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RISS 인기검색어
- 검색키워드
- 저자 : ( Keunhoo Cho ) (검색결과 4 건)
- 무료
- 기관 내 무료
- 유료
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Cho, KeunHoo,Park, Sang-Eun,Cho, Jae-Hyoung,Moon, Hyoi,Han, Seung-hoon The Korean Society of Remote Sensing 2018 大韓遠隔探査學會誌 Vol.34 No.4
Recent SAR systems provide fully polarimetric SAR data, which is known to be useful in a variety of applications such as disaster monitoring, target recognition, and land cover classification. The objective of this study is to evaluate the performance of polarization SAR data for landslide detection. The detectability of different SAR parameters was investigated based on the supervised classification approach. The classifier used in this study is the Adaptive Boosting algorithms. A fully polarimetric L-band PALSAR-2 data was used to examine landslides caused by the 2016 Kumamoto earthquake in Kyushu, Japan. Experimental results show that fully polarimetric features from the target decomposition technique can provide improved detectability of landslide site with significant reduction of false alarms as compared with the single polarimetric observables.
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( Keunhoo Cho ),( Sang-eun Park ),( Jae-hyoung Cho ),( Hyoi Moon ),( Seung-hoon Han ) 대한원격탐사학회 2018 大韓遠隔探査學會誌 Vol.34 No.4
Recent SAR systems provide fully polarimetric SAR data, which is known to be useful in a variety of applications such as disaster monitoring, target recognition, and land cover classification. The objective of this study is to evaluate the performance of polarization SAR data for landslide detection. The detectability of different SAR parameters was investigated based on the supervised classification approach. The classifier used in this study is the Adaptive Boosting algorithms. A fully polarimetric L-band PALSAR-2 data was used to examine landslides caused by the 2016 Kumamoto earthquake in Kyushu, Japan. Experimental results show that fully polarimetric features from the target decomposition technique can provide improved detectability of landslide site with significant reduction of false alarms as compared with the single polarimetric observables.
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Development of Landslide Detection Algorithm Using Fully Polarimetric ALOS-2 SAR Data
( Minhwa Kim ),( Keunhoo Cho ),( Yoontaek Jung ),( Yeji Lee ),( Sangeun Park ) 대한지질공학회 2019 대한지질공학회 학술발표회논문집 Vol.2019 No.2
SAR data is useful in a variety of applications such as disaster monitoring, target detection, and land cover classification. Especially, Polarimetric SAR data contains more information compared to conventional single-or dual- polarization SAR data. The objective of this study is to propose a novel landslide detection algorithm based on the polarimetric parameters providing better discriminability of landslide affected areas from undamaged backgrounds, such as the polarimetric entropy, radar vegetation index, and HH- and VV-polarization coherence. Since the estimation of polarimetric parameters can be affected by the number of look, another objective of this study is to evaluate the effect of speckle filtering on the landslide detection using polarimetric SAR data. On the other hand, it is important to correct geometric distortions in polarimetric SAR image because the landslide usually occurs in highly sloping terrain. In this study, we also investigate the effect of orthorectification on the estimation of polarimetric parameters. In order to develop the landslide detection algorithm, several deep seated landslides in Japan caused by Typhoon Talas in September 2011 were investigated using the L-band PALSAR-2 data.
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MSTAR 자료를 이용한 EOC 조건(표적 폐색 및 촬영부각)에 따른 표적인식 정확도 분석
김상완 ( Sang-wan Kim ),한아림 ( Ahrim Han ),조근후 ( Keunhoo Cho ),김동한 ( Donghan Kim ),박상은 ( Sang-eun Park ) 대한원격탐사학회 2019 大韓遠隔探査學會誌 Vol.35 No.3
Automatic Target Recognition (ATR) using Synthetic Aperture Radar (SAR) has been attracted attention in the fields of surveillance, reconnaissance, and national security due to its advantage of all-weather and day-and-night imaging capabilities. However, there have been some difficulties in automatically identifying targets in real situation due to various observational and environmental conditions. In this paper, ATR problems in Extended Operating Conditions (EOC) were investigated. In particular, we considered partial occlusions of the target (10% to 50%) and differences in the depression angle between training (17°) and test data (30° and 45°). To simulate various occlusion conditions, SARBake algorithm was applied to Moving and Stationary Target Acquisition and Recognition (MSTAR) images. The ATR accuracies were evaluated by using the template matching and Adaboost algorithms. Experimental results on the depression angle showed that the target identification rate of the two algorithms decreased by more than 30% from the depression angle of 45° to 30°. The accuracy of template matching was about 75.88% while Adaboost showed better results with an accuracy of about 86.80%. In the case of partial occlusion, the accuracy of template matching decreased significantly even in the slight occlusion (from 95.77% under no occlusion to 52.69% under 10% occlusion). The Adaboost algorithm showed better performance with an accuracy of 85.16% in no occlusion condition and 68.48% in 10% occlusion condition. Even in the 50% occlusion condition, the Adaboost provided an accuracy of 52.48%, which was much higher than the template matching (less than 30% under 50% occlusion).
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