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A Geographically Weighted Total Composite Error Analysis for Soft Classification 软分类的地理加权全复合误差分析
IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium Pub Date : 2020-09-26 DOI: 10.1109/IGARSS39084.2020.9323939
N. Tsutsumida, T. Yoshida, D. Murakami, T. Nakaya
{"title":"A Geographically Weighted Total Composite Error Analysis for Soft Classification","authors":"N. Tsutsumida, T. Yoshida, D. Murakami, T. Nakaya","doi":"10.1109/IGARSS39084.2020.9323939","DOIUrl":"https://doi.org/10.1109/IGARSS39084.2020.9323939","url":null,"abstract":"Errors in land cover classification are often spatially heterogeneous even though a soft classification model such as spectral unmixing is implemented to mitigate a mixed pixel problem. The estimated land covers are fractions of targeted classes with the restriction of the sum to one and being non-negative. To assess the classification with considering a spatial heterogeneity, we propose a geographically weighted total composite error analysis. By using the USGS global reference database, we assessed errors of spectral unmixing classification of ALOS AVNIR-2 data into 4 land cover classes. Results yield a spatial surface of local errors by the Aitchison distance and address that the error magnitude across space is associated with the complexity of land covers.","PeriodicalId":444267,"journal":{"name":"IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium","volume":"106 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124632910","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}
引用次数: 1
Research on the Development of Urbanization in Yangtze River Economic Belt Based on Nighttime Light Remote Sensing Data 基于夜间光遥感数据的长江经济带城市化发展研究
IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium Pub Date : 2020-09-26 DOI: 10.1109/IGARSS39084.2020.9324574
Wei Zhang, Jian-xiang Qi, Guanghui Wang, Tao Zhang, H. Zhai
{"title":"Research on the Development of Urbanization in Yangtze River Economic Belt Based on Nighttime Light Remote Sensing Data","authors":"Wei Zhang, Jian-xiang Qi, Guanghui Wang, Tao Zhang, H. Zhai","doi":"10.1109/IGARSS39084.2020.9324574","DOIUrl":"https://doi.org/10.1109/IGARSS39084.2020.9324574","url":null,"abstract":"Urbanization development information is an important factor in the spatiotemporal evolution of human activities. It is of great significance to grasp the dynamic information timely of long time series urbanization in a large spatial scale for policy-making of urbanization development. In this paper, a method based on extracting the city center of gravity and speed of development based on nighttime light remote sensing data (Defense Meteorological Satellite Program's Operational Line-scan System (DMSP-OLS) and Visible Infrared Imaging Radiometer Suite Day/Night Band (VIIRS-DNB)) from 1992 to 2018 is proposed to reveal the law of urban development, which has been used in Yangtze river economic belt successfully. The conclusion can indicate that the use of nighttime light remote sensing data can provide theoretical basis and technical support for the scientific management and planning of urbanization development.","PeriodicalId":444267,"journal":{"name":"IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130495052","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}
引用次数: 1
Digital Back End for P-Band Reflections Concepts p波段反射概念的数字后端
IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium Pub Date : 2020-09-26 DOI: 10.1109/IGARSS39084.2020.9324089
R. Shah, G. Franklin, Kameron Larsen, Devin Cody, Myron Lee
{"title":"Digital Back End for P-Band Reflections Concepts","authors":"R. Shah, G. Franklin, Kameron Larsen, Devin Cody, Myron Lee","doi":"10.1109/IGARSS39084.2020.9324089","DOIUrl":"https://doi.org/10.1109/IGARSS39084.2020.9324089","url":null,"abstract":"A low cost, low power, and low mass P-band digital back end (DBE) has been developed at JPL. The design is based upon a Global Navigation Satellite System (GNSS) receiver (called Cion) that has been currently flying on the CICERO CubeSats. This paper describes the design of the DBE as well as use cases for the subsystem. This Cion DBE will be used in NASA InVest mission SNOOPI (SigNals of Opportunity P-Band Investigation) and an instrument incubator project Signals of Opportunity Synthetic Aperture Radar (SoOpSAR) to demonstrate SAR like processing using P-band Signals of Opportunity (SoOp).","PeriodicalId":444267,"journal":{"name":"IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130508603","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}
引用次数: 0
The French Land Data and Services Center: Theia 法国土地数据和服务中心:Theia
IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium Pub Date : 2020-09-26 DOI: 10.1109/IGARSS39084.2020.9324217
N. Baghdadi, A. Sellé, H. Bazzi, M. Zribi, Isabelle Biagiotti, Frédéric Huynh
{"title":"The French Land Data and Services Center: Theia","authors":"N. Baghdadi, A. Sellé, H. Bazzi, M. Zribi, Isabelle Biagiotti, Frédéric Huynh","doi":"10.1109/IGARSS39084.2020.9324217","DOIUrl":"https://doi.org/10.1109/IGARSS39084.2020.9324217","url":null,"abstract":"The TREIA land data and services center was created with the objective of increasing the use of space data in complementarity with other types of data (in particular in situ, airborne data) by the scientific community and more generally the public actors. TREIA is structuring the French science community through 1) a mutualized Service and Data Infrastructure (SDI) distributed between several centers, allowing access to a variety of products; 2) the setup of Regional Animation Networks (RAN) to federate users (scientists and public / private actors) and 3) Scientific Expertise Centers (SEC) clustering virtual research groups on a thematic domain. A strong relationship between SECs and RANs is being developed to both disseminate the outputs to the user communities and aggregate the user needs. The research works carried out in two SECs are presented. They are organized around the design and development of value-added products and services.","PeriodicalId":444267,"journal":{"name":"IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126897044","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}
引用次数: 0
High-Order Triplet CRF-Pcanet for Unsupervised Segmentation of SAR Image SAR图像无监督分割的高阶三元组CRF-Pcanet
IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium Pub Date : 2020-09-26 DOI: 10.1109/IGARSS39084.2020.9324235
Peng Zhang, Yinyin Jiang, Beibei Li, Ming Li, M. E. Boudaren, Wanying Song, Y. Wu
{"title":"High-Order Triplet CRF-Pcanet for Unsupervised Segmentation of SAR Image","authors":"Peng Zhang, Yinyin Jiang, Beibei Li, Ming Li, M. E. Boudaren, Wanying Song, Y. Wu","doi":"10.1109/IGARSS39084.2020.9324235","DOIUrl":"https://doi.org/10.1109/IGARSS39084.2020.9324235","url":null,"abstract":"In this paper, we combine the modeling power of conditional random fields (CRF) model with the representation-learning ability of principal component analysis network (PCANet), and propose a high-order triplet CRF model, named as HOTCRF-PCANet, for unsupervised synthetic aperture radar (SAR) image segmentation. HOTCRF-PCANet introduces an auxiliary field to explicitly regulate label interactions of complex SAR image. In the label and auxiliary fields, HOTCRF-PCANet defines a discrete quadrilateral pairwise Markov fields (DQPMF) model, and thus constructs a high-order DQPMF potential to model the high-order label interactions in an unsupervised way. Additionally, HOTCRF-PCANet uses a product-of-expert (POE) potential to enforce the regions' labeling consistency for pixels within the weak-structured region. Moreover, HOTCRF-PCANet modifies PCANet into an unsupervised mode, i.e. UPCANet, automatically learns rich features of SAR image and constructs an UPCANet-based unary potential to predict the local class probability. The effectiveness of HOTCRF-PCANet is demonstrated by the application to the unsupervised segmentation of simulated and real SAR images.","PeriodicalId":444267,"journal":{"name":"IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129193406","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}
引用次数: 0
Human Detection with Range-Doppler Signatures Using 3D Convolutional Neural Networks 基于三维卷积神经网络的距离-多普勒特征人体检测
IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium Pub Date : 2020-09-26 DOI: 10.1109/IGARSS39084.2020.9324052
Youngwook Kim, Ibrahim Alnujaim, S. You, Byung Jang Jeong
{"title":"Human Detection with Range-Doppler Signatures Using 3D Convolutional Neural Networks","authors":"Youngwook Kim, Ibrahim Alnujaim, S. You, Byung Jang Jeong","doi":"10.1109/IGARSS39084.2020.9324052","DOIUrl":"https://doi.org/10.1109/IGARSS39084.2020.9324052","url":null,"abstract":"Human detection is proposed based on time-varying range- Doppler signatures measured by millimeter-wave FMCW radar using deep recurrent neural networks. Human detection is a significant topic for security, surveillance, and search and rescue. When a target is measured by fast-chirp FMCW radar, a range-Doppler diagram can be constructed in real time. Because the signatures in a range-Doppler diagram are time-varying, we investigated the feasibility of classifying targets using those signatures. We measured five classes-humans, cars, cyclists, dogs, and road clutter-using millimeter-wave FMCW radar. We applied 3D-convolutional neural networks to 3D representations of time-varying signatures and achieved a classification accuracy of 97%, with a human detection rate of 100%.","PeriodicalId":444267,"journal":{"name":"IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130649585","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}
引用次数: 1
CNN-Based Tropical Cyclone Track Forecasting from Satellite Infrared Images 基于cnn的热带气旋轨道卫星红外图像预报
IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium Pub Date : 2020-09-26 DOI: 10.1109/IGARSS39084.2020.9324408
Chong Wang, Qing Xu, Xiaofeng Li, Yongcun Cheng
{"title":"CNN-Based Tropical Cyclone Track Forecasting from Satellite Infrared Images","authors":"Chong Wang, Qing Xu, Xiaofeng Li, Yongcun Cheng","doi":"10.1109/IGARSS39084.2020.9324408","DOIUrl":"https://doi.org/10.1109/IGARSS39084.2020.9324408","url":null,"abstract":"In this study, a deep convolutional neural network (CNN) was developed to forecast the movement direction of tropical cyclones (or typhoons) over the Northwestern Pacific basin from Himawari-8 (H-8) satellite images. 2250 infrared images which captured 97 typhoon cases between 2015 and 2018 were used to train the CNN model. By using images from Channels 13 and 15 as input into the CNN model, the mean error of the typhoon movement angle reaches up to 27.8°, which shows the great potential of deep learning in tropical cyclone track prediction.","PeriodicalId":444267,"journal":{"name":"IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123802806","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}
引用次数: 6
Agriculture Multispectral Uav Image Registration Using Salient Features and Mutual Information 基于显著特征和互信息的农业多光谱无人机图像配准
IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium Pub Date : 2020-09-26 DOI: 10.1109/IGARSS39084.2020.9323325
Sergio Stempliuk, D. Menotti
{"title":"Agriculture Multispectral Uav Image Registration Using Salient Features and Mutual Information","authors":"Sergio Stempliuk, D. Menotti","doi":"10.1109/IGARSS39084.2020.9323325","DOIUrl":"https://doi.org/10.1109/IGARSS39084.2020.9323325","url":null,"abstract":"Multimodal image registration has been studied for a long time as a necessary pre-processing step to extract relevant information from the studied images. In this direction, agriculture remote sensing has evolved to use multispectral sensors and faces challenges since the application of classic solutions is not suitable. This paper preliminarily explores the benefits of applying Mutual Information (MI) based on SIFT points for image registration to agriculture remote sensing multi-spectral evaluated on a self-developed public database of images through a fixed-wing Unmanned Aerial Vehicle (UAV) equipped with a multispectral sensor operating within parameters that would apply to crops inspection in real life. Our preliminary results have shown a marginal improvement of MI after registration highlighting that we may apply it to improve the registration of agriculture remotely sensed images. This small variation of MI shows that there is room for improvement.","PeriodicalId":444267,"journal":{"name":"IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123932769","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}
引用次数: 0
NOC GNSS-R Global Ocean Wind Speed and Sea-Ice Products Using Data from the TechDemoSat-1 Mission 使用TechDemoSat-1任务数据的NOC GNSS-R全球海洋风速和海冰产品
IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium Pub Date : 2020-09-26 DOI: 10.1109/IGARSS39084.2020.9323151
G. Foti, M. Hammond, C. Gommenginger, M. Srokosz, M. Unwin, J. Rosello
{"title":"NOC GNSS-R Global Ocean Wind Speed and Sea-Ice Products Using Data from the TechDemoSat-1 Mission","authors":"G. Foti, M. Hammond, C. Gommenginger, M. Srokosz, M. Unwin, J. Rosello","doi":"10.1109/IGARSS39084.2020.9323151","DOIUrl":"https://doi.org/10.1109/IGARSS39084.2020.9323151","url":null,"abstract":"Global Navigation Satellite System-Reflectometry (GNSS-R) is an innovative and rapidly developing approach to Earth Observation that makes use of signals of opportunity from GNSS, which have been reflected off the Earth's surface. This technology has been demonstrated to be applicable to the remote sensing of a number of geophysical surface parameters including ocean wind speed and sea-ice. Using data collected by the UK TechDemoSat-1 mission between 2014 and 2018, the National Oceanography Centre (NOC) has developed a GNSS-R signal processing scheme called the NOC Calibrated Bistatic Radar Equation (C-BRE) processor that features an ocean wind speed inversion algorithm incorporating radiometric calibration submodules and several corrections steps that mitigate effects related to the GNSS system, instrumentm and geometry. The latest version of the NOC GNSS-R processor additionally features updated data quality control mechanisms that include the flagging of radio frequency interference (RFI) and sea-ice detection based on the GNSS-R waveform.","PeriodicalId":444267,"journal":{"name":"IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium","volume":"55 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124164904","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}
引用次数: 0
Classification of Building Structure Types Using UAV Optical Images 基于无人机光学图像的建筑结构类型分类
IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium Pub Date : 2020-09-26 DOI: 10.1109/IGARSS39084.2020.9323613
Haolin Wu, Gaozhong Nie, Xiwei Fan
{"title":"Classification of Building Structure Types Using UAV Optical Images","authors":"Haolin Wu, Gaozhong Nie, Xiwei Fan","doi":"10.1109/IGARSS39084.2020.9323613","DOIUrl":"https://doi.org/10.1109/IGARSS39084.2020.9323613","url":null,"abstract":"It is well know that for the same intensity areas, the buildings with different structure types can show different vulnerabilities. Thus, building structure type is one the key parameters for rapid estimation of casualties and injuries after earthquake, which is vital for emergency response and rescue. To estimate building structure types, the buildings are firstly extracted based on the spectrum, texture, and height information of UAV visible images. Then, the structure type of individual extracted buildings is classified using convolution neural network. To evaluate the accuracy of the proposed method, the images of Xuyi county, Huai'an City, Jiangsu Province are acquired using a small rotorcraft UAV. The results show that the user accuracy and cartography accuracy are 80.69% and 78.42%, respectively.","PeriodicalId":444267,"journal":{"name":"IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium","volume":"70 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124231749","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}
引用次数: 3
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