Tao Xiong

Wavenumber-Domain Autofocusing for Highly Squinted UAV SAR Imagery

Being capable of enhancing the flexibility and observing ability of synthetic aperture radar (SAR), squint mode is one of the most essential operating modes in SAR applications. However, processing of highly squinted SAR data is usually a challenging task attributed to the spatial-variant range cell migration over a long aperture. The Omega-k algorithm is generally accepted as an ideal solution to this problem. In this paper, we focus on using the wavenumber-domain approach for highly squinted unmanned aerial vehicle (UAV) SAR imagery. A squinted phase gradient autofocus (SPGA) algorithm is proposed to overcome the severe motion errors, including phase and nonsystematic errors. Herein, the inconsistence of phase error and range error in the squinted wavenumber-domain imaging is first presented, which reveals that even the motion error introduces very small phase error, it causes considerable range error due to the Stolt mapping. Based on this, two schemes of SPGA-based motion compensation are developed according to the severity of motion error. By adapting the advantages of weighted phase gradient autofocus and quality phase gradient autofocus, the robustness of SPGA is ensured. Real measured data sets are used to validate the proposed approach for highly squinted UAV-SAR imagery.

New Applications of Omega-K Algorithm for SAR Data Processing Using Effective Wavelength at High Squint

A novel method to obtain the formulations of the return signals in the 2-D frequency domain for both monostatic and bistatic synthetic aperture radar (SAR) is proposed. In this study, the squinted effective wavelength (SEW) is firstly used, so that the 2-D spectrums can be derived directly from their imaging geometries. For monostatic SAR (MoSAR), the 2-D spectrum is obtained without a lengthy derivation by using the widely-used principle of stationary phase. For the bistatic SAR (BiSAR), based on the assumption that the azimuth time durations of the transmitter and the receiver are the same, two individual SEWs can be derived, as well as the 2-D spectrums that are both concise and of high accuracy. Then, two modified omega-K algorithms based on the two 2-D spectrums are developed to process MoSAR and translational-invariant BiSAR data. Furthermore, as important processing steps of the proposed omega-K algorithms, a modified reference function multiplication and a modified Stolt mapping, which are much more suitable for SAR data processing than the conventional ones, are proposed. Simulations under a wide range of MoSAR and BiSAR system parameters are conducted. Finally, the proposed algorithms are applied to the analysis of acquired data and the results confirm not only the validity of the derived 2-D spectrums for both MoSAR and BiSAR but also the effectiveness of the proposed method.

A Variable-Decoupling- and MSR-Based Imaging Algorithm for a SAR of Curvilinear Orbit

For a synthetic aperture radar (SAR) onboard a platform with a rectilinear track, the range history of a point target can be accurately expressed hyperbolically. The track can be curvilinear for a maneuverable SAR platform. The hyperbolic equation becomes inadequate, and an expression with high-order terms is needed. Using the method of series reversion, we derived the 2-D spectrum for the return signal of the curvilinear SAR. There were five independent variables in the spectrum, but available imaging algorithms could only handle three in the focusing using the spectrum. Thus, a variable-decoupling method was developed to reparameterize the initial spectrum so that only three variables remained. After the incorporation of the decoupling method into the chirp-scaling algorithm, simulations of the SAR with a curvilinear track were studied. Promising results were obtained.

Minimum-Entropy-Based Autofocus Algorithm for SAR Data Using Chebyshev Approximation and Method of Series Reversion, and Its Implementation in a Data Processor

A novel autofocus method for synthetic aperture radar (SAR) image is studied. Based on a quadratic model for the phase error within each sub-area (narrow strip × sub-aperture) after a wide range swath is subdivided into narrow range strips and long azimuth aperture into sub-apertures, an objective function for estimation of the error is derived through the principle of minimum entropy. There is only one unknown variable in the function. With the Chebyshev approximation, the function is approximated as a polynomial, and the unknown is then solved using the method of series reversion. Curve-fitting methods are applied to estimate phase error for an entire scene of the full-swath by full-aperture. Through simulations, the proposed method is applied to restore the defocused SAR imagery that is well focused. The restored and original images are almost identical qualitatively and quantitatively. Next, the method is implemented into an existing SAR data processor. Two sets of SAR raw data at X- and Ku-bands are processed and two images are formed. Well-focused and high-resolution images from plain and rugged terrain are obtained even without the use of ancillary attitude data of the airborne SAR platform. Thus, the studied method is verified.

Supervised Band Selection Using Local Spatial Information for Hyperspectral Image

In order to alleviate the subsequent computation burden and storage requirement, band selection has been widely adopted to reduce the dimensionality of hyperspectral images, and the current methods mainly consist of the supervised and the unsupervised. Although these supervised methods have better performance, those unsupervised methods dominate the band selection field. In this letter, based on the unique properties of hyperspectral images, we propose a very simple but effective supervised band selection algorithm based on the local spatial information of the hyperspectral image and wrapper method. By using both the information of labeled and unlabeled pixels of the hyperspectral image, our proposed algorithm consistently outperforms the classical wrapper method. We use five widely used real hyperspectral data to demonstrate the effectiveness of our proposed algorithms. We also analyze the relationship between our band selection algorithm and the well-known Markov random field classifier.

A novel dynamic rough subspace based selective ensemble

Ensemble learning has been a hot topic in machine learning due to its successful utilization in many applications. Rough set theory has been proved to be an excellent mathematical tool for dimension reduction. In this paper, based on rough set, a novel framework for ensemble is proposed. In our proposed framework, the relationship among attributes in rough subspace is first considered, and the maximum dependency degree of attribute is first employed to effectively reduce the searching space of reducts and augment the diversity of selected reducts. In addition, in order to choose an appropriate reduct from the dynamic reduct searching space, an assessment function which can balance the accuracy and diversity is utilized. At last, a new method, i.e., Dynamic Rough Subspace based Selective Ensemble (DRSSE), which is derived from our framework is given. By repeatedly changing the searching space of reducts and selecting the next reduct from the changed searching space, DRSSE finally trains an ensemble system with these selected reducts. Compared with several available ensemble methods, experimental results with several datasets demonstrate that DRSSE can lead to a comparative or even better performance. A new framework for rough set ensemble and algorithm DRSSE is proposed.Dynamic searching space is used to increase the diversity of rough subspaces.The relationship among attributes is considered to reduce the searching space.Consider the accuracy and diversity of base classifiers in an ensemble system.

Semisupervised Feature Extraction With Neighborhood Constraints for Polarimetric SAR Classification

The supervised feature extraction methods have a relative high performance since the discriminating information of classes is introduced from large quantities of labeled samples. However, it is labor intensive to obtain labeled samples for terrain classification. In this paper, in order to reduce the cost of labeled samples, a novel semisupervised algorithm with neighborhood constraints (SNC) is proposed for polarimetric synthetic aperture radar (PolSAR) feature extraction and terrain classification. A number of PolSAR features of each pixel and its neighbors are used to construct a spatial group, which can represent the central pixel and weaken the influence of speckle noise. Then, with the class information from a few of pixels and the neighborhood constraints, an objective function is designed for the estimation of a nonlinear low-dimensional space. Finally, the spatial groups in the original high-dimensional space are projected to this low-dimensional space, and a low-dimensional feature set is obtained. The redundancy among the features is reduced. Additionally, unlike the conventional semisupervised algorithms, because the local spatial relation of PolSAR image is utilized, the extracted features not only are discriminating but also preserve the structure of the PolSAR data, which can enhance the classification accuracy. Experiments using the extracted features for classification are performed on both the synthesized PolSAR and real PolSAR data which are from the AIRSAR, RADARSAT-2, and EMISAR. Quantitative results indicate that SNC improves the separability of features and is superior to state-of-the-art feature extraction algorithms with a few labeled pixels.

A Resample-Based SVA Algorithm for Sidelobe Reduction of SAR/ISAR Imagery With Noninteger Nyquist Sampling Rate

A resample-based spatial variant apodization (SVA) algorithm for sidelobe reduction was studied for synthetic aperture radar (SAR) and inverse SAR (ISAR) imagery with a noninteger Nyquist sampling rate. The weighting function of every sample in the image domain was calculated with the sample and two adjacent noninteger samples. The noninteger samples were obtained by interpolation in the image domain using sinc function. With the proper selection of two noninteger samples, the monotonic property of the weighting function on each side of the sampling point was preserved. The unequivocal determination of sidelobe suppression was achieved for noninteger Nyquist sampled (NINS) SAR and ISAR imagery. In addition, the lower and upper boundaries of the weighting function under the cosine-on-pedestal condition were extended for further sidelobe suppression and main lobe sharpening. The algorithm was implemented and applied to NINS imagery that is simulated. The algorithm was then assessed for acquired SAR and ISAR images. Improved results have been qualitatively and quantitatively achieved in sidelobe suppression and main lobe sharping in comparison with an existing algorithm.

Unsupervised classification of polarimetric SAR images integrating color features

In conventional terrain classification for the polarimetric SAR (POLSAR) images, color features are rarely involved unless in one recent supervised work. Unlike that work, the color features are exploited for the unsupervised classification in this paper. Firstly, based on the polarimetric decomposition of the POLSAR data, the common color spaces, such as RGB, HSI, and CIELab are calculated. The color feature is quantitatively selected from these color spaces by introducing the color entropy. Then together with the spatial information, extended scattering power entropy and the copolarized ratio, the adaptive Mean-shift algorithm is used to segment the POLSAR image. Finally, the segments are merged according to the Wishart distance measurement. The experiments using AIRSAR L-band POLSAR data indicate that the proposed method has better discriminative ability for urban areas and for boundary preservation compared with existing works.

Using Derivatives of an Implicit Function to Obtain the Stationary Phase of the Two-Dimensional Spectrum for Bistatic SAR Imaging

There are two square-root terms in the range history of a return signal from a bistatic synthetic aperture radar (BiSAR). The transfer function for imaging in the 2-D frequency or range Doppler domain using the principle of stationary phase cannot be analytically derived. To address this problem, we approximated the stationary phase of the 2-D spectrum with an expansion of the Taylor series on the azimuth frequency and called the approximation as the derivatives of an implicit function (DIF). After algebraic manipulation, the DIF and 2-D spectrum were obtained for a generally configured BiSAR. With the DIF method, we dissolved one square-root term out of the two for an azimuth-invariant BiSAR, which is particularly advantageous in the implementation of an imaging algorithm. Then, a modified range Doppler algorithm was developed to process the BiSAR data. Promising results were obtained.