• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.18 no.12
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• pp.1345-1351
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• 2007

This paper proposes a polarimetric SAR image classification technique based on the degree of poarization(DoP) and copolarized phase-difference(CPD) statistics. At first, the formulation for the DoP and CPD is derived. Then, the classification technique is verified with the SAR full polarimetric L-band data with consideration of exceptional cases. The technique has capability of classifying SAR data into four major classes, such as bare surface, short-vegetation canopy, tall-vegetation canopy, and village.

• Proceedings of the KSRS Conference
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• 2006.03a
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• pp.257-260
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• 2006

본 연구에서는 SIR-C MLC 영상자료와 환경부에서 제공하는 중분류 토지피복도 자료를 참조하여 식생피복지역으로 예상되는 논, 밭 지역으로 분류된 농업지역과 활엽수림, 침엽수림, 혼효림 지역으로 분류된 산림지역에 대한 산란특성을 고찰하기 위해 편광 반응특성을 측정하였다. 편광반응특성분석결과 농업지역과 산림지역의 거동형태는 구형 산란체나 편평한 면에서의 거동특성을 나타냈으며, 측정된 HH, VV, HV 편광매개변수의 후방산란계수 값들은 각각의 지역에서 다른 경향을 보였다.

• Proceedings of the KSRS Conference
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• 2004.10a
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• pp.393-396
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• 2004

In this paper, multifrequency, polarimetric SAR data acquired during the first SIR-C/XSAR mission over the Seoul and Gyunggi-do (Korea) test sites are analyzed. The main objective of the study is to assess the possibility of extracting relevant information about surface properties for geophysical applications using polarimetry. This study analyses the characteristics of polarization responses and polarimetric parameters to conditions present in urban, river, agricultural, and forested areas. Results indicate that the dominant scattering property from these fields varies depending on the land cover types. The polarization response graphs and the backscattering coefficients associated with the polarimetric parameters are also useful in characterizing these cover types.

• Proceedings of the KSRS Conference
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• 2002.10a
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• pp.435-440
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• 2002

The objective of this study is to analyze the relationship between polarimetric radar backscatters and stand characteristics over the mountainous forest area. L- and P-band full polarimetric airborne SAR data obtained in September 2000 were processed to compare with forest stand maps and ground collected stand variables. After the geometric registration of SAR image, mean radar backscatters were extracted for those ground plots where the stand parameters, such as tree height, DBH, and basal area, were measured during and after the SAR data acquisition. Preliminary analysis was focused on the topographic influence of radar backscattering under the homogeneous forest stand condition. Topographic effects, assessed by the local incidence angles, were different obvious in L-band data while it was not clear with P-band data.

• Korean Journal of Remote Sensing
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• v.28 no.3
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• pp.329-336
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• 2012

This paper give further assessment on the original DoP-CPD classification scheme. This paper provides some additional comparative study on the DoP-CPD with H/A/alpha classifier in terms of multi look effects and classification performances. The statistics and multi looking effects of the DoP and CPD were analyzed with measured polarimetric SAR data. DoP-CPD is less sensitive to the number of averaging pixels than the entropy-alpha technique. A DoP-CPD diagram with appropriate boundaries between six different classes was then developed based on the data analysis. A polarimetric SAR image DoP-CPD classification technique is verified with C-band polarimetric RADARSAT-2 images.

• Korean Journal of Remote Sensing
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• v.26 no.1
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• pp.1-7
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• 2010

This paper proposes a new decomposition technique for polarimetric synthetic aperture radar (SAR) images. This new decomposition technique is based on the degree of polarization (DoP) and co-polarized phase-difference (CPD) of the measured polarimetric backscattering coefficients. This decomposition technique is compared with the existing three- and four-component decomposition techniques with the ALOS PALSAR full polarimetric L-band data acquired in 2009. It is shown that the new decomposition technique is better or comparable to the existing techniques for the study areas such as sea, bare soil, forest, and urban area.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.35 no.5
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• pp.365-374
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• 2017

SAR (Synthetic Aperture Radar) is an effective tool for monitoring areas damaged by disasters. Full PolSAR (Polarimetric SAR) enhances SAR's capabilities by providing specific scattering mechanisms. Thus, full PolSAR data have been widely used to analyze the situation when disasters occur. To interpret full PolSAR data, model-based decomposition methods are frequently used due to its easy physical interpretation of PolSAR data and computational efficiency. However, these methods present problems. One of the key problems is the overestimation of the volume scattering component. To minimize the volume scattering component, the OA (Orientation Angle) compensation method is widely utilized. This paper shows that the effect of the OA compensation was analyzed over landslide affected areas. In this paper, the OA compensation is applied by using the OA estimated from the maximum relative Hellinger distance. We conducted an experiment using two full polarimetric ALOS/PALSAR (Advanced Land Observing Satellite/Phased Array type L-band Synthetic Aperture Radar)-2 data collected over Mocoa, Colombia which was seriously damaged by the 2017 Mocoa landslide. After OA compensation, the experimental results showed volume scattering power decreased, while the double-bounce and surface scattering power increased. Particularly, significant changes were noted in urban areas. In addition, after OA compensation, the separability of the double-bounce and surface scattering components are improved over the damaged building areas. Furthermore, changes in the OA can discriminate visually between the damaged building areas and undamaged areas. In conclusion, we demonstrated that the effect of OA compensation improved the influence of the double-bounce and surface scattering components, and OA changes can be useful for detecting damaged building areas.

• Proceedings of the KSRS Conference
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• 2008.10a
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• pp.33-36
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• 2008

This paper presents soil moisture retrieval from measured polarimetric backscattering coefficients of a vegetated surface. Based on the analysis of the quite complicate first-order radiative transfer scattering model for vegetated surfaces, a simplified scattering model is proposed for an inversion algorithm. Extraction of the surface-scatter component from the total scattering of a vegetation canopy is addressed using the simplified model, and also using the three-component decomposition technique. The backscattering coefficients are measured with a polarimetric L-band scatterometer during two months. At the same time, the biomasses, leaf moisture contents, and soil moisture contents are also measured. Then the measurement data are used to estimate the model parameters for vv-, hh-, and vh-polarizations. The scattering model for tall-grass-covered surfaces is inverted to retrieve the soil moisture content from the measurements using a genetic algorithm. The retrieved soil moisture contents agree quite well with the in-situ measured soil moisture data.

• Korean Journal of Remote Sensing
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• v.23 no.2
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• pp.89-101
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• 2007

This paper investigates the effects of the fusion of multifrequency (C and L bands) polarimetric SAR data in land-cover classification. NASA JPL AIRSAR C and L bands data were used to supervised classification in an agricultural area to simulate the integration of ALOS PALSAR and Radarsat-2 SAR data to be available. Several scattering features derived from target decomposition based on eigen value/vector analysis were used as input for a support vector machines classifier and then the posteriori probabilities for each frequency SAR data were integrated by applying a probabilistic ratio model as a decision level fusion methodology. From the case study results, L band data had the proper amount of penetration power and showed better classification accuracy improvement (about 22%) over C band data which did not have enough penetration. When all frequency data were fused for the classification, a significant improvement of about 10% in overall classification accuracy was achieved thanks to an increase of discrimination capability for each class, compared with the case of L band Shh data.

• Proceedings of the KSRS Conference
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• 2008.10a
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• pp.29-32
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• 2008

In this paper, we propose one unsupervised classification technique using the degree of polarization (DoP) and the co-polarized phase-difference (CPD) statistics, instead of the entropy and alpha. It is shown that the DoP is closely related to the entropy, and the CPD to the alpha. The DoP explains the feature how much the effect of multiple reflections is contained. Hence, the DoP could be used as an important factor for classifying classes. The CPD can also be computed from the measured Mueller matrix elements. For the smooth surface scattering, the CPD is about $0^{\circ}$, and for dihedral-type scattering, the CPD is about $180^{\circ}$. A DoP-CPD diagram with appropriate boundaries between six different classes is developed based on the SAR image. The classification results are compared with the existing Entropy-alpha diagram as well as the IPL-AirSAR polarimetric data. The technique may have capability to classify an SAR image into six major classes; a bare surface, a village, a crown-layer short vegetation canopy, a trunk-layer short vegetation canopy, a crown-layer forest, and a trunk-dominated forest. Based on the DoP and CPD analysis, a simple three-component decomposition technique was also proposed.