Pub Date : 2019-07-28DOI: 10.1109/IGARSS.2019.8898060
Alessandro Montaldo, L. Fronda, Ihsen Hedhli, G. Moser, J. Zerubia, S. Serpico
In this paper, the problem of the classification of multiresolution and multisensor remotely sensed data is addressed by proposing a multiscale Markov mesh model. Multiresolution and multisensor fusion are jointly achieved through an explicitly hierarchical probabilistic graphical classifier, which uses a quadtree structure to model the interactions across different spatial resolutions, and a symmetric Markov mesh random field to deal with contextual information at each scale and favor applicability to very high resolution imagery. Differently from previous hierarchical Markovian approaches, here, data collected by distinct sensors are fused through either the graph topology itself (across its layers) or decision tree ensemble methods (within each layer). The proposed model allows taking benefit of strong analytical properties, most remarkably causality, which make it possible to apply time-efficient non-iterative inference algorithms.
{"title":"Joint Classification of Multiresolution and Multisensor Data Using a Multiscale Markov Mesh Model","authors":"Alessandro Montaldo, L. Fronda, Ihsen Hedhli, G. Moser, J. Zerubia, S. Serpico","doi":"10.1109/IGARSS.2019.8898060","DOIUrl":"https://doi.org/10.1109/IGARSS.2019.8898060","url":null,"abstract":"In this paper, the problem of the classification of multiresolution and multisensor remotely sensed data is addressed by proposing a multiscale Markov mesh model. Multiresolution and multisensor fusion are jointly achieved through an explicitly hierarchical probabilistic graphical classifier, which uses a quadtree structure to model the interactions across different spatial resolutions, and a symmetric Markov mesh random field to deal with contextual information at each scale and favor applicability to very high resolution imagery. Differently from previous hierarchical Markovian approaches, here, data collected by distinct sensors are fused through either the graph topology itself (across its layers) or decision tree ensemble methods (within each layer). The proposed model allows taking benefit of strong analytical properties, most remarkably causality, which make it possible to apply time-efficient non-iterative inference algorithms.","PeriodicalId":13262,"journal":{"name":"IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium","volume":"4 1","pages":"2810-2813"},"PeriodicalIF":0.0,"publicationDate":"2019-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84511632","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 : 2019-07-01DOI: 10.1109/IGARSS.2019.8900659
C. Pei, T. He
UVB radiation refers to ultraviolet (UV) with wavelength ranging from 280nm to 320nm and plays a major role in vitamin D synthesis, plant growth, and human health. In this article, erythemal weighted UVB irradiance (UVER) is modeled on the Surface Radiation Budget Network (SURFRAD) stations based on the relationship with solar zenith angel (SZA), clearness index (Kt), and ozone (O3). Two models are established, one of which can be used when the O3 information is missing (Model I) and the other one (Model II) takes O3 into consideration. Verification indicates both Model I and Model II show good performance on Fort Peck, Montana with tiny mean bias error (MBE), within ±0.2%, while Model II performs more stable when verified on all SURFRAD stations, with smaller MBE (-1.61%) and root mean square error (RMSE). By using MODIS downward shortwave radiation (DSR) as model input, a UVER product with a resolution of 5km×5km can be obtained. The MBE of this product on SURFRAD stations is 0.82% and 2.85% for the instantaneous and 3-hour estimation, respectively. And similar result can be obtained on stations of UVB monitoring and research program (UVMRP) maintained by U.S. department of agriculture. Erythemal daily dose (EDD) is further calculated from the hourly UVER product, and the result corresponds to that from measurement within ±10% bias in 33 out of total 35 stations and within ±5% bias in 18 stations. In addition, comparison with OMI product OMUVBd shows that our result corresponds the ground measurements better.
UVB辐射是指波长在280nm至320nm之间的紫外线，对维生素D合成、植物生长和人体健康起着重要作用。本文基于太阳天顶角(SZA)、清晰度指数(Kt)和臭氧(O3)的关系，在地表辐射收支网(SURFRAD)台站上模拟了红斑加权UVB辐照度(UVER)。建立了两个模型，其中一个模型是在O3信息缺失的情况下使用的(模型一)，另一个模型是考虑了O3的(模型二)。验证表明，模型I和模型II在蒙大拿州的Fort Peck上表现良好，平均偏差误差(MBE)很小，在±0.2%以内，而模型II在所有SURFRAD站点上验证时表现更稳定，MBE(-1.61%)和均方根误差(RMSE)更小。使用MODIS下向短波辐射(DSR)作为模型输入，可以得到分辨率为5km×5km的UVER产品。该产品在SURFRAD台站瞬时和3小时估计的MBE分别为0.82%和2.85%。在美国农业部UVMRP监测与研究项目(UVB monitoring And research program, UVMRP)站点上也可以得到类似的结果。根据每小时UVER产品进一步计算红斑日剂量(EDD)，结果与35个站点中33个站点在±10%偏差内的测量结果相对应，18个站点在±5%偏差内的测量结果相对应。此外，与OMI产品OMUVBd的对比表明，我们的结果与地面测量结果吻合得更好。
{"title":"UV Radiation Estimation in the United States Using Modis Data","authors":"C. Pei, T. He","doi":"10.1109/IGARSS.2019.8900659","DOIUrl":"https://doi.org/10.1109/IGARSS.2019.8900659","url":null,"abstract":"UVB radiation refers to ultraviolet (UV) with wavelength ranging from 280nm to 320nm and plays a major role in vitamin D synthesis, plant growth, and human health. In this article, erythemal weighted UVB irradiance (UVER) is modeled on the Surface Radiation Budget Network (SURFRAD) stations based on the relationship with solar zenith angel (SZA), clearness index (Kt), and ozone (O3). Two models are established, one of which can be used when the O3 information is missing (Model I) and the other one (Model II) takes O3 into consideration. Verification indicates both Model I and Model II show good performance on Fort Peck, Montana with tiny mean bias error (MBE), within ±0.2%, while Model II performs more stable when verified on all SURFRAD stations, with smaller MBE (-1.61%) and root mean square error (RMSE). By using MODIS downward shortwave radiation (DSR) as model input, a UVER product with a resolution of 5km×5km can be obtained. The MBE of this product on SURFRAD stations is 0.82% and 2.85% for the instantaneous and 3-hour estimation, respectively. And similar result can be obtained on stations of UVB monitoring and research program (UVMRP) maintained by U.S. department of agriculture. Erythemal daily dose (EDD) is further calculated from the hourly UVER product, and the result corresponds to that from measurement within ±10% bias in 33 out of total 35 stations and within ±5% bias in 18 stations. In addition, comparison with OMI product OMUVBd shows that our result corresponds the ground measurements better.","PeriodicalId":13262,"journal":{"name":"IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium","volume":"131 1","pages":"1880-1883"},"PeriodicalIF":0.0,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73497378","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 : 2019-07-01DOI: 10.1109/IGARSS.2019.8900352
Haruki Imai, Koichi Ito, T. Aoki, J. Uemoto, S. Uratsuka
Observation of seismic ground deformation is one of the fundamental topics in remote sensing. A Synthetic Aperture Radar (SAR) has been used to obtain images representing geometrical properties of the ground surface. SAR images can be taken in nearly all weather conditions and in nearly all time. This paper proposes a ground deformation observation method using image correspondence matching, which employs phase-only correlation to estimate displacement between two SAR intensity images with sub-pixel accuracy. Through experiments using airborne SAR intensity images of the Kumamoto Earthquake, we demonstrate that the proposed method exhibits the efficient performance in observing seismic ground deformation.
{"title":"A Method for Observing Seismic Ground Deformation from Airborne SAR Images","authors":"Haruki Imai, Koichi Ito, T. Aoki, J. Uemoto, S. Uratsuka","doi":"10.1109/IGARSS.2019.8900352","DOIUrl":"https://doi.org/10.1109/IGARSS.2019.8900352","url":null,"abstract":"Observation of seismic ground deformation is one of the fundamental topics in remote sensing. A Synthetic Aperture Radar (SAR) has been used to obtain images representing geometrical properties of the ground surface. SAR images can be taken in nearly all weather conditions and in nearly all time. This paper proposes a ground deformation observation method using image correspondence matching, which employs phase-only correlation to estimate displacement between two SAR intensity images with sub-pixel accuracy. Through experiments using airborne SAR intensity images of the Kumamoto Earthquake, we demonstrate that the proposed method exhibits the efficient performance in observing seismic ground deformation.","PeriodicalId":13262,"journal":{"name":"IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium","volume":"42 1","pages":"1506-1509"},"PeriodicalIF":0.0,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73669125","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 : 2019-07-01DOI: 10.1109/IGARSS.2019.8900041
Chi Xu, Wanchang Zhang, Yaning Yi, Qi Xu
The main purpose of this study is to map landslide susceptibility using the logistic regression model based on information value, for the region along China-Thailand Railway from Saraburi to Sikhio, Thailand. In this study, a total of 60 landslides identified from remotely sensed images were divided into two groups: a group of 80% for training and the left 20% for validation. Landslide hazardous areas were mapped using six landslide controlling factors by logistic regression model based on information value. The performance of the model was evaluated by Receiver Operating Characteristic (ROC) curve. The results showed the model could provide 81.8% and 79.4% success and prediction rates respectively, meaning the map behaved good performance. Furthermore, the two factors of river networks and geotechnical types had a higher impact on the occurrence of landslides compared with other factors. This landslide susceptibility map can be used for preliminary railway construction and landslide mitigation.
{"title":"Landslide Susceptibility Mapping Using Logistic Regression Model Based On Information Value for the Region Along China-Thailand Railway from Saraburi To Sikhio, Thailand","authors":"Chi Xu, Wanchang Zhang, Yaning Yi, Qi Xu","doi":"10.1109/IGARSS.2019.8900041","DOIUrl":"https://doi.org/10.1109/IGARSS.2019.8900041","url":null,"abstract":"The main purpose of this study is to map landslide susceptibility using the logistic regression model based on information value, for the region along China-Thailand Railway from Saraburi to Sikhio, Thailand. In this study, a total of 60 landslides identified from remotely sensed images were divided into two groups: a group of 80% for training and the left 20% for validation. Landslide hazardous areas were mapped using six landslide controlling factors by logistic regression model based on information value. The performance of the model was evaluated by Receiver Operating Characteristic (ROC) curve. The results showed the model could provide 81.8% and 79.4% success and prediction rates respectively, meaning the map behaved good performance. Furthermore, the two factors of river networks and geotechnical types had a higher impact on the occurrence of landslides compared with other factors. This landslide susceptibility map can be used for preliminary railway construction and landslide mitigation.","PeriodicalId":13262,"journal":{"name":"IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium","volume":"68 1","pages":"9650-9653"},"PeriodicalIF":0.0,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73997200","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 : 2019-07-01DOI: 10.1109/IGARSS.2019.8897938
Rui Yang, Xin Xu, Zhaozhuo Xu, Chujiang Ding, Fangling Pu
Interpretation of convolutional neural networks (CNNs) critically influence our understanding of deep learning models’ internal dynamics. In this paper, we demonstrate an interpretable training method, namely class activation mapping guided adversarial training (CAMAT), for two typical remote sensing tasks, land-use classification and object detection. We first generate class activation maps of the current batch training samples. Class activation map is a kind of class-specific saliency map that quantifies the contributions of a particular region in the image to the CNN prediction result. Then, high contribution regions in the training samples are occluded, and we leverage the partial masked images as the inputs for network training. Following this paradigm, the key areas for network learning and decision making are purposefully disturbed in the training phase, thus the trained model could have better performance in robustness and generalization. Experiments conducted on classic remote sensing datasets verified the outperforming effectiveness and efficiency of the proposed CAMAT.
{"title":"A Class Activation Mapping Guided Adversarial Training Method for Land-Use Classification and Object Detection","authors":"Rui Yang, Xin Xu, Zhaozhuo Xu, Chujiang Ding, Fangling Pu","doi":"10.1109/IGARSS.2019.8897938","DOIUrl":"https://doi.org/10.1109/IGARSS.2019.8897938","url":null,"abstract":"Interpretation of convolutional neural networks (CNNs) critically influence our understanding of deep learning models’ internal dynamics. In this paper, we demonstrate an interpretable training method, namely class activation mapping guided adversarial training (CAMAT), for two typical remote sensing tasks, land-use classification and object detection. We first generate class activation maps of the current batch training samples. Class activation map is a kind of class-specific saliency map that quantifies the contributions of a particular region in the image to the CNN prediction result. Then, high contribution regions in the training samples are occluded, and we leverage the partial masked images as the inputs for network training. Following this paradigm, the key areas for network learning and decision making are purposefully disturbed in the training phase, thus the trained model could have better performance in robustness and generalization. Experiments conducted on classic remote sensing datasets verified the outperforming effectiveness and efficiency of the proposed CAMAT.","PeriodicalId":13262,"journal":{"name":"IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium","volume":"28 1","pages":"9474-9477"},"PeriodicalIF":0.0,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74028321","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 : 2019-07-01DOI: 10.1109/IGARSS.2019.8898965
F. Iturbide-Sanchez, Joe K. Taylor, M. Esplin, B. Yan, C. Cao, S. Kalluri, Yong Chen, D. Tremblay, Xin Jin, D. Tobin, H. Revercomb, L. Strow, David G. Johnson, J. Predina
In this work, the current performance of the calibrated Joint Polar Satellite System (JPSS) Cross-track Infrared Sensor (CrIS) observations is reported. The CrIS instrument is currently on-board the Suomi National Polar-orbiting Partnership (SNPP) and NOAA-20 spacecraft, and planned for the JPSS-2, -3 and -4 satellites. Presently, calibrated and validated CrIS observations, in the form of sensor data record (SDR) products, are being assimilated by operational NWP models and atmospheric retrieval systems. CrIS measurements from SNPP and NOAA-20 are expected to improve our understanding of the dynamics of the atmosphere due to the higher temporal and spatial coverage resulting from optimally blending the hyperspectral Earth observations. This work also reports recent improvements performed on the CrIS SDR products, including: 1) the implementation of the polarization correction, 2) the optimization of the spike detection and correction algorithm, and 3) the optimization of the lunar intrusion algorithm.
{"title":"Performance of the SNPP and NOAA-20 CrIS Sensor Data Record Products","authors":"F. Iturbide-Sanchez, Joe K. Taylor, M. Esplin, B. Yan, C. Cao, S. Kalluri, Yong Chen, D. Tremblay, Xin Jin, D. Tobin, H. Revercomb, L. Strow, David G. Johnson, J. Predina","doi":"10.1109/IGARSS.2019.8898965","DOIUrl":"https://doi.org/10.1109/IGARSS.2019.8898965","url":null,"abstract":"In this work, the current performance of the calibrated Joint Polar Satellite System (JPSS) Cross-track Infrared Sensor (CrIS) observations is reported. The CrIS instrument is currently on-board the Suomi National Polar-orbiting Partnership (SNPP) and NOAA-20 spacecraft, and planned for the JPSS-2, -3 and -4 satellites. Presently, calibrated and validated CrIS observations, in the form of sensor data record (SDR) products, are being assimilated by operational NWP models and atmospheric retrieval systems. CrIS measurements from SNPP and NOAA-20 are expected to improve our understanding of the dynamics of the atmosphere due to the higher temporal and spatial coverage resulting from optimally blending the hyperspectral Earth observations. This work also reports recent improvements performed on the CrIS SDR products, including: 1) the implementation of the polarization correction, 2) the optimization of the spike detection and correction algorithm, and 3) the optimization of the lunar intrusion algorithm.","PeriodicalId":13262,"journal":{"name":"IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium","volume":"1 1","pages":"8815-8818"},"PeriodicalIF":0.0,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74071721","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 : 2019-07-01DOI: 10.1109/IGARSS.2019.8898677
Tianzhu Liu, Yanfeng Gu
The miniaturization of hyperspectral sensors and the popularity of the unmanned aerial vehicle (UAV) make it possible to obtain a series of hyperspectral images (HSIs) in the same geographical area at different time-points by same or different sensors. When classifying these multi-temporal HSIs, temporal-adaptation is required to deal with the spectral drift and band inconsistency problems. Since most studies focus on semi-supervised domain adaptation (DA) strategy, and spatial features are usually absent during most of the DA procedure, an unsupervised temporal-adaptation method is realized by spatial-spectral multiple Geodesic Flow Kernels (S2-GFKs) to classify bi-temporal HSIs. Experiments conducted on two real HSI datasets and compared with several well-known methods demonstrate the availability of the proposed model.
{"title":"Unsupervised Temporal-Adaptation with Multiple Geodesic Flow Kernels for Hyperspectral Image Classification","authors":"Tianzhu Liu, Yanfeng Gu","doi":"10.1109/IGARSS.2019.8898677","DOIUrl":"https://doi.org/10.1109/IGARSS.2019.8898677","url":null,"abstract":"The miniaturization of hyperspectral sensors and the popularity of the unmanned aerial vehicle (UAV) make it possible to obtain a series of hyperspectral images (HSIs) in the same geographical area at different time-points by same or different sensors. When classifying these multi-temporal HSIs, temporal-adaptation is required to deal with the spectral drift and band inconsistency problems. Since most studies focus on semi-supervised domain adaptation (DA) strategy, and spatial features are usually absent during most of the DA procedure, an unsupervised temporal-adaptation method is realized by spatial-spectral multiple Geodesic Flow Kernels (S2-GFKs) to classify bi-temporal HSIs. Experiments conducted on two real HSI datasets and compared with several well-known methods demonstrate the availability of the proposed model.","PeriodicalId":13262,"journal":{"name":"IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium","volume":"46 1","pages":"10111-10114"},"PeriodicalIF":0.0,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74127124","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 : 2019-07-01DOI: 10.1109/IGARSS.2019.8898610
Hongtu Xie, Guoqian Wang, Jun Hu, K. Duan, Zengping Chen, Shiyou Xu, Yiquan Lin, Nannan Zhu, Bin Xi, D. An
The ultrahigh frequency ultra-wideband synthetic aperture radar (UHF UWB SAR) has the well foliage penetrating and high-resolution imaging, which can be used to detect the concealed area under the foliage in forests. This paper presents an airborne UHF UWB SAR experiment and imaging results. During the winter, an airborne campaign has been carried out in Shanxi Province in China, and the raw data was collected. In this experiment, the SAR system was integrated onboard a CESSNA-172 airplane. The antenna was fixed on the suspension arm of the right wing of the airplane, while the other part of the SAR system was placed on the back seat of this airplane. The experimental results have been obtained from the collected raw data, which proved the imaging performance of the airborne UHF UWB SAR system as well as the validity of the imaging method.
{"title":"Imaging Experiment of Airborne UHF Ultra-wideband Synthetic Aperture Radar","authors":"Hongtu Xie, Guoqian Wang, Jun Hu, K. Duan, Zengping Chen, Shiyou Xu, Yiquan Lin, Nannan Zhu, Bin Xi, D. An","doi":"10.1109/IGARSS.2019.8898610","DOIUrl":"https://doi.org/10.1109/IGARSS.2019.8898610","url":null,"abstract":"The ultrahigh frequency ultra-wideband synthetic aperture radar (UHF UWB SAR) has the well foliage penetrating and high-resolution imaging, which can be used to detect the concealed area under the foliage in forests. This paper presents an airborne UHF UWB SAR experiment and imaging results. During the winter, an airborne campaign has been carried out in Shanxi Province in China, and the raw data was collected. In this experiment, the SAR system was integrated onboard a CESSNA-172 airplane. The antenna was fixed on the suspension arm of the right wing of the airplane, while the other part of the SAR system was placed on the back seat of this airplane. The experimental results have been obtained from the collected raw data, which proved the imaging performance of the airborne UHF UWB SAR system as well as the validity of the imaging method.","PeriodicalId":13262,"journal":{"name":"IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium","volume":"4 1","pages":"2913-2916"},"PeriodicalIF":0.0,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75179405","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 is always the most concerned issue of radar imaging. Traditional radar systems, which obtain echo data using single platform, can achieve limited imaging resolution in a specific view angle. Distributed radar system, which expands multi-platform in space to obtain high imaging resolution by forming a large aperture, is a novel and hot research point. Matched filter, such as inverse fast Fourier transform (IFFT), is a conventional method to deal with distributed radar imaging. However, the method relies strictly on geometric configuration. In this paper, an iterative adaptive approach (IAA) based method is proposed to solve the problem of configuration adaptability. It can maintain the performance of matrix during the iteration. Then, the distributed radar system can keep high resolution in different geometric configurations. Simulation results verified the excellent performance of the proposed IAA-based imaging method.
{"title":"Improved Configuration Adaptability Based on IAA for Distributed Radar Imaging","authors":"Fanyun Xu, Deqing Mao, Yongchao Zhang, Yin Zhang, Yulin Huang, Jianyu Yang","doi":"10.1109/IGARSS.2019.8900294","DOIUrl":"https://doi.org/10.1109/IGARSS.2019.8900294","url":null,"abstract":"High resolution is always the most concerned issue of radar imaging. Traditional radar systems, which obtain echo data using single platform, can achieve limited imaging resolution in a specific view angle. Distributed radar system, which expands multi-platform in space to obtain high imaging resolution by forming a large aperture, is a novel and hot research point. Matched filter, such as inverse fast Fourier transform (IFFT), is a conventional method to deal with distributed radar imaging. However, the method relies strictly on geometric configuration. In this paper, an iterative adaptive approach (IAA) based method is proposed to solve the problem of configuration adaptability. It can maintain the performance of matrix during the iteration. Then, the distributed radar system can keep high resolution in different geometric configurations. Simulation results verified the excellent performance of the proposed IAA-based imaging method.","PeriodicalId":13262,"journal":{"name":"IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium","volume":"15 1","pages":"3562-3565"},"PeriodicalIF":0.0,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75421254","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 : 2019-07-01DOI: 10.1109/IGARSS.2019.8898018
T. Dey, Kousik Biswas, D. Chakravarty, A. Misra, B. Samanta
Small Baseline Subset (SBAS) technique is one of most accurate methods in Differential SAR interferometry (DInSAR) to estimate the surface deformation. In this paper, this technique has been applied on 23 X-band COSMO-SKyMed (CSK) datasets during 2011 – 2016 to get the annual subsidence rate over Jharia Coal Field (JCF), India. Validation of the subsidence result with ground water level data strongly indicates the predominant underground coal mining induced surface deformation over Jharia area.
{"title":"Spatio-Temporal Subsidence Estimation of Jharia Coal Field, India Using SBAS-Dinsar with Cosmo-Skymed Data","authors":"T. Dey, Kousik Biswas, D. Chakravarty, A. Misra, B. Samanta","doi":"10.1109/IGARSS.2019.8898018","DOIUrl":"https://doi.org/10.1109/IGARSS.2019.8898018","url":null,"abstract":"Small Baseline Subset (SBAS) technique is one of most accurate methods in Differential SAR interferometry (DInSAR) to estimate the surface deformation. In this paper, this technique has been applied on 23 X-band COSMO-SKyMed (CSK) datasets during 2011 – 2016 to get the annual subsidence rate over Jharia Coal Field (JCF), India. Validation of the subsidence result with ground water level data strongly indicates the predominant underground coal mining induced surface deformation over Jharia area.","PeriodicalId":13262,"journal":{"name":"IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium","volume":"1 1","pages":"2123-2126"},"PeriodicalIF":0.0,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75614819","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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