Lamei Zhang

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.

Classification of polarimetric SAR image based on support vector machine using multiple-component scattering model and texture features

The classification of polarimetric SAR image based on Multiple-Component Scattering Model (MCSM) and Support Vector Machine (SVM) is presented in this paper. MCSM is a potential decomposition method for a general condition. SVM is a popular tool for machine learning tasks involving classification, recognition, or detection. The scattering powers of single-bounce, double-bounce, volume, helix, and wire scattering components are extracted from fully polarimetric SAR images. Combining with the scattering powers of MCSM and the selected texture features from Gray-level cooccurrence matrix (GCM), SVM is used for the classification of polarimetric SAR image. We generate a validity test for the proposed method using Danish EMISAR L-band fully polarimetric data of Foulum Area (DK), Denmark. The preliminary result indicates that this method can classify most of the areas correctly.

Polarimetric Interferometric Eigenvalue Similarity Parameter and Its Application in Target Detection

Polarimetric synthetic aperture radar (SAR) interferometry (PolInSAR) combines SAR polarimetry and SAR interferometry and is much more sensitive to the distribution of orientated scatterers compared with polarimetric or interferometric data alone. The polarimetric similarity parameter is an efficient parameter to analyze target characteristics using the similarity between a target and the canonical target. In this letter, the polarimetric interferometric eigenvalue similarity parameter (PIESP) is proposed based on the similarity between two polarimetric SAR images obtained by two interferometric antennas. The PIESP is defined by the eigenvalues of two polarimetric coherence matrices in the PolInSAR system, and the eigenvalues of polarimetric coherence matrix are independent on the target orientation angle; therefore, the PIESP is rotation invariant. PolInSAR systems use two antennas to measure the same ground area with slightly different image geometry. Thus, the PIESP can be used to distinguish the target based on coherence and similarity. Then, the target detection method using the PIESP is implemented with the DLR experimental SAR L-band full polarized image of the Oberpfaffenhofen test site of Germany obtained on September 30, 2000. The results confirmed that the proposed model is accurate and effective for the detection and the analysis of buildings in urban areas.

Polarimetric SAR image classification using Multiple-Component Scattering Model and Support Vector Machine

The classification of polarimetric SAR image based on Multiple-Component Scattering Model (MCSM) and Support Vector Machine (SVM) is presented in this paper. MCSM is a potential decomposition method for a general condition. SVM is a popular tool for machine learning tasks involving classification, recognition or detection. The scattering powers of single-bounce, double-bounce, volume, helix, and wire scattering components are extracted from full polarimetric SAR images. Combined with the scattering power and the texture feature, SVM is used for the polarimetric classification. We generate a validity test for the method using EMISAR L-band full polarized data of Foulum Area (DK), Denmark. The preliminary result indicates that this method can classify most of the areas correctly.

An extended multiple-component scattering model for PolSAR images

An extended multiple-component scattering model (MCSM) is proposed for polarimetric synthetic aperture radar (PolSAR) image decomposition. The MCSM is an extension of the three-component scattering model (TCSM), and it describes single-bounce, double-bounce, volume, helix and wire scattering as elementary scattering mechanisms in the analysis of PolSAR images. The proposed MCSM is demonstrated with German Aerospace Centre (DLR) experimental SAR (ESAR) L-band fully polarized images of the Oberpfaffenhofen Test Site Area (DE), Germany. Double-bounce, helix and wire scattering are found to be predominant in urban areas and the results confirm that the MCSM is effective for analysis of buildings in urban areas. A comparison of the TCSM and its extended models is also implemented.

Improving spatial resolution for CHANG'E-1 imagery using ARSIS concept and Pulse Coupled Neural Networks

To broaden the future application of CHANGE-1 imagery, including hyperspectral imagery (low spatial resolution of 200m) and CCD imagery (relatively high spatial resolution of 120m), an ARSIS-based method for spatial-spectral fusion is proposed in this paper, which aims at combine high spatial and high spectral resolution. Firstly, ARSIS concept is employed, in which Àtrous wavelet is used to describe images at different resolutions for multiresolution analysis. Secondly, Pulse Coupled Neural Network (PCNN) is employed to search and model a relationship between the high frequencies of the images to be fused for missing information. The ARSIS method preserves the spectral content of the original image for its very definition, and Àtrous wavelet and PCNN prove to be effective means to implement it on CHANGE-1 Imagery. The experimental results demonstrate that the visual improvement and spectral fidelity of the proposed method outperform many conventional methods of image fusion.To broaden the future application of CHANGE-1 imagery, including hyperspectral imagery (low spatial resolution of 200m) and CCD imagery (relatively high spatial resolution of 120m), an ARSIS-based method for spatial-spectral fusion is proposed in this paper, which aims at combine high spatial and high spectral resolution. Firstly, ARSIS concept is employed, in which Atrous wavelet is used to describe images at different resolutions for multiresolution analysis. Secondly, Pulse Coupled Neural Network (PCNN) is employed to search and model a relationship between the high frequencies of the images to be fused for missing information. The ARSIS method preserves the spectral content of the original image for its very definition, and Atrous wavelet and PCNN prove to be effective means to implement it on CHANGE-1 Imagery. The experimental results demonstrate that the visual improvement and spectral fidelity of the proposed method outperform many conventional methods of image fusion.

A novel super-resolution method of PolSAR images based on target decomposition and polarimetric spatial correlation

The polarimetric synthetic aperture radar (PolSAR) is becoming more and more popular in remote-sensing research areas. However, due to system limitations, such as bandwidth of the signal and the physical dimension of antennas, the resolution of PolSAR images cannot be compared with those of optical remote-sensing images. Super-resolution processing of PolSAR images is usually desired for PolSAR image applications, such as image interpretation and target detection. Usually, in a PolSAR image, each resolution contains several different scattering mechanisms. If these mechanisms can be allocated to different parts within one resolution cell, details of the images can be enhanced, which that means the resolution of the images is improved. In this article, a novel super-resolution algorithm for PolSAR images is proposed, in which polarimetric target decomposition and polarimetric spatial correlation are both taken into consideration. The super-resolution method, based on polarimetric spatial correlation (SRPSC), can make full use of the polarimetric spatial correlation to allocate different scattering mechanisms of PolSAR images. The advantage of SRPSC is that the phase information can be preserved in the processed PolSAR images. The proposed methods are demonstrated with the German Aerospace Center (DLR) Experimental SAR (E-SAR) L-band full polarized images of the Oberpfaffenhofen Test Site Area in Germany, obtained on 30 September 2000. The experimental results of the SRPSC confirms the effectiveness of the proposed methods.1

Target detection based on granularity computing of quotient space theory using SAR image

Target detection is a hot topic and key technique of SAR image interpretation. There are many detection methods, such as CFAR detector and Extended Fractal (EF) feature detector. In order to overcome their shortcomings and combine their merits at the same time, the combination of some different detection methods need be implemented. Granularity computing is just an approach that solves the problem at different granularity space due to different principles. Therefore, SAR image target detection based on granularity synthetic algorithm of quotient space theory is proposed in this paper. Firstly, CFAR detector and EF feature detection method are performed to generate different detection results as coarse granularity spaces. Then combine the different quotient spaces and construct the fine granularity space by using granularity synthesis algorithm. Finally, obtain the final target detection result. The experimental result of RADARSAT-I C band SAR image proves that the proposed algorithm is effective.

Building detection based on Polarimetric Interferometric Eigenvalue Similarity Parameter

Polarimetric SAR interferometry (PolInSAR) combines SAR polarimetry and SAR interferometry and is much more sensitive to the distribution of orientated scatterers comparing to polarimetric or interferometric data alone. Feature extraction and target detection using Polarimetric SAR interferometry (PolInSAR) images are hot issues of SAR image interpretation and application with much theoretical and applicable significance. Polarimetric Similarity Parameter is an efficient parameter to analyze target characteristics using the similarity between a target and the canonical target. In this paper, the Polarimetric Interferometric Eigenvalue Similarity Parameter (PIESP) is proposed based on the similarity between two polarimetric SAR images obtained by two interferometric antennas to analyze target characteristics. PIESP is defined by the eigenvalues of two polarimetric coherence matrices in PolInSAR system, and PIESP is rotated-invariant. PolInSAR systems use two antennas to measure the same ground area with slightly different image geometry. Thus, PIESP can be used to distinguish target based on coherence and similarity. Then the target detection method using PIESP is implemented with DLR E-SAR L-band full polarized image of the Oberpfaffenhofen test site of Germany, obtained on September 30th, 2000. The results confirmed that the proposed model is accurate and effective for detection and analysis of buildings in urban areas.

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.