Hongjun Cai

Multiple-Component Scattering Model for Polarimetric SAR Image Decomposition

A multiple-component scattering model (MCSM) is proposed to decompose polarimetric synthetic aperture radar (PolSAR) images. The MCSM extends a three-component scattering model, which describes single-bounce, double-bounce, volume, helix, and wire scattering as elementary scattering mechanisms in the analysis of PolSAR images. It can be found that double-bounce, helix, and wire scattering are predominant in urban areas. These elementary scattering mechanisms correspond to the asymmetric reflection condition that the copolar and cross-polar correlations are not close to zero. The MCSM is demonstrated with a German Aerospace Center (DLR) Experimental Synthetic Aperture Radar (ESAR) L-band full-polarized image of the Oberpfaffenhofen Test Site Area (DE), Germany, which was obtained on September 30, 2000. The result of this decomposition confirmed that the proposed model is effective for analysis of buildings in urban areas.

Building Parameters Extraction From Spaceborne PolInSAR Image Using a Built-Up Area Scattering Model

Polarimetric interferometric synthetic aperture radar (SAR) is more advantageous than either polarimetric or interferometric SAR alone in extracting information from vertical object structure. Generally, for spaceborne SAR, its resolution is low and several targets may be contained in the same resolution cell. Based on the characteristics of spaceborne SAR, a built-up area scattering model (BASM), considering buildings and vegetation in the same resolution cell, is proposed to extract building parameters using L-band polarimetric interferometric SAR data. For single-baseline polarimetric interferometric SAR, the scattering component related to the natural targets could be removed from polarimetric coherency matrices and polarimetric interferometric matrix. Then building parameters could be estimated using the BASM after detecting built-up areas. The BASM is validated using ALOS PALSAR L-band polarimetric interferometric SAR data and experimental results indicate that building parameters could be effectively extracted by the proposed BASM.

Parameter inversion models based on PolInSAR images

Polarimetric SAR Interferometry (PolInSAR) can be used for parameter inversion of ground. An appropriate model is of most importance for parameters inversion. This paper derives a series of random scattering model that can be used for parameter inversion of forestry area, such as RV, RVoG, OV and OVoG. These models can reveal the characteristics of forestry area as well as establish the relationship between observed data and parameters of test area. Finally, these scattering models are compared.

Target detection based on L- and C-band PolSAR data

For SAR signals with different frequency, the scattering characteristic of objects is generally different. Hence, more information can be obtained with multi-frequency SAR. It is easier to detect different targets by using multi-frequency PolSAR data, and even for the man-made targets in forest area. To effectively detect natural and man-made targets, the scattering components of different scattering mechanisms obtained from L- and C-band PolSAR data by using polarimetric target decomposition could be combined. The experiment result indicates that different targets can be detected by using multi-frequency PolSAR data.

Model of Man-made Target beneath Foliage Using PolInSAR

Polarimetric SAR Interferometry can be used for parameter inversion of ground. An appropriate model is of most importance for parameters inversion. This paper derives a series of scattering model that can be used for parameter inversion of forestry area, such as RV, RVoG, OV and OVoG. These models can reveal the characteristics of forestry area as well as establish the relationship between observed data and parameters of test area. Finally, a multi-layer random scattering model that can be used for parameter inversion of forestry area containing a man-made target is derived. An improved model is examined by the simulated data of the single- baseline polarimetric SAR interferometry. The result turns out that the improved RVoG scattering model is correct.

Building parameters extraction from PolInSAR image using hybrid method

PolInSAR is more advantageous than either polarimetric or interferometric SAR alone in extracting information of vertical object structure. Building parameters could be extracted from PolInSAR data based on scattering model by using nonlinear inversion algorithms. However, inversion results are subject to initialization parameters. To overcome this problem, ESPRIT could be introduced combined with scattering model. In this paper, a method based on polarimetric target decomposition and polarimetric correlation coefficient is used to detect building areas and then building parameters are estimated using a hybrid scattering model and ESPRIT algorithm. Experimental results indicate that building parameters could be effectively extracted by the proposed method.

Ground topography estimation over forests using PolInSAR image by means of coherence set

Polarimetric SAR Interferometry (PolInSAR) provides a useful tool for forestry parameters retrieval at L-band. Three-stage inversion process is a traditional way to retrieve these parameters, but it has several shortcomings while ground phase estimation is processed. In order to overcome these shortcomings, an algorithm of ground topography estimation using PolInSAR image by means of coherence set is proposed in this paper. The coherence region derived from numerical range is combined with RVoG model to obtain accurate estimation of ground topography. Simulated image and SIR-C image of forestry area are used to validate the proposed algorithm.

Target detection based on eigen-decomposition using PolInSAR data

In this paper, an eigenvalue decomposition method of the simplified polarimetric interferometric coherency matrix (SPICM) is proposed for polarimetric SAR interferometry (PolInSAR) data analysis. Firstly, the definition of the SPICM is given. Then, the characteristics of this matrix are analyzed and connected with the polarimetric scattering mechanism of targets and clutter objects. Further more the eigenvalue decomposition method is described in detail. The polarimetric scattering vectors can be converted to optimal states, in which the optimal coherence between the signals in same polarimetric states of two antennas can be obtained. Finally, the method is verified with E-SAR data. The results show that the proposed method is promising in classification or recognition of man-made targets such as buildings.