Pub Date : 2019-11-01DOI: 10.1109/APSAR46974.2019.9048572
Lin Wu, Mengwei Sun, Lin Min, Jianhui Zhao, Ning Li, Zhengwei Guo
The algal bloom is a prominent manifestation of water pollution. Synthetic aperture radar (SAR) shows an advantage in water monitoring due to its characteristic of all-time and all-weather. The water regions where algae gather present dark in SAR image. However, dark regions may also be caused by other factors, such as low wind. This paper proposes an improved algal bloom discrimination method based on Artificial Neural Network (ANN) to recognize the dark regions of algal bloom. Taihu Lake is chosen as the research area in this study because of its serious bloom in recent years. By means of quasi-synchronous optical images, the dark region database of SAR images labeled algal bloom and non-algal bloom are obtained. Then the segmentation algorithm and region growing algorithm are used to acquire the feature from dark regions, and divided into training feature set and testing feature set. Finally, the training and testing feature set are used for ANN-based discrimination model construction and verification. According the experimental results, the overall accuracy reaches 80%, which indicates that ANN model has a good applicability in algal bloom recognition of SAR image.
{"title":"An improved method of algal-bloom discrimination in Taihu Lake using Sentinel-1A data","authors":"Lin Wu, Mengwei Sun, Lin Min, Jianhui Zhao, Ning Li, Zhengwei Guo","doi":"10.1109/APSAR46974.2019.9048572","DOIUrl":"https://doi.org/10.1109/APSAR46974.2019.9048572","url":null,"abstract":"The algal bloom is a prominent manifestation of water pollution. Synthetic aperture radar (SAR) shows an advantage in water monitoring due to its characteristic of all-time and all-weather. The water regions where algae gather present dark in SAR image. However, dark regions may also be caused by other factors, such as low wind. This paper proposes an improved algal bloom discrimination method based on Artificial Neural Network (ANN) to recognize the dark regions of algal bloom. Taihu Lake is chosen as the research area in this study because of its serious bloom in recent years. By means of quasi-synchronous optical images, the dark region database of SAR images labeled algal bloom and non-algal bloom are obtained. Then the segmentation algorithm and region growing algorithm are used to acquire the feature from dark regions, and divided into training feature set and testing feature set. Finally, the training and testing feature set are used for ANN-based discrimination model construction and verification. According the experimental results, the overall accuracy reaches 80%, which indicates that ANN model has a good applicability in algal bloom recognition of SAR image.","PeriodicalId":377019,"journal":{"name":"2019 6th Asia-Pacific Conference on Synthetic Aperture Radar (APSAR)","volume":"365 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124583131","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-11-01DOI: 10.1109/APSAR46974.2019.9048521
F. Yuan, C. Ticehurst, Zheng-Shu Zhou, E. Lehmann, B. Lewis, A. Rosenqvist, Sean M. T. Chua, N. Mueller
We present a study to use Sentinel-1 data to map open water in Australia, with a goal to develop a method that can eventually be applied automatically across the continent. The Digital Earth Australia platform and its optical based water classification product are used to train and validate our model. We present test results of this supervised model in multiple sites with different land cover properties and discuss future improvements.
{"title":"Water Mapping with SAR and Optical Data Cube","authors":"F. Yuan, C. Ticehurst, Zheng-Shu Zhou, E. Lehmann, B. Lewis, A. Rosenqvist, Sean M. T. Chua, N. Mueller","doi":"10.1109/APSAR46974.2019.9048521","DOIUrl":"https://doi.org/10.1109/APSAR46974.2019.9048521","url":null,"abstract":"We present a study to use Sentinel-1 data to map open water in Australia, with a goal to develop a method that can eventually be applied automatically across the continent. The Digital Earth Australia platform and its optical based water classification product are used to train and validate our model. We present test results of this supervised model in multiple sites with different land cover properties and discuss future improvements.","PeriodicalId":377019,"journal":{"name":"2019 6th Asia-Pacific Conference on Synthetic Aperture Radar (APSAR)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124610006","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-11-01DOI: 10.1109/APSAR46974.2019.9048269
Hao Su, Shunjun Wei, Mengke Wang, Liming Zhou, Jun Shi, Xiaoling Zhang
Ship detection in high-resolution synthetic aperture radar (SAR) imagery is a fundamental and challenging problem due to the complex environments. In this paper, a RetinaNet-Plus method is presented for ship detection in high-resolution SAR imagery based on RetinaNet network modified. In this approach, instead of setting the score for neighboring region proposals to zero as in Non-Maximum Suppression (NMS), Soft-NMS decreases the detection scores as an increasing function of overlap to avoid loss of precision. In addition, focal loss is used to address the class imbalance and to increase the importance of the hard examples during training. The experiments on SAR ship SSDD dataset and TerraSAR-X image from Barcelona port, show that our method is more accurate than the existing algorithms and is effective for ship detection of high-resolution SAR imagery.
{"title":"Ship Detection Based on RetinaNet-Plus for High-Resolution SAR Imagery","authors":"Hao Su, Shunjun Wei, Mengke Wang, Liming Zhou, Jun Shi, Xiaoling Zhang","doi":"10.1109/APSAR46974.2019.9048269","DOIUrl":"https://doi.org/10.1109/APSAR46974.2019.9048269","url":null,"abstract":"Ship detection in high-resolution synthetic aperture radar (SAR) imagery is a fundamental and challenging problem due to the complex environments. In this paper, a RetinaNet-Plus method is presented for ship detection in high-resolution SAR imagery based on RetinaNet network modified. In this approach, instead of setting the score for neighboring region proposals to zero as in Non-Maximum Suppression (NMS), Soft-NMS decreases the detection scores as an increasing function of overlap to avoid loss of precision. In addition, focal loss is used to address the class imbalance and to increase the importance of the hard examples during training. The experiments on SAR ship SSDD dataset and TerraSAR-X image from Barcelona port, show that our method is more accurate than the existing algorithms and is effective for ship detection of high-resolution SAR imagery.","PeriodicalId":377019,"journal":{"name":"2019 6th Asia-Pacific Conference on Synthetic Aperture Radar (APSAR)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130316535","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-11-01DOI: 10.1109/APSAR46974.2019.9048401
Li Li, Caipin Li, Mingyi He, Chongdi Duan
In order to verify the imaging effect of long synthetic aperture time imaging, we describe an airship-born SAR experiment firstly and present measurement results. In May 2019, a measurement campaign was conducted in Jiangsu Province in China. In this paper, the equipment composition is described. Imaging results show that the two-dimensional spectrum of echo signal based on stationary phase principle is no longer valid because of the azimuth wavenumber is larger than the range wavenumber. Therefore, the frequency domain imaging algorithm is difficult to apply in the raw data processing. So the BP imaging algorithm is used to process experimental data.
{"title":"Imaging experiment based on airship-born SAR for long synthetic aperture time","authors":"Li Li, Caipin Li, Mingyi He, Chongdi Duan","doi":"10.1109/APSAR46974.2019.9048401","DOIUrl":"https://doi.org/10.1109/APSAR46974.2019.9048401","url":null,"abstract":"In order to verify the imaging effect of long synthetic aperture time imaging, we describe an airship-born SAR experiment firstly and present measurement results. In May 2019, a measurement campaign was conducted in Jiangsu Province in China. In this paper, the equipment composition is described. Imaging results show that the two-dimensional spectrum of echo signal based on stationary phase principle is no longer valid because of the azimuth wavenumber is larger than the range wavenumber. Therefore, the frequency domain imaging algorithm is difficult to apply in the raw data processing. So the BP imaging algorithm is used to process experimental data.","PeriodicalId":377019,"journal":{"name":"2019 6th Asia-Pacific Conference on Synthetic Aperture Radar (APSAR)","volume":"129 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126878941","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-11-01DOI: 10.1109/APSAR46974.2019.9048553
Shuliang Gui, Jin Li, Feng Zuo
In this paper, we propose the two-dimensional imaging response of three-dimensional moving target via synthetic aperture radar (SAR) with back-projection imaging algorithm. Firstly, we analyzed the imaging response for each single echo, which is the intersection of Doppler isodop and iso-range contour. Then, we come up with a focus condition for moving target SAR imaging. Besides, we find that the Doppler frequency will cause a shift and the motion trail of moving target will draw a curve in the imaging result over synthetic aperture time. Meanwhile, we provide the numerical simulation experiments of moving point targets via linear SAR and circular SAR with X-band and THz-band, respectively, to verify the conclusion of moving target imaging response.
{"title":"Synthetic Aperture Radar Imaging Response of Three-dimensional Moving Target","authors":"Shuliang Gui, Jin Li, Feng Zuo","doi":"10.1109/APSAR46974.2019.9048553","DOIUrl":"https://doi.org/10.1109/APSAR46974.2019.9048553","url":null,"abstract":"In this paper, we propose the two-dimensional imaging response of three-dimensional moving target via synthetic aperture radar (SAR) with back-projection imaging algorithm. Firstly, we analyzed the imaging response for each single echo, which is the intersection of Doppler isodop and iso-range contour. Then, we come up with a focus condition for moving target SAR imaging. Besides, we find that the Doppler frequency will cause a shift and the motion trail of moving target will draw a curve in the imaging result over synthetic aperture time. Meanwhile, we provide the numerical simulation experiments of moving point targets via linear SAR and circular SAR with X-band and THz-band, respectively, to verify the conclusion of moving target imaging response.","PeriodicalId":377019,"journal":{"name":"2019 6th Asia-Pacific Conference on Synthetic Aperture Radar (APSAR)","volume":"62 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123812372","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-11-01DOI: 10.1109/APSAR46974.2019.9048392
K. D. de Macedo, S. Placidi, A. Meta
This paper presents the interferometric results obtained from the data acquired during two MetaSensing campaigns. The first results are related to the Bistatic SAR campaign at L-band in Belgium. The data were acquired with the objective to monitor agriculture and crop growth as part of the ESA BelSAR project. The second results are related to single-pass monostatic InSAR acquisitions at C-band in Norway. The objective is to derive the interferometric height for further use in the carbon stock estimation context.
{"title":"Bistatic and Monostatic InSAR Results with the MetaSensing Airborne SAR System","authors":"K. D. de Macedo, S. Placidi, A. Meta","doi":"10.1109/APSAR46974.2019.9048392","DOIUrl":"https://doi.org/10.1109/APSAR46974.2019.9048392","url":null,"abstract":"This paper presents the interferometric results obtained from the data acquired during two MetaSensing campaigns. The first results are related to the Bistatic SAR campaign at L-band in Belgium. The data were acquired with the objective to monitor agriculture and crop growth as part of the ESA BelSAR project. The second results are related to single-pass monostatic InSAR acquisitions at C-band in Norway. The objective is to derive the interferometric height for further use in the carbon stock estimation context.","PeriodicalId":377019,"journal":{"name":"2019 6th Asia-Pacific Conference on Synthetic Aperture Radar (APSAR)","volume":"66 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124213303","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
High resolution inverse synthetic aperture radar(ISAR) image is becoming increasingly important in target recognition and space situation awareness, however, many problems appear accompanying the high resolution such as the nonideal performance of amplitude and phase of broadband signal, and the migration through resolution cell(MTRC). To reduce the harmful influence caused by the nonideal performance of broadband signal, a millimeter wave radar of good magnitude-phase property, based on microwave photonics technique, has been built, meanwhile, a new form of keystone transform related to the dechirping mode is proposed to compensate the relative moving between different scattering points of target. After that, the 4GHz high resolution ISAR images of standard reflectors and aircraft are obtained by the microwave photonics radar and compensating algorithm, which demonstrates that both the system and compensating method work well.
{"title":"Research of Broadband Imaging Technique Based on a Microwave Photonic Radar","authors":"Li Yu, Jiquan Zhai, Guoqiang Zhang, Yuqi Tan, Ziqian Wang, Yingni Hou","doi":"10.1109/APSAR46974.2019.9048271","DOIUrl":"https://doi.org/10.1109/APSAR46974.2019.9048271","url":null,"abstract":"High resolution inverse synthetic aperture radar(ISAR) image is becoming increasingly important in target recognition and space situation awareness, however, many problems appear accompanying the high resolution such as the nonideal performance of amplitude and phase of broadband signal, and the migration through resolution cell(MTRC). To reduce the harmful influence caused by the nonideal performance of broadband signal, a millimeter wave radar of good magnitude-phase property, based on microwave photonics technique, has been built, meanwhile, a new form of keystone transform related to the dechirping mode is proposed to compensate the relative moving between different scattering points of target. After that, the 4GHz high resolution ISAR images of standard reflectors and aircraft are obtained by the microwave photonics radar and compensating algorithm, which demonstrates that both the system and compensating method work well.","PeriodicalId":377019,"journal":{"name":"2019 6th Asia-Pacific Conference on Synthetic Aperture Radar (APSAR)","volume":"13 2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116496488","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
An adaptive classification is developed as a hybrid of the eigenvector- and model-based decompositions of polarimetric SAR (PolSAR) image. It adopts the canonical models that widely used in model-based target decomposition to obtain an improvement for the well-known $H/alpha$ classification. First, a correspondence principle is developed to adaptively select the matched canonical models. The models are parallelly combined in terms of the scattering similarity for a fine description of the scattering mechanism then. Twelve classes are finally achieved with each one carrying a unique symbol to indicate a specific scattering. The classification does not depend on a particular data set, avoids the hard partitioning, and solves the obscures in $H/alpha$. Comparison on real PolSAR image with $H/alpha$ validates the better discrimination of radar targets.
{"title":"Adaptive Model-Based Classification of Polarimetric SAR Image","authors":"Dong Li, Yunhua Zhang, Liting Liang, Jiefang Yang, Xiaojin Shi, Xun Wang","doi":"10.1109/APSAR46974.2019.9048390","DOIUrl":"https://doi.org/10.1109/APSAR46974.2019.9048390","url":null,"abstract":"An adaptive classification is developed as a hybrid of the eigenvector- and model-based decompositions of polarimetric SAR (PolSAR) image. It adopts the canonical models that widely used in model-based target decomposition to obtain an improvement for the well-known $H/alpha$ classification. First, a correspondence principle is developed to adaptively select the matched canonical models. The models are parallelly combined in terms of the scattering similarity for a fine description of the scattering mechanism then. Twelve classes are finally achieved with each one carrying a unique symbol to indicate a specific scattering. The classification does not depend on a particular data set, avoids the hard partitioning, and solves the obscures in $H/alpha$. Comparison on real PolSAR image with $H/alpha$ validates the better discrimination of radar targets.","PeriodicalId":377019,"journal":{"name":"2019 6th Asia-Pacific Conference on Synthetic Aperture Radar (APSAR)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121579936","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-11-01DOI: 10.1109/APSAR46974.2019.9048251
Shuang Yang, Xiaoran Shi, Feng Zhou
Synthetic aperture radar (SAR) automatic target recognition (ATR) is one of the hottest issue in current research because of its wide application value. However, the low-resolution SAR images will decline the recognition accuracy of targets due to its obscure characteristic, and meanwhile it is difficult to acquire a great number of high-resolution SAR images for extracting clear characteristic. To solve these problems, this paper proposes a method of ATR for low-resolution SAR images based on super-resolution network. Super-resolution generative adversarial network (SRGAN) and deep convolutional neural network (DCNN) are utilized for extracting characteristic and classification, respectively. The segmented low-resolution SAR images are enhanced through SRGAN to improve the visual resolution and the feature characterization ability of target in SAR image; Then the enhanced SAR images are classified automatically by DCNN. Finally, the effectiveness and the efficiency are verified on the open data set, moving and stationary target acquisition and recognition (MSTAR).
{"title":"Automatic Target Recognition for Low-Resolution SAR Images Based on Super-Resolution Network","authors":"Shuang Yang, Xiaoran Shi, Feng Zhou","doi":"10.1109/APSAR46974.2019.9048251","DOIUrl":"https://doi.org/10.1109/APSAR46974.2019.9048251","url":null,"abstract":"Synthetic aperture radar (SAR) automatic target recognition (ATR) is one of the hottest issue in current research because of its wide application value. However, the low-resolution SAR images will decline the recognition accuracy of targets due to its obscure characteristic, and meanwhile it is difficult to acquire a great number of high-resolution SAR images for extracting clear characteristic. To solve these problems, this paper proposes a method of ATR for low-resolution SAR images based on super-resolution network. Super-resolution generative adversarial network (SRGAN) and deep convolutional neural network (DCNN) are utilized for extracting characteristic and classification, respectively. The segmented low-resolution SAR images are enhanced through SRGAN to improve the visual resolution and the feature characterization ability of target in SAR image; Then the enhanced SAR images are classified automatically by DCNN. Finally, the effectiveness and the efficiency are verified on the open data set, moving and stationary target acquisition and recognition (MSTAR).","PeriodicalId":377019,"journal":{"name":"2019 6th Asia-Pacific Conference on Synthetic Aperture Radar (APSAR)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114838762","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-11-01DOI: 10.1109/apsar46974.2019.9048514
Yun Lin, Qiming Zhang, Yanping Wang, Yang Li, Yang Song, Yutong Liu
Traditional linear GBSAR can only image in the range-azimuth plane. It can't obtain elevation information of the target. Circular GBSAR is a new GBSAR model. Due to the advantage of resolution in the vertical direction, circular GBSAR can acquire the 3D image. However, the azimuth section of the single-baseline circular GBSAR image has high side lobes. In this paper, a multi-baseline circular GBSAR 3D imaging system using multiple antennas is proposed. Multiple antennas are fixed to the rotating arm, the rotating arm rotates in a vertical plane to form a circular synthetic aperture, and the antenna beam points directly in front. Multi-baseline circular GBSAR model can effectively reduce the side lobe of the azimuth section of the SAR image. In this paper we analyzed the effect of the multi-baseline circular GBSAR model on reducing the side lobes of the azimuth section and the special variation of point target imaging at different locations. By the simulation experiments, the 3D resolution ability of the multi-baseline circular GBSAR for different point targets is verified.
{"title":"3D imaging performance analysis of multi-baseline Circular GBSAR","authors":"Yun Lin, Qiming Zhang, Yanping Wang, Yang Li, Yang Song, Yutong Liu","doi":"10.1109/apsar46974.2019.9048514","DOIUrl":"https://doi.org/10.1109/apsar46974.2019.9048514","url":null,"abstract":"Traditional linear GBSAR can only image in the range-azimuth plane. It can't obtain elevation information of the target. Circular GBSAR is a new GBSAR model. Due to the advantage of resolution in the vertical direction, circular GBSAR can acquire the 3D image. However, the azimuth section of the single-baseline circular GBSAR image has high side lobes. In this paper, a multi-baseline circular GBSAR 3D imaging system using multiple antennas is proposed. Multiple antennas are fixed to the rotating arm, the rotating arm rotates in a vertical plane to form a circular synthetic aperture, and the antenna beam points directly in front. Multi-baseline circular GBSAR model can effectively reduce the side lobe of the azimuth section of the SAR image. In this paper we analyzed the effect of the multi-baseline circular GBSAR model on reducing the side lobes of the azimuth section and the special variation of point target imaging at different locations. By the simulation experiments, the 3D resolution ability of the multi-baseline circular GBSAR for different point targets is verified.","PeriodicalId":377019,"journal":{"name":"2019 6th Asia-Pacific Conference on Synthetic Aperture Radar (APSAR)","volume":"125 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124489605","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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