Pub Date : 2019-07-01DOI: 10.1109/IGARSS.2019.8899194
Rui Li, Jiancheng Shi, T. Zhao, Jinmei Pan
The knowledge and understanding of intra- and inter annual characteristics of canal water is crucial for agricultural water management. The narrow shape of canal greatly limits the application of moderate-resolution remote sensing technologies. Based on newly available Sentinel-2 Multispectral Instrument (MSI) imagery with frequent revisit and higher spatial resolution, we identified variation of water/bank boundary by Roberts, Sobel, Prewitt, Laplacian of Gaussian and Canny 5 edge detectors and furtherly compared the water width results with estimation by ground measurement. The preliminary results show that all detectors can successfully monitor seasonal variation of canal water surface. Canny detector is most stable among 5 methods for time series monitoring, although overestimated the water width during dry period. Our methods and results reveal the great potential of Sentinel-2 imagery for canal water utilization and irrigation management.
{"title":"Water Surface Monitoring of Qingtongxia West Main Canal by Sentinel-2 Satellite Observations","authors":"Rui Li, Jiancheng Shi, T. Zhao, Jinmei Pan","doi":"10.1109/IGARSS.2019.8899194","DOIUrl":"https://doi.org/10.1109/IGARSS.2019.8899194","url":null,"abstract":"The knowledge and understanding of intra- and inter annual characteristics of canal water is crucial for agricultural water management. The narrow shape of canal greatly limits the application of moderate-resolution remote sensing technologies. Based on newly available Sentinel-2 Multispectral Instrument (MSI) imagery with frequent revisit and higher spatial resolution, we identified variation of water/bank boundary by Roberts, Sobel, Prewitt, Laplacian of Gaussian and Canny 5 edge detectors and furtherly compared the water width results with estimation by ground measurement. The preliminary results show that all detectors can successfully monitor seasonal variation of canal water surface. Canny detector is most stable among 5 methods for time series monitoring, although overestimated the water width during dry period. Our methods and results reveal the great potential of Sentinel-2 imagery for canal water utilization and irrigation management.","PeriodicalId":13262,"journal":{"name":"IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium","volume":"65 1","pages":"4423-4426"},"PeriodicalIF":0.0,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72578508","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.2019.8898498
M. Kotiranta, R. Gomez, G. Nedoluha, N. Kämpfer, A. Murk
A highly sensitive receiver for the microwave ozone profiling instrument MOPI 5 has been developed. The receiver allows the simultaneous measurement of the pressure-broadened line shape of the thermally-excited rotational emission lines of middle atmospheric ozone and carbon monoxide at 110.836 GHz and 115.271 GHz, respectively. It exhibits a single side band noise temperature of 550-625 K. This paper describes the receiver design and compares the results of the first spectral line measurements obtained using two different digital fast Fourier transform spectrometer back-ends.
{"title":"Receiver Development for the Microwave Ozone Profiling Instrument MOPI 5","authors":"M. Kotiranta, R. Gomez, G. Nedoluha, N. Kämpfer, A. Murk","doi":"10.1109/IGARSS.2019.8898498","DOIUrl":"https://doi.org/10.1109/IGARSS.2019.8898498","url":null,"abstract":"A highly sensitive receiver for the microwave ozone profiling instrument MOPI 5 has been developed. The receiver allows the simultaneous measurement of the pressure-broadened line shape of the thermally-excited rotational emission lines of middle atmospheric ozone and carbon monoxide at 110.836 GHz and 115.271 GHz, respectively. It exhibits a single side band noise temperature of 550-625 K. This paper describes the receiver design and compares the results of the first spectral line measurements obtained using two different digital fast Fourier transform spectrometer back-ends.","PeriodicalId":13262,"journal":{"name":"IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium","volume":"16 1","pages":"8952-8955"},"PeriodicalIF":0.0,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74556749","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.2019.8899320
Kun Yang
The frequent floods and droughts in China have seriously affected the development of the national economy and people’s lives. Remote sensing can rapidly and extensively monitor, assess and warn the flood and drought disasters. This paper reviewed the progresses of Chinese flood and drought disaster monitoring by using remote sensing, described the development and innovation of models and methods in flood and drought disaster monitoring with remote sensing technology, discussed the challenges of remote sensing monitoring of flood and drought disasters, and finally, explored the development tendency of remote sensing monitoring of flood and drought disasters in the new era.
{"title":"Progress, Challenge and Prospect for Remote Sensing Monitoring of Flood and Drought Disasters in China","authors":"Kun Yang","doi":"10.1109/IGARSS.2019.8899320","DOIUrl":"https://doi.org/10.1109/IGARSS.2019.8899320","url":null,"abstract":"The frequent floods and droughts in China have seriously affected the development of the national economy and people’s lives. Remote sensing can rapidly and extensively monitor, assess and warn the flood and drought disasters. This paper reviewed the progresses of Chinese flood and drought disaster monitoring by using remote sensing, described the development and innovation of models and methods in flood and drought disaster monitoring with remote sensing technology, discussed the challenges of remote sensing monitoring of flood and drought disasters, and finally, explored the development tendency of remote sensing monitoring of flood and drought disasters in the new era.","PeriodicalId":13262,"journal":{"name":"IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium","volume":"3 1","pages":"4280-4283"},"PeriodicalIF":0.0,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74860269","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.2019.8899789
Mei Sun, Yanchen Bo, Lei Cui, R. Li
Optical data plays an important role in various remote sensing applications. However, cloud and cloud shadow contamination reduce the availability of optical data, especially in tropical regions. Accurate identification of cloud and cloud shadow is an essential step in optical image preprocessing. The Function of Mask (FMASK) [1] is one of the most widely used cloud and cloud shadow detection methods. In view of the problems of some thin clouds and cloud shadows omission errors in tropical regions of FMASK, we develop an improved FMASK algorithm in tropical regions from the following two aspects: (1) Cloud detection: Firstly, the parameters and thresholds of FMASK are adjusted to generate the basic cloud layer; Secondly, the cloud index layer is calculated based on the bright features of clouds; Then, combining the temporal randomness and spectral characteristics of cloud, other bright objects are excluded and thin clouds are retained. (2) Cloud shadow detection: Firstly, the dark features of cloud shadows are mainly used to detect the basic cloud shadow layer; Secondly, combining the spectral and randomness characteristics of cloud shadow to avoid the interference of other dark objects. We randomly selected three experimental areas in tropical regions to verify the proposed algorithm developed in this paper. Through comparing and evaluating the accuracy of the clouds and cloud shadows mask generated based on the method in this paper with real samples drawn manually, the experiment results show that the average overall precision of clouds and cloud shadows mask generated based on the algorithm in this paper exceeding 80%. This improved FMASK algorithm improves the accuracy of clouds and cloud shadows detection in several tropical regions for Landsat images.
光学数据在各种遥感应用中发挥着重要作用。然而，云和云阴影污染降低了光学数据的可用性，特别是在热带地区。云与云阴影的准确识别是光学图像预处理的重要环节。FMASK (Function of Mask)[1]是目前应用最广泛的云与云阴影检测方法之一。针对FMASK在热带地区存在一些薄云和云影遗漏错误的问题，我们从以下两个方面开发了一种改进的热带地区FMASK算法:(1)云检测:首先，调整FMASK的参数和阈值，生成基本云层;其次，根据云的亮度特征计算云指数层;然后，结合云的时间随机性和光谱特性，排除其他明亮物体，保留薄云。(2)云阴影检测:首先，主要利用云阴影的暗特征对基本云阴影层进行检测;其次，结合云层阴影的光谱和随机性特点，避免其他黑暗物体的干扰。我们在热带地区随机选择了三个实验区来验证本文提出的算法。通过将本文方法生成的云和云阴影掩模与人工绘制的真实样本的精度进行对比和评价，实验结果表明，基于本文算法生成的云和云阴影掩模的平均整体精度超过80%。改进的FMASK算法提高了陆地卫星图像在热带地区云和云影检测的精度。
{"title":"An Improved Fmask Algorithm in Tropical Regions for Landsat Images","authors":"Mei Sun, Yanchen Bo, Lei Cui, R. Li","doi":"10.1109/IGARSS.2019.8899789","DOIUrl":"https://doi.org/10.1109/IGARSS.2019.8899789","url":null,"abstract":"Optical data plays an important role in various remote sensing applications. However, cloud and cloud shadow contamination reduce the availability of optical data, especially in tropical regions. Accurate identification of cloud and cloud shadow is an essential step in optical image preprocessing. The Function of Mask (FMASK) [1] is one of the most widely used cloud and cloud shadow detection methods. In view of the problems of some thin clouds and cloud shadows omission errors in tropical regions of FMASK, we develop an improved FMASK algorithm in tropical regions from the following two aspects: (1) Cloud detection: Firstly, the parameters and thresholds of FMASK are adjusted to generate the basic cloud layer; Secondly, the cloud index layer is calculated based on the bright features of clouds; Then, combining the temporal randomness and spectral characteristics of cloud, other bright objects are excluded and thin clouds are retained. (2) Cloud shadow detection: Firstly, the dark features of cloud shadows are mainly used to detect the basic cloud shadow layer; Secondly, combining the spectral and randomness characteristics of cloud shadow to avoid the interference of other dark objects. We randomly selected three experimental areas in tropical regions to verify the proposed algorithm developed in this paper. Through comparing and evaluating the accuracy of the clouds and cloud shadows mask generated based on the method in this paper with real samples drawn manually, the experiment results show that the average overall precision of clouds and cloud shadows mask generated based on the algorithm in this paper exceeding 80%. This improved FMASK algorithm improves the accuracy of clouds and cloud shadows detection in several tropical regions for Landsat images.","PeriodicalId":13262,"journal":{"name":"IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium","volume":"45 1","pages":"1562-1565"},"PeriodicalIF":0.0,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78746527","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.2019.8898814
É. Anterrieu, Josianne Costerate, B. Palacin, R. Rodriguez-Suquet, T. Tournier, T. Decoopman, Romain Caujolle, N. Jeannin, L. Costes, F. Payot, N. Rodríguez-Fernández, B. Rougé, F. Cabot, P. Richaume, A. Khazâal, Y. Kerr, J. Morel, M. Colom
The Soil Moisture and Ocean Salinity (SMOS) satellite has provided, for the very first time, systematic passive L-band (1420−1427 MHz) measurements from space with a spatial resolution of ~50 Km. This contribution presents preliminary results of studies conducted for a High Resolution (HR) follow-on mission. The SMOS-HR project is currently undergoing a Phase 0 study by the French space agency. The goal is to ensure continuity of L-band measurements while increasing the spatial resolution to ~10 Km without degrading the radiometric sensitivity and keeping the revisit time of 3 days unchanged.
{"title":"Preliminary System Studies on a High-Resolution SMOS Follow-On: SMOS-HR","authors":"É. Anterrieu, Josianne Costerate, B. Palacin, R. Rodriguez-Suquet, T. Tournier, T. Decoopman, Romain Caujolle, N. Jeannin, L. Costes, F. Payot, N. Rodríguez-Fernández, B. Rougé, F. Cabot, P. Richaume, A. Khazâal, Y. Kerr, J. Morel, M. Colom","doi":"10.1109/IGARSS.2019.8898814","DOIUrl":"https://doi.org/10.1109/IGARSS.2019.8898814","url":null,"abstract":"The Soil Moisture and Ocean Salinity (SMOS) satellite has provided, for the very first time, systematic passive L-band (1420−1427 MHz) measurements from space with a spatial resolution of ~50 Km. This contribution presents preliminary results of studies conducted for a High Resolution (HR) follow-on mission. The SMOS-HR project is currently undergoing a Phase 0 study by the French space agency. The goal is to ensure continuity of L-band measurements while increasing the spatial resolution to ~10 Km without degrading the radiometric sensitivity and keeping the revisit time of 3 days unchanged.","PeriodicalId":13262,"journal":{"name":"IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium","volume":"18 1","pages":"8451-8454"},"PeriodicalIF":0.0,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78773219","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.2019.8898021
R. Hänsch, O. Hellwich
The deployment of numerous air- and space-borne remote sensing sensors as well as new data policies led to a tremendous increase of available data. While methods such as neural networks are trained by online or batch processing, i.e. keeping only parts of the data in the memory, other methods such as Random Forests require offline processing, i.e. keeping all data in the memory of the computer. The latter are therefore often trained on a small subset of a larger data set that is hoped to be representative instead of exploiting the information contained in all samples. This paper shows that Random Forests can be trained by batch processing too making their application to large data sets feasible without further constraints. The benefits of this training scheme are illustrated for the use case of land-use classification from PolSAR imagery.
{"title":"Online Random Forests For Large-Scale Land-Use Classification From Polarimetric Sar Images","authors":"R. Hänsch, O. Hellwich","doi":"10.1109/IGARSS.2019.8898021","DOIUrl":"https://doi.org/10.1109/IGARSS.2019.8898021","url":null,"abstract":"The deployment of numerous air- and space-borne remote sensing sensors as well as new data policies led to a tremendous increase of available data. While methods such as neural networks are trained by online or batch processing, i.e. keeping only parts of the data in the memory, other methods such as Random Forests require offline processing, i.e. keeping all data in the memory of the computer. The latter are therefore often trained on a small subset of a larger data set that is hoped to be representative instead of exploiting the information contained in all samples. This paper shows that Random Forests can be trained by batch processing too making their application to large data sets feasible without further constraints. The benefits of this training scheme are illustrated for the use case of land-use classification from PolSAR imagery.","PeriodicalId":13262,"journal":{"name":"IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium","volume":"15 1","pages":"5808-5811"},"PeriodicalIF":0.0,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75928384","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.2019.8900422
F. Léger, F. Birol, F. Niño, M. Passaro, F. Marti, A. Cazenave
Climate change is likely to worsen many of the problems already facing coastal environments. Sea level change is one of the main threats to coastal areas. Improving its observation is essential to better understand and predict the behaviour of the coastal ocean. Altimetry provides a unique set of long-term measurements to characterize the evolution of sea level variability from the open to the coastal ocean.The X-TRACK processing chain has been developed in order to recover as much altimetry data as possible in coastal areas. X-TRACK has now become a multi-mission altimetry product covering all the coastal oceans. It is produced by the CTOH (Center of Topography of the Ocean and the Hydrosphere). Recently, the Level 2 ALES (Adaptive Leading Edge Subwaveform) retracked product has been included in X-TRACK, as well as the best altimetric corrections available, thus merging the most recent advances in coastal altimetry into in a new high resolution (20 Hz ~ 350 m Level 3 alongtrack product made available for the research community.
气候变化可能会加剧沿海环境已经面临的许多问题。海平面变化是沿海地区面临的主要威胁之一。改善其观测对更好地了解和预测沿海海洋的行为至关重要。测高提供了一套独特的长期测量资料，以描述从开放海域到沿海海域海平面变化的演变特征。开发X-TRACK处理链是为了在沿海地区恢复尽可能多的测高数据。X-TRACK现已成为覆盖所有沿海海洋的多任务测高产品。它是由CTOH(海洋和水圈地形中心)产生的。最近，2级ALES(自适应前沿子波形)重新跟踪产品已包含在X-TRACK中，以及可用的最佳高程校正，从而将沿海高程测量的最新进展合并为新的高分辨率(20 Hz ~ 350 m) 3级跟踪产品，可供研究界使用。
{"title":"X-Track/Ales Regional Altimeter Product for Coastal Application: Toward a New Multi-Mission Altimetry Product at High Resolution","authors":"F. Léger, F. Birol, F. Niño, M. Passaro, F. Marti, A. Cazenave","doi":"10.1109/IGARSS.2019.8900422","DOIUrl":"https://doi.org/10.1109/IGARSS.2019.8900422","url":null,"abstract":"Climate change is likely to worsen many of the problems already facing coastal environments. Sea level change is one of the main threats to coastal areas. Improving its observation is essential to better understand and predict the behaviour of the coastal ocean. Altimetry provides a unique set of long-term measurements to characterize the evolution of sea level variability from the open to the coastal ocean.The X-TRACK processing chain has been developed in order to recover as much altimetry data as possible in coastal areas. X-TRACK has now become a multi-mission altimetry product covering all the coastal oceans. It is produced by the CTOH (Center of Topography of the Ocean and the Hydrosphere). Recently, the Level 2 ALES (Adaptive Leading Edge Subwaveform) retracked product has been included in X-TRACK, as well as the best altimetric corrections available, thus merging the most recent advances in coastal altimetry into in a new high resolution (20 Hz ~ 350 m Level 3 alongtrack product made available for the research community.","PeriodicalId":13262,"journal":{"name":"IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium","volume":"36 1","pages":"8271-8274"},"PeriodicalIF":0.0,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76007205","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.2019.8897806
Bex Dunn, L. Lymburner, V. Newey, A. Hicks, H. Carey
Combining observations of open water, wet vegetation, and vegetation fractional cover allows us to observe the spatiotemporal behaviour of wetlands. We developed a Wetlands Insight Tool (WIT) using Analysis-Ready Data available through Digital Earth Australia that combines Water Observations from Space (WOfS), the Tasseled Cap Wetness Transform (TCW) and Fractional Cover into an wetland summary. We demonstrate the tool on three Australian wetlands, showing changes in water and vegetation from bush fires, sand mining and planned recovery.
{"title":"Developing a Tool for Wetland Characterization Using Fractional Cover, Tasseled Cap Wetness And Water Observations From Space","authors":"Bex Dunn, L. Lymburner, V. Newey, A. Hicks, H. Carey","doi":"10.1109/IGARSS.2019.8897806","DOIUrl":"https://doi.org/10.1109/IGARSS.2019.8897806","url":null,"abstract":"Combining observations of open water, wet vegetation, and vegetation fractional cover allows us to observe the spatiotemporal behaviour of wetlands. We developed a Wetlands Insight Tool (WIT) using Analysis-Ready Data available through Digital Earth Australia that combines Water Observations from Space (WOfS), the Tasseled Cap Wetness Transform (TCW) and Fractional Cover into an wetland summary. We demonstrate the tool on three Australian wetlands, showing changes in water and vegetation from bush fires, sand mining and planned recovery.","PeriodicalId":13262,"journal":{"name":"IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium","volume":"28 1","pages":"6095-6097"},"PeriodicalIF":0.0,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75099905","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.2019.8899149
Samuel Prager, M. Moghaddam
In this paper we demonstrate the capability of an ultra-wideband software defined radar (SDRadar) implemented in commercial USRP SDR hardware to produce high-resolution images of sub-surface landmine-like targets. We formulate a half-space back-projection focusing algorithm for low-altitude nadir-looking airborne altimetric ground-penetrating SAR that accounts for dispersive and refractive effects of the air-ground interface. Performance of the SDRadar and focusing algorithm are shown in experimental results. This work has applications in the development of low-cost high-resolution UAV-based radar systems for landmine detection and other sub-surface imaging tasks.
{"title":"Application of Ultra-Wideband Synthesis in Software Defined Radar for UAV-based Landmine Detection","authors":"Samuel Prager, M. Moghaddam","doi":"10.1109/IGARSS.2019.8899149","DOIUrl":"https://doi.org/10.1109/IGARSS.2019.8899149","url":null,"abstract":"In this paper we demonstrate the capability of an ultra-wideband software defined radar (SDRadar) implemented in commercial USRP SDR hardware to produce high-resolution images of sub-surface landmine-like targets. We formulate a half-space back-projection focusing algorithm for low-altitude nadir-looking airborne altimetric ground-penetrating SAR that accounts for dispersive and refractive effects of the air-ground interface. Performance of the SDRadar and focusing algorithm are shown in experimental results. This work has applications in the development of low-cost high-resolution UAV-based radar systems for landmine detection and other sub-surface imaging tasks.","PeriodicalId":13262,"journal":{"name":"IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium","volume":"74 1","pages":"10115-10118"},"PeriodicalIF":0.0,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77386363","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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