images show that SFAW notably smoothes speckle with unperceivable detail blurring and achieves better performances than other related methods.

Echo Separation in Multidimensional Waveform Encoding SAR Remote Sensing Using an Advanced Null-Steering Beamformer

In order to reap the potential benefits that waveform diversity can provide for spaceborne synthetic aperture radar remote sensing, echoes from different subpulses constituting a complete transmit waveform should be effectively separated at first. This paper presents a new separation approach implemented by an advanced null-steering beamformer on satellite. Compared with common null-steering beamforming, our approach will take into account the characteristics of echo signal from the scene and accordingly embed the finite-impulse response (FIR) filtering process into the modified null-steering beamformer to deal with the issue of pulse extension. In this paper, echo signals generated by multidimensional encoded waveform will be analyzed in detail; based on this analysis, FIR filter and the new null-steering beamformer are derived. Simulation results show that much better separation performance can be obtained by our approach than by conventional null-steering beamforming.

Integrated Denoising and Unwrapping of InSAR Phase Based on Markov Random Fields

In the traditional processing flow of interferometric synthetic aperture radar (SAR) technique, the processing of phase is conducted via two separated and successive steps, i.e., phase denoising and phase unwrapping. That is to say, first, wrapped phases without noise are generated, and then, the true phases without 2π-ambiguities are reconstructed (here and in the rest of this paper, true phase refers to the information-induced unwrapped phase without noise). Such separated steps will inevitably bring in extra estimation error because each step has necessary approximations and presumptions which do not always hold. On the contrary, in this paper, we treat phase denoising and unwrapping as a single problem of true phase recovery from observed ones. Following this methodology, an integrated phase denoising and unwrapping algorithm based upon Markov random fields (MRFs) is proposed. Taking a priori knowledge of interferometric phases into account, MRF is used to model the relationship between the elements in the random variable set including both true phases and their observations. After the model is built up, the energy function of this MRF is defined according to the local-independence property inferred from the MRF structure and then minimized to obtain the estimate of the true phase value. In the end of this paper, experiments on simulated and true phase data are conducted, and the comparison with several commonly used unwrapping methods is proposed to verify the efficiency of the proposed MRF algorithm.

A New Region Growing-Based Method for Road Network Extraction and Its Application on Different Resolution SAR Images

Road network extraction plays an irreplaceable role in the applications of synthetic aperture radar (SAR) images. In this paper, we propose a new method based on the region growing to quickly extract the road network, which is suitable for different resolution SAR images. First, a weighted ratio line detector (W-RLD) is proposed to extract road features. Then, an automatic road seeds extraction method, which merges the ratio and direction information, is utilized to improve the quality of the extracted road seeds. Finally, the region growing concept is adopted to construct the road network, and a fast parameter selection procedure is presented for adaptively adjusting growing parameters. In experiments, four kinds of SAR images are used to assess the performance of the proposed method, including Envisat ASAR (30 m), HJ-1-C (5 m), TerraSAR-X (3 m), and airborne C-band data (0.5 m). Both visual and quantitative evaluation results show the adaptability and efficiency of the proposed approach.

Study on the Processing Scheme for Space–Time Waveform Encoding SAR System Based on Two-Dimensional Digital Beamforming

The combination of space-time waveform encoding and digital beamforming (DBF) has been proposed as a novel concept to improve the performance of synthetic aperture radar (SAR) systems in the future. In this paper, we research one such new operational mode to reduce azimuth ambiguity in high-resolution wide-swath SAR image and present the processing scheme based on 2-D DBF for this mode. The main procedures and techniques are described in detail, and a complete mathematical derivation of the scheme is given; furthermore, a sample SAR system is provided, from which numerical simulation results are obtained to justify our derivations. In addition, an in-depth analysis of system performance associated with this mode, from the perspectives of both azimuth ambiguity-to-signal ratio (ASR) (AASR) and range ASR (RASR), is carried out. It is shown that a meaningful improvement of AASR can be attained at a small cost of raised RASR level, as compared with the performance of single-input multiple-output SAR.

Comparison of Nonnegative Eigenvalue Decompositions With and Without Reflection Symmetry Assumptions

Nonnegative eigenvalue decomposition (NNED), which insists and guarantees that each decomposed scattering component corresponds to a physically realizable scatterer, is powerful for polarimetric synthetic aperture radar (SAR) images analysis. Previous NNED is mainly illustrated under the reflection symmetric condition. In this paper, the coherency matrix approach is derived to implement the NNED for the nonreflection symmetry scattering case. We explicitly show the diversifications of the decomposition results between NNED with and without reflection symmetry assumptions, and quantitatively analyze the differences between them using the E-SAR polarimetric data acquired over the Oberpfaffenhofen area in Germany.

Interferometric Phase Denoising by Pyramid Nonlocal Means Filter

Interferometric phase denoising is a crucial step of interferometric synthetic aperture radar processing flow because it has significant influence on the following steps. Traditional interferometric phase denoising algorithms have a similar drawback that they will wipe off some texture details in phase images while denoising. Nonlocal (NL) means filter, in contrast, can reach a balance between denoising and texture preserving because it utilizes the feature of recursive structures in the whole image. Taking the characteristics of interferometric phase image into consideration, this letter proposes a modified NL means filter algorithm for phase denoising. Moreover, in order to preserve texture to the biggest extent, an iterative algorithm is invented. In the end, experiments on synthetic and real data validate that this algorithm outperforms other traditional denoising methods.

Sharpness-Based Autofocusing for Stripmap SAR Using an Adaptive-Order Polynomial Model

A novel autofocusing technique is developed for image from stripmap-mode synthetic aperture radar (SAR) data. The approach is based on maximizing the image sharpness function that induces the solution to maximum-posterior estimation. In this letter, closed-form expressions are derived for the gradients of the sharpness function with respect to the coefficients of the polynomial expansion, which makes the use of conjugate gradient algorithm available. Additionally, we also design a modified adaptive-order searching strategy, and it helps to remarkably reduce the computational load while maintaining the accuracy. Real airborne SAR data experiments and comparisons demonstrate the validity and effectiveness of the proposed algorithm.

Polarimetric SAR Internal Calibration Scheme Based on T/R Module Orthogonal Phase Coding

Two important aspects of internal calibration of polarimetric synthetic aperture radar (SAR) are discussed, i.e., individual transmit/receive module (TRM) calibration and system gain calibration. The system has a general structure utilizing a phased array antenna composed of dual-channel TRMs. TRM gain and phase calibration is carried out using orthogonal phase coding (OPC). The signal of the individual TRM is phase-encoded according to a set of orthogonal codes to be separated from the composite calibration signal. OPC uses 1 bit of a digital phase-shifter for encoding, without the need for additional encoding hardware. Performance of the method is examined. Calibration results are developed both theoretically and through simulation in case of TRM amplifier or phase-shifter failure. Zero-padding is used to eliminate calibration error of the first TRM. A crosstalk model is proposed to investigate the effect of imperfect isolation between the two polarization channels of each TRM, and a way to reduce this error is also given. At last, system path gain variation is measured utilizing the internal calibration loop. The OPC method has an accuracy of 0.2 dB for gain and better than 2deg for phase, with 10-dB signal-to-noise ratio and perfect isolation between the two polarization channels. The error due to imperfect isolation is usually small and can be ignored. The simple way to detect TRM malfunction is verified through simulation, and it is also in accordance with TerraSAR-X in-orbit calibration outcomes. The proposed OPC method is shown to be an effective way of internally calibrating TRMs of a phased array antenna.

New building signature extraction method from single very high-resolution synthetic aperture radar images based on symmetric analysis

Abstract. To monitor urban areas using a synthetic aperture radar (SAR) sensor, we propose a symmetric analysis-based building signature extraction method. Instead of using separated algorithms, a unified framework is proposed to extract both layover and shadow areas. Since these two primitives usually exhibit long strip patterns in very-high-resolution SAR images, symmetry axes are first delineated. After that, local features are extracted from both symmetry and range direction to better distinguish different primitives. Then, these local radiometric features are used to identify different categories (layover, shadow, and background) via an efficient multiclass logistic regression classifier. To discriminate individual primitives, geometric information is adopted via an improved Ramer Douglas Peucker algorithm, which also simplifies the parameters for describing these primitives. To further enhance accuracy, combinatory analysis is implemented to exclude some false detections, and then shadow areas are extended via a local region growing method. The proposed approach is tested on a 0.75-m resolution airborne C band SAR image. The experiments are carried out under both small- and large-scale scenes, and the comparative results show our method has some advantages in low-contrast target detection and false-alarm elimination.