Pub Date : 2020-01-01DOI: 10.1109/IGARSS39084.2020.9323301
G. Caporusso, E. Lopinto, R. Lorusso, R. Loizzo, R. Guarini, M. Daraio, P. Sacco
{"title":"The Hyperspectral Prisma Mission in Operations","authors":"G. Caporusso, E. Lopinto, R. Lorusso, R. Loizzo, R. Guarini, M. Daraio, P. Sacco","doi":"10.1109/IGARSS39084.2020.9323301","DOIUrl":"https://doi.org/10.1109/IGARSS39084.2020.9323301","url":null,"abstract":"","PeriodicalId":6466,"journal":{"name":"2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS)","volume":"171 1","pages":"3282-3285"},"PeriodicalIF":0.0,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78765734","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 : 2020-01-01DOI: 10.1109/IGARSS39084.2020.9323910
Jieying He, Shengwei Zhang
{"title":"Analysis of System Linearity Caused by Gain Variation for Microsat-Based Microwave Radiometer","authors":"Jieying He, Shengwei Zhang","doi":"10.1109/IGARSS39084.2020.9323910","DOIUrl":"https://doi.org/10.1109/IGARSS39084.2020.9323910","url":null,"abstract":"","PeriodicalId":6466,"journal":{"name":"2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS)","volume":"59 1","pages":"6361-6364"},"PeriodicalIF":0.0,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76151710","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}
Aiming at the problems of poor adaptability of traditional target detection algorithms and high computational resources of deep learning algorithms, a BP neural network target detection algorithm based on histogram statistics is proposed. It is based on the principle that similar areas have similar histograms. In this algorithm, the two-dimensional image information converts to the one-dimensional histogram information. We establish a three-layer neural network model, and the histogram is used as the input of the BP neural network. Compared to the traditional target detection algorithms, its complexity is low, and its efficiency and accuracy is high. The experimental results show that the fewer classification categories, the higher target detection probability. The computational complexity of the BP neural network is low, so the computational efficiency is quite high. The accuracy of target recognition is higher than 97% with SAR and optical images.
{"title":"A Target Detection Algorithm of Neural Network Based on Histogram Statistics","authors":"Shuai Jiang, Yalong Pang, Lu-yuan Wang, Ji-yang Yu, Bowen Cheng, Zongling Li","doi":"10.1109/IGARSS39084.2020.9323126","DOIUrl":"https://doi.org/10.1109/IGARSS39084.2020.9323126","url":null,"abstract":"Aiming at the problems of poor adaptability of traditional target detection algorithms and high computational resources of deep learning algorithms, a BP neural network target detection algorithm based on histogram statistics is proposed. It is based on the principle that similar areas have similar histograms. In this algorithm, the two-dimensional image information converts to the one-dimensional histogram information. We establish a three-layer neural network model, and the histogram is used as the input of the BP neural network. Compared to the traditional target detection algorithms, its complexity is low, and its efficiency and accuracy is high. The experimental results show that the fewer classification categories, the higher target detection probability. The computational complexity of the BP neural network is low, so the computational efficiency is quite high. The accuracy of target recognition is higher than 97% with SAR and optical images.","PeriodicalId":6466,"journal":{"name":"2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS)","volume":"22 1","pages":"1628-1631"},"PeriodicalIF":0.0,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79504496","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 : 2020-01-01DOI: 10.1109/IGARSS39084.2020.9323745
G. Nimish, B. Aithal
{"title":"Forecasting Land Surface Temperature Using Artificial Neural Network","authors":"G. Nimish, B. Aithal","doi":"10.1109/IGARSS39084.2020.9323745","DOIUrl":"https://doi.org/10.1109/IGARSS39084.2020.9323745","url":null,"abstract":"","PeriodicalId":6466,"journal":{"name":"2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS)","volume":"37 1","pages":"4387-4390"},"PeriodicalIF":0.0,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83575976","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 : 2020-01-01DOI: 10.1109/IGARSS39084.2020.9324193
M. J. Bemelmans, E. V. Dalfsen, M. Poland
{"title":"Determining the Source Location and Evolution of the May 2015 Summit Inflation Event at Kilauea Volcano Hawai'i","authors":"M. J. Bemelmans, E. V. Dalfsen, M. Poland","doi":"10.1109/IGARSS39084.2020.9324193","DOIUrl":"https://doi.org/10.1109/IGARSS39084.2020.9324193","url":null,"abstract":"","PeriodicalId":6466,"journal":{"name":"2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS)","volume":"2 1","pages":"6818-6821"},"PeriodicalIF":0.0,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78773085","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.2019.8898750
Victoria Ionca, M. Bogliolo, G. Laneve, G. Liberti, A. Palombo, S. Pignatti
In this work, we present the calibration and validation method we have applied in order to retrieve the split window (SW) coefficients for land surface temperature (LST) estimations from Thermal Airborne Spectrographic imager (TASI). For calibration and validation two different datasets has been used, both extracted from SeeBor V5.0 training dataset. The coefficients have been retrieved by a multiple regression analysis and MODTRAN simulations. For the radiative transfer experiment, we considered seven different viewing angles in a range between 0° and 60° with a step of 10°. Simulations have been performed considering all TASI channel combinations and the sensor spectral response functions. Preliminary results are presented for best band combinations suitable for SW algorithm application; these are channel 19 (10.034 gm) with 28 (11.024 gm), and channel 29 (11.134 gm) with 31 (11.354 gm). Finally, validation of the LST retrievals presents a RMSE lower than 0.6 K for both band combinations.
{"title":"Split Window Algorithm Calibration and Validation for TASI Sensor","authors":"Victoria Ionca, M. Bogliolo, G. Laneve, G. Liberti, A. Palombo, S. Pignatti","doi":"10.1109/IGARSS.2019.8898750","DOIUrl":"https://doi.org/10.1109/IGARSS.2019.8898750","url":null,"abstract":"In this work, we present the calibration and validation method we have applied in order to retrieve the split window (SW) coefficients for land surface temperature (LST) estimations from Thermal Airborne Spectrographic imager (TASI). For calibration and validation two different datasets has been used, both extracted from SeeBor V5.0 training dataset. The coefficients have been retrieved by a multiple regression analysis and MODTRAN simulations. For the radiative transfer experiment, we considered seven different viewing angles in a range between 0° and 60° with a step of 10°. Simulations have been performed considering all TASI channel combinations and the sensor spectral response functions. Preliminary results are presented for best band combinations suitable for SW algorithm application; these are channel 19 (10.034 gm) with 28 (11.024 gm), and channel 29 (11.134 gm) with 31 (11.354 gm). Finally, validation of the LST retrievals presents a RMSE lower than 0.6 K for both band combinations.","PeriodicalId":6466,"journal":{"name":"2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS)","volume":"14 1","pages":"3420-3423"},"PeriodicalIF":0.0,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83764870","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.2019.8898336
S. Pascucci, S. Pignatti, C. Belviso, F. Cavalcante, M. Bogliolo
The paper compares the potential of WorldView-3 (WV-3) and Sentinel-2 (S-2) satellite data for mapping naturally occurring asbestos (NOA) outcrops to be used by geologists in the planning phase of environmental monitoring. The wide distribution as well as the variety and extent of asbestos-bearing rocks make the selected area a significant case study for the evaluation of the feasibility of multispectral VNIR-SWIR (0.425-2.330 µm) remote sensing observations for NOA outcrops mapping, in those areas where the density of vegetation allows their spectral identification. Different classification procedures were used to produce NOA outcrops maps for the study area. In our study, we found in general a good agreement (k > 0.8) between the produced NOA outcrops maps and the extensive available in situ data for the accessible locations.
{"title":"Worldview-3 and Sentinel-2 Imagery for Mapping Naturally Occurring Asbestos (NOA) in Serpentinites Rocks in Southern Italy","authors":"S. Pascucci, S. Pignatti, C. Belviso, F. Cavalcante, M. Bogliolo","doi":"10.1109/IGARSS.2019.8898336","DOIUrl":"https://doi.org/10.1109/IGARSS.2019.8898336","url":null,"abstract":"The paper compares the potential of WorldView-3 (WV-3) and Sentinel-2 (S-2) satellite data for mapping naturally occurring asbestos (NOA) outcrops to be used by geologists in the planning phase of environmental monitoring. The wide distribution as well as the variety and extent of asbestos-bearing rocks make the selected area a significant case study for the evaluation of the feasibility of multispectral VNIR-SWIR (0.425-2.330 µm) remote sensing observations for NOA outcrops mapping, in those areas where the density of vegetation allows their spectral identification. Different classification procedures were used to produce NOA outcrops maps for the study area. In our study, we found in general a good agreement (k > 0.8) between the produced NOA outcrops maps and the extensive available in situ data for the accessible locations.","PeriodicalId":6466,"journal":{"name":"2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS)","volume":"31 1","pages":"6756-6759"},"PeriodicalIF":0.0,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74951722","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.2019.8900609
Nyasha Mureriwa, E. Adam, Adewale Samuel Adelabu
This study evaluates the use of SPOT-6 data in conjunction with two machine learning classifiers, namely, Random Forest (RF) and Support Vector Machines (SVM) to map Prosopis glandulosa, its co-existing acacia species and other land-cover types in an arid South African environment. This highly invasive species has been difficult to control using physical, chemical and biological methods because of insufficient knowledge of the species dynamic and lack of spatial data. Results show that it is possible to distinguish Prosopis glandulosa from coexisting Acacia karoo and Acacia mellifera as well as other general land cover types. Classification using SVM obtained a higher overall accuracy of 78.66% (Kappa of 0.7428) whilst RF obtained a lower classification accuracy of 69.93% (Kappa of 0.6331). The high accuracies obtained show the potential to map the invasive species spread on a large scale. This can assist monitoring and planning against future invasions.
{"title":"Cost Effective Approach for Mapping Prosopis Invasion in Arid South Africa Using SPOT-6 Imagery and Two Machine Learning Classifiers","authors":"Nyasha Mureriwa, E. Adam, Adewale Samuel Adelabu","doi":"10.1109/IGARSS.2019.8900609","DOIUrl":"https://doi.org/10.1109/IGARSS.2019.8900609","url":null,"abstract":"This study evaluates the use of SPOT-6 data in conjunction with two machine learning classifiers, namely, Random Forest (RF) and Support Vector Machines (SVM) to map Prosopis glandulosa, its co-existing acacia species and other land-cover types in an arid South African environment. This highly invasive species has been difficult to control using physical, chemical and biological methods because of insufficient knowledge of the species dynamic and lack of spatial data. Results show that it is possible to distinguish Prosopis glandulosa from coexisting Acacia karoo and Acacia mellifera as well as other general land cover types. Classification using SVM obtained a higher overall accuracy of 78.66% (Kappa of 0.7428) whilst RF obtained a lower classification accuracy of 69.93% (Kappa of 0.6331). The high accuracies obtained show the potential to map the invasive species spread on a large scale. This can assist monitoring and planning against future invasions.","PeriodicalId":6466,"journal":{"name":"2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS)","volume":"116 1","pages":"3724-3727"},"PeriodicalIF":0.0,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77889686","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}
{"title":"Impact of Specular Point Estimation Inaccuracies on TechoDemoSat-1 GNSS-Reflectometry Observables Over Oceans","authors":"G. Grieco, A. Stoffelen, M. Portabella","doi":"10.1109/IGARSS.2019.8898561","DOIUrl":"https://doi.org/10.1109/IGARSS.2019.8898561","url":null,"abstract":"2019 IEEE International Geoscience and Remote Sensing Symposium (IGARSS 2019), 28 July - 2 August 2019, Yokohama, Japan","PeriodicalId":6466,"journal":{"name":"2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS)","volume":"344 2 1","pages":"8715-8718"},"PeriodicalIF":0.0,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75734057","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 : 2019-01-01DOI: 10.1109/IGARSS.2019.8898788
Yun Cheng, Lu Zhang, Huiqian Chen, Bing Sun
{"title":"Researth on the Detection Method of Antarctic Ice Sheet Freezing and Thawing Based on Gee and Sentinel-1 Data","authors":"Yun Cheng, Lu Zhang, Huiqian Chen, Bing Sun","doi":"10.1109/IGARSS.2019.8898788","DOIUrl":"https://doi.org/10.1109/IGARSS.2019.8898788","url":null,"abstract":"","PeriodicalId":6466,"journal":{"name":"2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS)","volume":"4 1","pages":"4141-4144"},"PeriodicalIF":0.0,"publicationDate":"2019-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75644403","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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