Jian Yang

Numerical methods for solving the optimal problem of contrast enhancement

This paper proposes two effective numerical methods to solve the optimal problem of contrast enhancement for the coherent and incoherent cases, respectively. For the coherent case, the objective function of the optimal problem is transformed into a bilinear form. Then a numerical method is presented by using the idea of the Sequential Unconstrained Minimization Technique (SUMT). For the incoherent case, a cross-iterative method is proposed for solving the optimal problem of contrast enhancement, based on the formula of the optimal contrast polarization state in the matched-polarized channel. Both the proposed methods are convergent and straightforward for programming. In addition, the proposed methods can be used for the bistatic radar case because in this paper, it is unnecessary to restrict the symmetry of the scattering matrix and the Kennaugh matrix. For showing the effectiveness of the proposed methods, the authors give three examples. The results of the calculation are completely identical with other papers, showing the validity of the proposed methods. In these three examples, if the receiving polarization state is independent of the transmittings, the power ratios may be much larger than those in the cases of the co-polarized, the cross-polarized, and the matched-polarized channels, showing the importance of the considered models. In addition, the computation costs are estimated by these illustrative examples, illustrating that the proposed methods are very effective.

Speckle filtering in polarimetric SAR data based on the subspace decomposition

In this paper, a new approach to speckle filtering of synthetic aperture radar (SAR) data is presented. We define a parameter space consisting of two orthogonal subspaces-the signal subspace and the noise subspace. Then, the full polarimetric information from the signal subspace is obtained after speckle filtering. In this way, edges of different kinds of targets are preserved. The effectiveness of this method is demonstrated using the National Aeronautics and Space Administration Jet Propulsion Laboratory airborne L-band polarimetric SAR data.

Three-Component Power Decomposition for Polarimetric SAR Data Based on Adaptive Volume Scatter Modeling

In this paper, the three-component power decomposition for polarimetric SAR (PolSAR) data with an adaptive volume scattering model is proposed. The volume scattering model is assumed to be reflection-symmetric but parameterized. For each image pixel, the decomposition first starts with determining the adaptive parameter based on matrix similarity metric. Then, a respective scattering power component is retrieved with the established procedure. It has been shown that the proposed method leads to complete elimination of negative powers as the result of the adaptive volume scattering model. Experiments with the PolSAR data from both the NASA/JPL (National Aeronautics and Space Administration/Jet Propulsion Laboratory) Airborne SAR (AIRSAR) and the JAXA (Japan Aerospace Exploration Agency) ALOS-PALSAR also demonstrate that the proposed method not only obtains similar/better results in vegetated areas as compared to the existing Freeman-Durden decomposition but helps to improve discrimination of the urban regions.

Polarimetric SAR image classification by using generalized optimization of polarimetric contrast enhancement

In this letter, a generalized optimization of polarimetric contrast enhancement (GOPCE) is employed for supervised polarimetric synthetic aperture radar (SAR) image classification. The GOPCE is the extension of optimization of polarimetric contrast enhancement (OPCE), and it includes three optimal coefficients associated with the Cloude entropy and two special similarity parameters in addition to the optimal polarization states. Using the GOPCE, the authors propose an approach to supervised classification. For comparison, the authors also use the maximum likelihood (ML) classifier for classification, based on the complex Wishart distribution. The classification results of a NASA/JPL AIRSAR L‐band image over San Francisco demonstrate the effectiveness of the proposed approach.

The characteristic polarization states and the equi-power curves

Characteristic polarization state theory is restudied for the symmetric coherent Sinclair scattering matrix case. First, the geometric relations of the characteristic polarization states on the Poincare sphere are derived. Based on these relations, simple formulas are given for all of the characteristic polarization states of this Sinclair matrix in Stokes vector form. From the formulation, it is clear that the CO-POL Nulls are fundamental characteristic polarization states for the symmetric coherent Sinclair scattering matrix case, in that the others can straightforwardly be obtained from the Stokes vectors of the CO-POL Nulls. For further study of the characteristic polarization state and the distribution of the received powers on the Poincare sphere, the authors introduce the concept of the equi-power curve. It is defined as the curve on the Poincare sphere on which the received powers in some defined channel have the same value. They deal with the characteristics of the equi-power curves for various special cases. In addition, they show how the characteristic polarization states are generated by the equi-power curves. It is demonstrated that the characteristic polarization states can usually be regarded as the points of contact of the Poincare sphere and a conicoid representing a power-related quadratic form. This leads to a new method to introduce the characteristic polarization states.

Detection of Roads in SAR Images using Particle Filter

A novel method is presented to detect roads in synthetic aperture radar (SAR) images. A multi-segmented poly-line model is introduced to provide a more accurate description of the road as well as to ensure the road curves smoothness in the model level. We then solve the road detection problem using the Bayesian tracking theory, where the particle filtering algorithm is adopted to provide a simple and consistent framework. The effectiveness and robustness of the proposed method is demonstrated by experimental results.

Development of target null theory

In a co- or cross-polarized channel, the polarization states of the transmitting and receiving antennas are the same or orthogonal, and the corresponding target nulls (i.e., the co-pol nulls or x-pol nulls) are defined as the polarization states of the transmitting antenna such that the received power equals zero. However, no systematic studies have been carried out to solve the problem of the corresponding target nulls if the polarization states of the transmitting and receiving antennas are independent. In this paper, the target null theory is extended to the case of two independent polarization states. For two arbitrary independent symmetric scattering matrices, it is proved that there exists only one pair of polarization states such that both of the received powers equal zero. This polarization states pair is called the co-null of the two targets, which can easily be obtained by solving an eigenvalue problem. Based on this concept and algebraic theory, the concept of the co-null space is introduced for the symmetric scattering matrix case, and many important results are presented, e.g., the relations between the co-null and the co-pol/x-pol nulls, the properties of the co-null space, and the relation between the co-null and target decomposition. Finally, the co-null for the asymmetric scattering matrix case is studied. The concepts of the mono-co-null space and the bi-co-null space are introduced, and the relations between both spaces are presented.

Particle filter based road detection in SAR image

A novel method is proposed for the detection of roads in synthetic aperture radar (SAR) images. We first claim that roads in SAR images can be extracted in a Bayesian tracking framework. Then the particle filter is employed for the tracking implementation, where local linear feature and global contextual knowledge are incorporated into the filtering procedure. The effectiveness of the proposed method is demonstrated by the experimental detection results of single road as well as road network.

Similarity Parameter in Polarimetric SAR Interferometry

The similarity parameter is used to measure the similarity between two different scattering matrixes. In this paper, the similarity parameter is introduced into polarimetric SAR Interferometry (Pol-In-SAR). For two scattering matrixes of every scattering cell, the similarity parameter can be used to register the pairs of images received by two flights and fuse the polarimetric information to enhance the coherence and reduce the phase noise. The quality of the interferometric phase is improved remarkably and the number of residues is reduced sufficiently. This is helpful in generating an accurate digital elevation model (DEM)

Applications of Polarimetric SAR

1Department of Electronic Engineering, Tsinghua University, Beijing 100084, China 2Department of Information Engineering, Niigata University, Niigata 950-2181, Japan 3Computational Physics Inc., Springfield, VA 22151-2110, USA 4Canada Centre for Remote Sensing, Natural Resources Canada, 588 Booth Street, Ottawa, ON, Canada K1A OY7 5UIC-ECE Communications, Sensing & Navigation Laboratory, University of Illinois at Chicago, 900 W. Taylor Street, SEL (607) W-4210, M/C 154, Chicago, IL 60607, USA