Pub Date : 2015-07-26DOI: 10.1109/IGARSS.2015.7325954
A. Nielsen, Jacob S. Vestergaard
Canonical correlation analysis (CCA) is an established multi-variate statistical method for finding similarities between linear combinations of (normally two) sets of multivariate observations. In this contribution we replace (linear) correlation as the measure of association between the linear combinations with the information theoretical measure mutual information (MI). We term this type of analysis canonical information analysis (CIA). MI allows for the actual joint distribution of the variables involved and not just second order statistics. While CCA is ideal for Gaussian data, CIA facilitates analysis of variables with different genesis and therefore different statistical distributions and different modalities. As a proof of concept we give a toy example. We also give an example with one (weather radar based) variable in the one set and eight spectral bands of optical satellite data in the other set.
{"title":"Canonical analysis basedonmutual information","authors":"A. Nielsen, Jacob S. Vestergaard","doi":"10.1109/IGARSS.2015.7325954","DOIUrl":"https://doi.org/10.1109/IGARSS.2015.7325954","url":null,"abstract":"Canonical correlation analysis (CCA) is an established multi-variate statistical method for finding similarities between linear combinations of (normally two) sets of multivariate observations. In this contribution we replace (linear) correlation as the measure of association between the linear combinations with the information theoretical measure mutual information (MI). We term this type of analysis canonical information analysis (CIA). MI allows for the actual joint distribution of the variables involved and not just second order statistics. While CCA is ideal for Gaussian data, CIA facilitates analysis of variables with different genesis and therefore different statistical distributions and different modalities. As a proof of concept we give a toy example. We also give an example with one (weather radar based) variable in the one set and eight spectral bands of optical satellite data in the other set.","PeriodicalId":125717,"journal":{"name":"2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS)","volume":"20 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":"115612603","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.2015.7326309
Dingsheng Hu, A. Doulgeris, Xiaolan Qiu
This paper introduces a novel unsupervised estimator of equivalent number of looks (ENL) that can be applied to an arbitrary image. It avoids the assumption that homogeneous speckle will dominate the investigated image that is followed by current unsupervised ENL estimators but not always valid, especially for the complex SAR scenes with high mixture and texture. Incorporating the statistical properties of ENL data into an automatic segmentation method, we isolate the sub-class affected least by mixture and texture and suggest taking the mean value of this class as the final ENL estimate. The proposed estimator is evaluated in the experiments performed on simulated and real data from two very different sensors. It always gives better results than the other two existing methods and possesses greater adaptability.
{"title":"An unsupervised method for equivalent number of looks estimation in complex SAR scenes","authors":"Dingsheng Hu, A. Doulgeris, Xiaolan Qiu","doi":"10.1109/IGARSS.2015.7326309","DOIUrl":"https://doi.org/10.1109/IGARSS.2015.7326309","url":null,"abstract":"This paper introduces a novel unsupervised estimator of equivalent number of looks (ENL) that can be applied to an arbitrary image. It avoids the assumption that homogeneous speckle will dominate the investigated image that is followed by current unsupervised ENL estimators but not always valid, especially for the complex SAR scenes with high mixture and texture. Incorporating the statistical properties of ENL data into an automatic segmentation method, we isolate the sub-class affected least by mixture and texture and suggest taking the mean value of this class as the final ENL estimate. The proposed estimator is evaluated in the experiments performed on simulated and real data from two very different sensors. It always gives better results than the other two existing methods and possesses greater adaptability.","PeriodicalId":125717,"journal":{"name":"2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS)","volume":"21 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":"115618125","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.2015.7326158
M. Vakalopoulou, K. Karantzalos, N. Komodakis, N. Paragios
The automated man-made object detection and building extraction from single satellite images is, still, one of the most challenging tasks for various urban planning and monitoring engineering applications. To this end, in this paper we propose an automated building detection framework from very high resolution remote sensing data based on deep convolutional neural networks. The core of the developed method is based on a supervised classification procedure employing a very large training dataset. An MRF model is then responsible for obtaining the optimal labels regarding the detection of scene buildings. The experimental results and the performed quantitative validation indicate the quite promising potentials of the developed approach.
{"title":"Building detection in very high resolution multispectral data with deep learning features","authors":"M. Vakalopoulou, K. Karantzalos, N. Komodakis, N. Paragios","doi":"10.1109/IGARSS.2015.7326158","DOIUrl":"https://doi.org/10.1109/IGARSS.2015.7326158","url":null,"abstract":"The automated man-made object detection and building extraction from single satellite images is, still, one of the most challenging tasks for various urban planning and monitoring engineering applications. To this end, in this paper we propose an automated building detection framework from very high resolution remote sensing data based on deep convolutional neural networks. The core of the developed method is based on a supervised classification procedure employing a very large training dataset. An MRF model is then responsible for obtaining the optimal labels regarding the detection of scene buildings. The experimental results and the performed quantitative validation indicate the quite promising potentials of the developed approach.","PeriodicalId":125717,"journal":{"name":"2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS)","volume":"116 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":"123161316","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.2015.7326190
N. Baghdadi, M. Zribi, S. Paloscia, N. Verhoest, H. Lievens, F. Baup, F. Mattia
The objective of this paper is to extend the semi-empirical calibration of the backscattering Integral Equation Model (IEM) initially proposed for SAR data at C- and X-bands to SAR data at L band. A large dataset of radar signal and in situ measurements (soil moisture and surface roughness) over bare soil surfaces were used. A semi-empirical calibration of the IEM was performed at L band in replacing the correlation length derived from field experiments by a fitting parameter. Better agreement was observed between the backscattering coefficient provided by the SAR and that simulated by the calibrated version of the IEM.
{"title":"Semi-empirical calibration of the integral equation model for co-polarized L-band backscattering","authors":"N. Baghdadi, M. Zribi, S. Paloscia, N. Verhoest, H. Lievens, F. Baup, F. Mattia","doi":"10.1109/IGARSS.2015.7326190","DOIUrl":"https://doi.org/10.1109/IGARSS.2015.7326190","url":null,"abstract":"The objective of this paper is to extend the semi-empirical calibration of the backscattering Integral Equation Model (IEM) initially proposed for SAR data at C- and X-bands to SAR data at L band. A large dataset of radar signal and in situ measurements (soil moisture and surface roughness) over bare soil surfaces were used. A semi-empirical calibration of the IEM was performed at L band in replacing the correlation length derived from field experiments by a fitting parameter. Better agreement was observed between the backscattering coefficient provided by the SAR and that simulated by the calibrated version of the IEM.","PeriodicalId":125717,"journal":{"name":"2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS)","volume":"126 1 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":"116706333","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.2015.7325740
D. Calabrese, Flavia Carnevale, A. Croce, I. Rana, G. Spera, R. Venturini, C. Germani, F. Spadoni, F. Bagaglini, R. Roscigno, Luigi Corsano, S. Serva, M. Porfilio, G. F. D. Luca
COSMO Second Generation (CSG) system has been conceived, according to the requirements stated by ASI and I-MoD, at the twofold need of ensuring operational continuity to the currently operating “first generation” COSMO-SkyMed (CSK) spacecraft constellation, while achieving a generational step ahead in terms of functionality and performance. The improved quality of the imaging service is among the foremost characteristics of CSG (see [5]), providing the End Users with new/enhanced capabilities in terms of higher number of images and increased image quality (i.e. larger swath, and finer resolution) with respect to COSMO-SkyMed (first generation) spacecrafts currently in operation, along with additional capabilities (e.g. full polarimetric SAR acquisition mode). The greater operative versatility in system resources management is one of the key aspects of the design approach. Some examples are the satellite agility both at platform and antenna level, used to increase the density of acquisitions in a fixed region, the capability to adapt operative profiles to system environment (e.g. sun illumination, downlink scenarios), service request planning process not only function of the priority but also able to maximize the system resources exploitation.
{"title":"New concepts and innovative solutions of the COSMO-SkyMed “Seconda Generazione” system","authors":"D. Calabrese, Flavia Carnevale, A. Croce, I. Rana, G. Spera, R. Venturini, C. Germani, F. Spadoni, F. Bagaglini, R. Roscigno, Luigi Corsano, S. Serva, M. Porfilio, G. F. D. Luca","doi":"10.1109/IGARSS.2015.7325740","DOIUrl":"https://doi.org/10.1109/IGARSS.2015.7325740","url":null,"abstract":"COSMO Second Generation (CSG) system has been conceived, according to the requirements stated by ASI and I-MoD, at the twofold need of ensuring operational continuity to the currently operating “first generation” COSMO-SkyMed (CSK) spacecraft constellation, while achieving a generational step ahead in terms of functionality and performance. The improved quality of the imaging service is among the foremost characteristics of CSG (see [5]), providing the End Users with new/enhanced capabilities in terms of higher number of images and increased image quality (i.e. larger swath, and finer resolution) with respect to COSMO-SkyMed (first generation) spacecrafts currently in operation, along with additional capabilities (e.g. full polarimetric SAR acquisition mode). The greater operative versatility in system resources management is one of the key aspects of the design approach. Some examples are the satellite agility both at platform and antenna level, used to increase the density of acquisitions in a fixed region, the capability to adapt operative profiles to system environment (e.g. sun illumination, downlink scenarios), service request planning process not only function of the priority but also able to maximize the system resources exploitation.","PeriodicalId":125717,"journal":{"name":"2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS)","volume":"21 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":"120983360","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.2015.7326389
R. Rincon, T. Fatoyinbo, B. Osmanoglu, Seung-Kuk Lee, K. Ranson, Victor Marrero, M. Yeary
The second generation Digital Beamforming SAR (DBSAR-2) is a state-of-the-art airborne L-band radar being developed at the NASA Goddard Space Flight Center (GSFC). The instrument employs a 16-channel radar architecture characterized by multi-mode operation, software defined waveform generation, digital beamforming, and configurable radar parameters. The instrument has been design to support several disciplines in Earth and Planetary sciences. This technology seeks to establish the Next Generation SAR as a science instrument while setting a path future airborne and spaceborne SAR missions.
{"title":"Next generation Digital Beamforming Synthetic Aperture Radar (DBSAR-2)","authors":"R. Rincon, T. Fatoyinbo, B. Osmanoglu, Seung-Kuk Lee, K. Ranson, Victor Marrero, M. Yeary","doi":"10.1109/IGARSS.2015.7326389","DOIUrl":"https://doi.org/10.1109/IGARSS.2015.7326389","url":null,"abstract":"The second generation Digital Beamforming SAR (DBSAR-2) is a state-of-the-art airborne L-band radar being developed at the NASA Goddard Space Flight Center (GSFC). The instrument employs a 16-channel radar architecture characterized by multi-mode operation, software defined waveform generation, digital beamforming, and configurable radar parameters. The instrument has been design to support several disciplines in Earth and Planetary sciences. This technology seeks to establish the Next Generation SAR as a science instrument while setting a path future airborne and spaceborne SAR missions.","PeriodicalId":125717,"journal":{"name":"2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS)","volume":"67 11","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120987580","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.2015.7326469
I. Herlin, E. Huot
Motion fields describing the ocean surface dynamics live in vectorial spaces of high dimension. Consequently, their estimation from satellite images requires huge computational resources. The issue of dimensionality reduction, that is the determination of representative low dimensional structures in these high dimensional spaces, is of major importance for any application that demands real-time or short-term results. Proper Order Decomposition allows to determine such sub-space of motion fields on which estimation may be assessed with reduced complexity. A reduced model is obtained by Galerkin projection of evolution equations on this subspace. Motion is estimated by assimilating the observed image sequence with the reduced model. The paper describes how to derive the reduced space from a database of ocean model's outputs and explains how to estimate surface circulation from satellite sequences. Results are given on images acquired on the Black Sea basin by NOAA-AVHRR sensors.
{"title":"Dimensionality reduction on ocean model's outputs: Application to motion estimation on satellite images","authors":"I. Herlin, E. Huot","doi":"10.1109/IGARSS.2015.7326469","DOIUrl":"https://doi.org/10.1109/IGARSS.2015.7326469","url":null,"abstract":"Motion fields describing the ocean surface dynamics live in vectorial spaces of high dimension. Consequently, their estimation from satellite images requires huge computational resources. The issue of dimensionality reduction, that is the determination of representative low dimensional structures in these high dimensional spaces, is of major importance for any application that demands real-time or short-term results. Proper Order Decomposition allows to determine such sub-space of motion fields on which estimation may be assessed with reduced complexity. A reduced model is obtained by Galerkin projection of evolution equations on this subspace. Motion is estimated by assimilating the observed image sequence with the reduced model. The paper describes how to derive the reduced space from a database of ocean model's outputs and explains how to estimate surface circulation from satellite sequences. Results are given on images acquired on the Black Sea basin by NOAA-AVHRR sensors.","PeriodicalId":125717,"journal":{"name":"2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS)","volume":"3 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":"121252732","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.2015.7326589
F. Antonietta, P. Boccardo, F. G. Tonolo, M. Vassileva
The present paper aims to review the role satellite remote sensing played during the response phase to the largest (in terms of mortality) natural disaster occurred in 2013, i.e. the tropical typhoon Haiyan that struck the Philippines in November 2013. The outcomes of a thorough analysis of the emergency mapping products (about 750 maps) released in the aftermath of the event and in the following weeks are analyzed, with the goal to derive information and statistics on the main product types, the underlying datasets and the input data. Focusing on the damage assessment maps based on satellite data, operational tests on satellite imagery semi-automated classification techniques aimed at automatically extract damaged buildings will be described and discussed.
{"title":"Damage assessment exploiting remote sensing imagery: Review of the typhoon Haiyan case study","authors":"F. Antonietta, P. Boccardo, F. G. Tonolo, M. Vassileva","doi":"10.1109/IGARSS.2015.7326589","DOIUrl":"https://doi.org/10.1109/IGARSS.2015.7326589","url":null,"abstract":"The present paper aims to review the role satellite remote sensing played during the response phase to the largest (in terms of mortality) natural disaster occurred in 2013, i.e. the tropical typhoon Haiyan that struck the Philippines in November 2013. The outcomes of a thorough analysis of the emergency mapping products (about 750 maps) released in the aftermath of the event and in the following weeks are analyzed, with the goal to derive information and statistics on the main product types, the underlying datasets and the input data. Focusing on the damage assessment maps based on satellite data, operational tests on satellite imagery semi-automated classification techniques aimed at automatically extract damaged buildings will be described and discussed.","PeriodicalId":125717,"journal":{"name":"2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS)","volume":"34 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":"127204502","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.2015.7325866
Xinyu Lan, Ziqi Guo, Ye Tian, Xiaoen Lei, Jie Wang
Based on the measured spectra in the research area of Guangting reservoir, we build the model to retrieve chlorophyll-a, suspended solid and yellow substance. The paper mainly achieved the following results: we adopted matrix inversion method and L-M & NN method to analyse the water quality parameters, the selection schemes of the spectral band include REF, DER, RAN method, and then use the Guanting Reservoir experiment data for inspection and comparative analysis. The results showed that: the retrieval accuracy of L-M & NN method was better than matrix inversion method for three ocean color elements, the RAN weighted method overall had better retrieval accuracy. For Cchla, acdom (440), the best scheme in band selection is RAN, REF showed better retrieval accuracy for Cs.
{"title":"Retrieval of water quality parameters by neural network and analytical algorithm in Guanting Reservoir in Hebei Province in China","authors":"Xinyu Lan, Ziqi Guo, Ye Tian, Xiaoen Lei, Jie Wang","doi":"10.1109/IGARSS.2015.7325866","DOIUrl":"https://doi.org/10.1109/IGARSS.2015.7325866","url":null,"abstract":"Based on the measured spectra in the research area of Guangting reservoir, we build the model to retrieve chlorophyll-a, suspended solid and yellow substance. The paper mainly achieved the following results: we adopted matrix inversion method and L-M & NN method to analyse the water quality parameters, the selection schemes of the spectral band include REF, DER, RAN method, and then use the Guanting Reservoir experiment data for inspection and comparative analysis. The results showed that: the retrieval accuracy of L-M & NN method was better than matrix inversion method for three ocean color elements, the RAN weighted method overall had better retrieval accuracy. For Cchla, acdom (440), the best scheme in band selection is RAN, REF showed better retrieval accuracy for Cs.","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":"127273037","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.2015.7327033
J. Doubleday, Steve Ankuo Chien, C. Norton, K. Wagstaff, D. Thompson, J. Bellardo, Craig Francis, Eric Baumgarten
The Intelligent Payload Experiment (IPEX) is a CubeSat mission to flight validate technologies for onboard instrument processing and autonomous operations for NASA's Earth Science Technologies Office (ESTO). Specifically IPEX is to demonstrate onboard instrument processing and product generation technologies for the Intelligent Payload Module (IPM) of the proposed Hyperspectral Infra-red Imager (HyspIRI) mission concept. Many proposed future missions, including HyspIRI, are slated to produce enormous volumes of data requiring either significant communication advancements or data reduction techniques. IPEX demonstrates several technologies for onboard data reduction, such as computer vision, image analysis, image processing and in general demonstrates general operations autonomy. We conclude this paper with a number of lessons learned through operations of this technology demonstration mission on a novel platform for NASA.
{"title":"Autonomy for remote sensing — Experiences from the IPEX CubeSat","authors":"J. Doubleday, Steve Ankuo Chien, C. Norton, K. Wagstaff, D. Thompson, J. Bellardo, Craig Francis, Eric Baumgarten","doi":"10.1109/IGARSS.2015.7327033","DOIUrl":"https://doi.org/10.1109/IGARSS.2015.7327033","url":null,"abstract":"The Intelligent Payload Experiment (IPEX) is a CubeSat mission to flight validate technologies for onboard instrument processing and autonomous operations for NASA's Earth Science Technologies Office (ESTO). Specifically IPEX is to demonstrate onboard instrument processing and product generation technologies for the Intelligent Payload Module (IPM) of the proposed Hyperspectral Infra-red Imager (HyspIRI) mission concept. Many proposed future missions, including HyspIRI, are slated to produce enormous volumes of data requiring either significant communication advancements or data reduction techniques. IPEX demonstrates several technologies for onboard data reduction, such as computer vision, image analysis, image processing and in general demonstrates general operations autonomy. We conclude this paper with a number of lessons learned through operations of this technology demonstration mission on a novel platform for NASA.","PeriodicalId":125717,"journal":{"name":"2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS)","volume":"20 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":"125134928","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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