The ground moving target (GMT) model has been widely employed in modern coherent radar systems, such as the synthetic aperture radar (SAR) and the bistatic SAR (BiSAR). For the coherent radar systems, GMT imaging necessitates the compensation of the additional azimuth modulation without a priori knowledge of the GMT's motion parameters. That is to say, it is necessary to estimate the Doppler parameters of the GMT before the azimuth compression processing. For conventional estimation methods, such as the map drift (MD) method and the phase gradient auto-focus (PGA) method, a searching procedure is necessary and leads to an expensive computational cost. In this paper, a Doppler parameter estimation method based on mismatched compression is proposed. One advantage of this method is that it doesn't need the searching procedure. In addition, another advantage of this method is that both the Doppler centroid and the Doppler frequency rate of the GMT can be simultaneously estimated according to the relationships among the Doppler parameters, the positional offset and the boarding width of the mismatched imaging result. The theoretical analysis and numerical simulations validate that the proposed method works well with different signal to noise ratio.
{"title":"A Doppler parameter estimation method based on mismatched compression","authors":"Zhongyu Li, Junjie Wu, Yulin Huang, Zhichao Sun, Jianyu Yang, Xiaobo Yang","doi":"10.1109/IGARSS.2015.7326819","DOIUrl":"https://doi.org/10.1109/IGARSS.2015.7326819","url":null,"abstract":"The ground moving target (GMT) model has been widely employed in modern coherent radar systems, such as the synthetic aperture radar (SAR) and the bistatic SAR (BiSAR). For the coherent radar systems, GMT imaging necessitates the compensation of the additional azimuth modulation without a priori knowledge of the GMT's motion parameters. That is to say, it is necessary to estimate the Doppler parameters of the GMT before the azimuth compression processing. For conventional estimation methods, such as the map drift (MD) method and the phase gradient auto-focus (PGA) method, a searching procedure is necessary and leads to an expensive computational cost. In this paper, a Doppler parameter estimation method based on mismatched compression is proposed. One advantage of this method is that it doesn't need the searching procedure. In addition, another advantage of this method is that both the Doppler centroid and the Doppler frequency rate of the GMT can be simultaneously estimated according to the relationships among the Doppler parameters, the positional offset and the boarding width of the mismatched imaging result. The theoretical analysis and numerical simulations validate that the proposed method works well with different signal to noise ratio.","PeriodicalId":125717,"journal":{"name":"2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS)","volume":"95 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114201185","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 : 2015-07-26DOI: 10.1109/IGARSS.2015.7326242
Yingying Jing, Jiancheng Shi, Tianxing Wang
In recent years, global warming caused by increased atmospheric CO2 has greatly drawn widespread attention from the public. Although satellite observations and model-simulation offer us two effective approaches to monitor and assess the global atmospheric CO2, quantification of the differences between these two different CO2 data is not fully investigated yet. In this paper, these CO2 products including satellite observations and model-simulation are inter-compared in terms of magnitude and their spatiotemporal distributions. The results reveal that these CO2 data from different data source show a good agreement all over the world, whereas many discrepancies still exist between satellite observations and model-simulation, especially in the Northern Sphere.
{"title":"Evaluation and comparison of atmospheric CO2 concentrations from models and satellite retrievals","authors":"Yingying Jing, Jiancheng Shi, Tianxing Wang","doi":"10.1109/IGARSS.2015.7326242","DOIUrl":"https://doi.org/10.1109/IGARSS.2015.7326242","url":null,"abstract":"In recent years, global warming caused by increased atmospheric CO2 has greatly drawn widespread attention from the public. Although satellite observations and model-simulation offer us two effective approaches to monitor and assess the global atmospheric CO2, quantification of the differences between these two different CO2 data is not fully investigated yet. In this paper, these CO2 products including satellite observations and model-simulation are inter-compared in terms of magnitude and their spatiotemporal distributions. The results reveal that these CO2 data from different data source show a good agreement all over the world, whereas many discrepancies still exist between satellite observations and model-simulation, especially in the Northern Sphere.","PeriodicalId":125717,"journal":{"name":"2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114228104","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 : 2015-07-26DOI: 10.1109/IGARSS.2015.7326595
B. Adriano, E. Mas, S. Koshimura, H. Gokon, Wen Liu, M. Matsuoka
In this study, a practical methodology was presented to map damaged buildings using high resolution synthetic aperture radar (SAR) images and post-event building damage data from the 2013 Super Typhoon Haiyan, in Tacloban city, the Philippines. To detect destroyed structures, we focused on the changes in the radar signal within footprints of buildings between pre- and post-event SAR images. The method was tested using a 1.0 m resolution COSMO-SkyMed SAR images taken over Tacloban city, the Philippines. The method proves, with 73% accuracy in this case, to be suitable for estimating destroyed buildings.
{"title":"Developing a method for urban damage mapping using radar signatures of building footprint in SAR imagery: A case study after the 2013 Super Typhoon Haiyan","authors":"B. Adriano, E. Mas, S. Koshimura, H. Gokon, Wen Liu, M. Matsuoka","doi":"10.1109/IGARSS.2015.7326595","DOIUrl":"https://doi.org/10.1109/IGARSS.2015.7326595","url":null,"abstract":"In this study, a practical methodology was presented to map damaged buildings using high resolution synthetic aperture radar (SAR) images and post-event building damage data from the 2013 Super Typhoon Haiyan, in Tacloban city, the Philippines. To detect destroyed structures, we focused on the changes in the radar signal within footprints of buildings between pre- and post-event SAR images. The method was tested using a 1.0 m resolution COSMO-SkyMed SAR images taken over Tacloban city, the Philippines. The method proves, with 73% accuracy in this case, to be suitable for estimating destroyed buildings.","PeriodicalId":125717,"journal":{"name":"2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS)","volume":"102 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114355705","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 : 2015-07-26DOI: 10.1109/IGARSS.2015.7326485
Yuan-Qi Li, M. Xia
A new time-reversal imaging method based on the time focusing is presented. The time focusing behavior of the retransmitted time-reversal back-propagating waves from the time-reversal mirror (TRM) at a single target position is extended to multi target positions by using a grouping technique that divides the receiving array as a group of sub-arrays. The number of targets can exceed that of either the transmitters or the receivers. Locally normalized schemes can be employed to favor farer or weaker targets that may be swamped by nearer or stronger targets.
{"title":"Target location based on time focusing of time-reveral retransmitting signals","authors":"Yuan-Qi Li, M. Xia","doi":"10.1109/IGARSS.2015.7326485","DOIUrl":"https://doi.org/10.1109/IGARSS.2015.7326485","url":null,"abstract":"A new time-reversal imaging method based on the time focusing is presented. The time focusing behavior of the retransmitted time-reversal back-propagating waves from the time-reversal mirror (TRM) at a single target position is extended to multi target positions by using a grouping technique that divides the receiving array as a group of sub-arrays. The number of targets can exceed that of either the transmitters or the receivers. Locally normalized schemes can be employed to favor farer or weaker targets that may be swamped by nearer or stronger targets.","PeriodicalId":125717,"journal":{"name":"2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS)","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114372367","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 : 2015-07-26DOI: 10.1109/IGARSS.2015.7325812
Jun Liu, Shuguang Liu
Since the properties of temporal and spatial complexity and mass diversity that remote sensing image data owns, remote sensing image retrieval becomes an international advanced frontier scientific issue in remote sensing. Content-based image retrieval technology is currently widely used; however, the difference between low-level features and high-level semantics, named semantic gap, becomes a difficult while important issue for remote sensing image retrieval. In this paper, a novel semantic retrieval method for remote sensing images using association rules mining is presented. Unlike the traditional content-based image retrieval methods, association rules are mined and used to express the semantic information of images instead of low-level features. The original image is firstly segmented into many objects; and then the classified association rules between the properties of objects are mined and transformed to semantic information by semantic annotation method; finally the semantic retrieval is achieved using the similarity measurement approach. The experimental results indicate that the proposed method can provide better retrieval performance than the existing content-based image retrieval methods.
{"title":"Semantic retrieval for remote sensing images using association rules mining","authors":"Jun Liu, Shuguang Liu","doi":"10.1109/IGARSS.2015.7325812","DOIUrl":"https://doi.org/10.1109/IGARSS.2015.7325812","url":null,"abstract":"Since the properties of temporal and spatial complexity and mass diversity that remote sensing image data owns, remote sensing image retrieval becomes an international advanced frontier scientific issue in remote sensing. Content-based image retrieval technology is currently widely used; however, the difference between low-level features and high-level semantics, named semantic gap, becomes a difficult while important issue for remote sensing image retrieval. In this paper, a novel semantic retrieval method for remote sensing images using association rules mining is presented. Unlike the traditional content-based image retrieval methods, association rules are mined and used to express the semantic information of images instead of low-level features. The original image is firstly segmented into many objects; and then the classified association rules between the properties of objects are mined and transformed to semantic information by semantic annotation method; finally the semantic retrieval is achieved using the similarity measurement approach. The experimental results indicate that the proposed method can provide better retrieval performance than the existing content-based image retrieval methods.","PeriodicalId":125717,"journal":{"name":"2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS)","volume":"136 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114535747","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 : 2015-07-26DOI: 10.1109/IGARSS.2015.7325909
R. Beauchamp, V. Chandrasekar, M. Vega
The polarimetric radar parameter, linear depolarization ratio (LDR), provides microphysical insight into a scattering volume, particularly for mixed-phase and ice particles. A new estimator for improved estimation of LDR is presented. The NASA dual-frequency, dual-polarization, Doppler radar (D3R), which was recently upgraded to support operational linear depolarization ratio observations, was used as a testbed for evaluation of the new estimator. With D3R's Ku-band observations, the new LDR estimator is compared to conventional estimators and it is demonstrated that the new estimator is insensitive to attenuation, a number of radar system biases, and has increased immunity to noise for low SNR observations.
{"title":"NASA D3R linear depolarization ratio observations and a new estimation technique","authors":"R. Beauchamp, V. Chandrasekar, M. Vega","doi":"10.1109/IGARSS.2015.7325909","DOIUrl":"https://doi.org/10.1109/IGARSS.2015.7325909","url":null,"abstract":"The polarimetric radar parameter, linear depolarization ratio (LDR), provides microphysical insight into a scattering volume, particularly for mixed-phase and ice particles. A new estimator for improved estimation of LDR is presented. The NASA dual-frequency, dual-polarization, Doppler radar (D3R), which was recently upgraded to support operational linear depolarization ratio observations, was used as a testbed for evaluation of the new estimator. With D3R's Ku-band observations, the new LDR estimator is compared to conventional estimators and it is demonstrated that the new estimator is insensitive to attenuation, a number of radar system biases, and has increased immunity to noise for low SNR observations.","PeriodicalId":125717,"journal":{"name":"2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121500289","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 : 2015-07-26DOI: 10.1109/IGARSS.2015.7325855
X. Wen, Hui Lu, Chengwei Li
In this study, we inter-compared the spatial-temporal characteristics of SMOS and AMSR-E soil moisture products over Mongolia plateau. The results show that in temporal scale, the standard deviation of SMOS soil moisture data is higher than JAXA and ECMWF products, comparable to the in situ observation. To demonstrate the spatial variation of SMOS and JAXA soil moisture products, we first defined the smoothness index (SSI). The mean and maximum value of SSI of SMOS are much higher than those of JAXA and ECMWF for both daily and monthly soil moisture products. The mean value of SSI of daily SMOS products is 1.284, while that of JAXA and ECMWF is 0.010 and 5.010@10-4, respectively. For monthly products, the mean value of SSI of SMOS is 0.264, while the value of JAXA is 0.004, and 3.753*10-4 for ECMWF. Further, we counted the SSI of SMOS and JAXA TB, and the SSI mean value of SMOS TB is higher than that of JAXA TB for both daily and monthly time scale. It indicates that the big uncertainty of SMOS soil moisture products may raises from the unstable TB observation, which is highly contaminated by RFI and even cannot be removed at monthly scale.
{"title":"An intercomparison of the spatial-temporal characteristics of SMOS and AMSR-E soil moisture products over Mongolia plateau","authors":"X. Wen, Hui Lu, Chengwei Li","doi":"10.1109/IGARSS.2015.7325855","DOIUrl":"https://doi.org/10.1109/IGARSS.2015.7325855","url":null,"abstract":"In this study, we inter-compared the spatial-temporal characteristics of SMOS and AMSR-E soil moisture products over Mongolia plateau. The results show that in temporal scale, the standard deviation of SMOS soil moisture data is higher than JAXA and ECMWF products, comparable to the in situ observation. To demonstrate the spatial variation of SMOS and JAXA soil moisture products, we first defined the smoothness index (SSI). The mean and maximum value of SSI of SMOS are much higher than those of JAXA and ECMWF for both daily and monthly soil moisture products. The mean value of SSI of daily SMOS products is 1.284, while that of JAXA and ECMWF is 0.010 and 5.010@10-4, respectively. For monthly products, the mean value of SSI of SMOS is 0.264, while the value of JAXA is 0.004, and 3.753*10-4 for ECMWF. Further, we counted the SSI of SMOS and JAXA TB, and the SSI mean value of SMOS TB is higher than that of JAXA TB for both daily and monthly time scale. It indicates that the big uncertainty of SMOS soil moisture products may raises from the unstable TB observation, which is highly contaminated by RFI and even cannot be removed at monthly scale.","PeriodicalId":125717,"journal":{"name":"2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS)","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121532457","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}
A novel pansharpening method is proposed for creating a fused image of high spatial and spectral resolutions through merging a panchromatic (PAN) image with a multispectral (MS) image. To replace the patch pairs sampled from the images directly as the dictionary pairs, a joint learning model is proposed to learn a pair of compact dictionaries. Meanwhile, instead of restricting the coding coefficients of low resolution (LR) MS and high resolution (HR) MS image patches to be equal, ridge regression model is employed to describe their relation. Then, the fused MS image is calculated by combining the mapped sparse coefficients and the dictionary for the HR MS image. By comparing with some well-known methods in terms of several universal quality evaluation indexes, the simulated experimental results demonstrate the superiority of our method.
{"title":"Joint dictionary learning with ridge regression for pansharpening","authors":"Songze Tang, Liang Xiao, Bushra Naz, Pengfei Liu, Yufeng Chen","doi":"10.1109/IGARSS.2015.7325838","DOIUrl":"https://doi.org/10.1109/IGARSS.2015.7325838","url":null,"abstract":"A novel pansharpening method is proposed for creating a fused image of high spatial and spectral resolutions through merging a panchromatic (PAN) image with a multispectral (MS) image. To replace the patch pairs sampled from the images directly as the dictionary pairs, a joint learning model is proposed to learn a pair of compact dictionaries. Meanwhile, instead of restricting the coding coefficients of low resolution (LR) MS and high resolution (HR) MS image patches to be equal, ridge regression model is employed to describe their relation. Then, the fused MS image is calculated by combining the mapped sparse coefficients and the dictionary for the HR MS image. By comparing with some well-known methods in terms of several universal quality evaluation indexes, the simulated experimental results demonstrate the superiority of our method.","PeriodicalId":125717,"journal":{"name":"2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS)","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121540288","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 : 2015-07-26DOI: 10.1109/IGARSS.2015.7326972
P. Lombardo, D. Pastina, Fabrizio Turin
Multichannel space-based SAR systems that exploit receiving antenna sub-arrays displaced in the along track-direction have been shown to provide the capability to detect ground moving targets and potentially estimate their radial motion velocity, thus making possible their correct relocation inside the SAR image of the background. However, the use of only two receiving channels to contain cost, mass and data-rate, imposes limitations in terms of potentiality of joint clutter cancellation and target velocity estimation. To overcome this problem we propose a full MIMO SAR scheme obtained by transmitting nearly orthogonal waveforms with the two sub-arrays achieved from a single antenna aperture, and receiving with the two sub-arrays. A complete processing chain for the joint detection, imaging and radial speed estimation is presented and the performance of the proposed MIMO system is deeply investigated and compared to the performance of conventional multichannel systems to assess the relative merits and drawbacks.
{"title":"Detection of ground moving targets via MIMO SAR systems","authors":"P. Lombardo, D. Pastina, Fabrizio Turin","doi":"10.1109/IGARSS.2015.7326972","DOIUrl":"https://doi.org/10.1109/IGARSS.2015.7326972","url":null,"abstract":"Multichannel space-based SAR systems that exploit receiving antenna sub-arrays displaced in the along track-direction have been shown to provide the capability to detect ground moving targets and potentially estimate their radial motion velocity, thus making possible their correct relocation inside the SAR image of the background. However, the use of only two receiving channels to contain cost, mass and data-rate, imposes limitations in terms of potentiality of joint clutter cancellation and target velocity estimation. To overcome this problem we propose a full MIMO SAR scheme obtained by transmitting nearly orthogonal waveforms with the two sub-arrays achieved from a single antenna aperture, and receiving with the two sub-arrays. A complete processing chain for the joint detection, imaging and radial speed estimation is presented and the performance of the proposed MIMO system is deeply investigated and compared to the performance of conventional multichannel systems to assess the relative merits and drawbacks.","PeriodicalId":125717,"journal":{"name":"2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS)","volume":"10 3","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114028157","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}
Scene classification for high-resolution remotely sensed imagery have been widely investigated in recent years. However, there is few public, widely accepted and large scale dataset for benchmarking different methods. This paper presents a new and large dataset consisting of 5000 high-resolution remote sensing images which is manually labeled in 20 semantic classes for scene classification. Each class includes more than 200 image samples with different appearances. Some classic classification algorithms are compared on this dataset. To our knowledge, this work is the first time to give a public benchmark dataset at this size on the problem of scene classification in high-resolution remote sensing imagery, and give comparative results and analysis of various classic classification algorithms.
{"title":"A benchmark for scene classification of high spatial resolution remote sensing imagery","authors":"Jingwen Hu, Tianbi Jiang, Xinyi Tong, Gui-Song Xia, Liangpei Zhang","doi":"10.1109/IGARSS.2015.7326956","DOIUrl":"https://doi.org/10.1109/IGARSS.2015.7326956","url":null,"abstract":"Scene classification for high-resolution remotely sensed imagery have been widely investigated in recent years. However, there is few public, widely accepted and large scale dataset for benchmarking different methods. This paper presents a new and large dataset consisting of 5000 high-resolution remote sensing images which is manually labeled in 20 semantic classes for scene classification. Each class includes more than 200 image samples with different appearances. Some classic classification algorithms are compared on this dataset. To our knowledge, this work is the first time to give a public benchmark dataset at this size on the problem of scene classification in high-resolution remote sensing imagery, and give comparative results and analysis of various classic classification algorithms.","PeriodicalId":125717,"journal":{"name":"2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS)","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124372931","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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