Analysis of Remotely Sensed Images Through Social Media

During the past decades, the volume of big data available in remote sensing (RS) applications has grown significantly. In addition, a number of applications related to monitoring human activity are being developed based on this kind of data. This has considerably increased the demand for RS processing methods. In this sense, the scientific community is facing the challenge of how to maximize the potential of the data that are produced in a fast and efficient way. In particular, the provision of processing algorithms that can be developed in an easy way is a fundamental problem for the RS community, due to the large volume of data offered by different portals and agencies, and the need for algorithms developed on different platforms and using a variety of programming frameworks. To address these challenges, this article takes advantage of social media tools to bring images and algorithms closer to users. In particular, a new system based on the Telegram messaging application Bot (called @ThuleRSbot) has been developed to provide a wide variety of RS image processing methods to users under the same interface. The system has been developed using the Python language and the Telegram Bot application program interface (API). The most remarkable characteristics of the system are: first, a completely open architecture that facilitates the incorporation of new algorithms without effort; and second, an easy way to automatically gather satellite images from the Sentinel Hub platform.

[1]  Rameen Abdal,et al.  UFCN: a fully convolutional neural network for road extraction in RGB imagery acquired by remote sensing from an unmanned aerial vehicle , 2018 .

[2]  He Chen,et al.  On-Board, Real-Time Preprocessing System for Optical Remote-Sensing Imagery , 2018, Sensors.

[3]  R. McRoberts,et al.  Remote sensing support for national forest inventories , 2007 .

[4]  Xiaobo Liu,et al.  Unsupervised Hyperspectral Image Band Selection Based on Deep Subspace Clustering , 2019, IEEE Geoscience and Remote Sensing Letters.

[5]  DeLiang Wang,et al.  Remote Sensing Image Segmentation by Combining Spectral and Texture Features , 2014, IEEE Transactions on Geoscience and Remote Sensing.

[6]  Kostas Stamatiou,et al.  Combining GeoEye-1 Satellite Remote Sensing, UAV Aerial Imaging, and Geophysical Surveys in Anomaly Detection Applied to Archaeology , 2011, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[7]  Joan Serra-Sagristà,et al.  Extending the CCSDS Recommendation for Image Data Compression for Remote Sensing Scenarios , 2009, IEEE Transactions on Geoscience and Remote Sensing.

[8]  Zhenzhong Chen,et al.  Superpixel-enhanced deep neural forest for remote sensing image semantic segmentation , 2020 .

[9]  Le Wang,et al.  Fully Constrained Linear Spectral Unmixing: Analytic Solution Using Fuzzy Sets , 2010, IEEE Transactions on Geoscience and Remote Sensing.

[10]  Daniel Cremers,et al.  Regularization for Deep Learning: A Taxonomy , 2017, ArXiv.

[11]  H. Sebastian Seung,et al.  Algorithms for Non-negative Matrix Factorization , 2000, NIPS.

[12]  Lijun Zhao,et al.  Remote Sensing Image Scene Classification Using CNN-CapsNet , 2019, Remote. Sens..

[13]  Peter A. Flach,et al.  Machine Learning - The Art and Science of Algorithms that Make Sense of Data , 2012 .

[14]  Bruce W. Pengra,et al.  Non-commercial Research and Educational Use including without Limitation Use in Instruction at Your Institution, Sending It to Specific Colleagues That You Know, and Providing a Copy to Your Institution's Administrator. All Other Uses, Reproduction and Distribution, including without Limitation Comm , 2022 .

[15]  Yun Shi,et al.  3D Convolutional Neural Networks for Crop Classification with Multi-Temporal Remote Sensing Images , 2018, Remote. Sens..

[16]  Jan G. P. W. Clevers,et al.  Using Hyperspectral Remote Sensing Data for Retrieving Canopy Chlorophyll and Nitrogen Content , 2012, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[17]  Saeid Homayouni,et al.  Anomaly Detection in Hyperspectral Images Based on an Adaptive Support Vector Method , 2011, IEEE Geoscience and Remote Sensing Letters.

[18]  W. Cohen,et al.  Lidar Remote Sensing of the Canopy Structure and Biophysical Properties of Douglas-Fir Western Hemlock Forests , 1999 .

[19]  Wenzhong Shi,et al.  Remote Sensing Image Classification Based on Stacked Denoising Autoencoder , 2017, Remote. Sens..

[20]  A. Goetz,et al.  Atmospheric correction algorithms for hyperspectral remote sensing data of land and ocean , 2009 .

[21]  Russell C. Hardie,et al.  Application of the stochastic mixing model to hyperspectral resolution enhancement , 2004, IEEE Transactions on Geoscience and Remote Sensing.

[22]  Uta Heiden,et al.  Capability of Spaceborne Hyperspectral EnMAP Mission for Mapping Fractional Cover for Soil Erosion Modeling , 2015, Remote. Sens..

[23]  Yuanchao Su,et al.  DAEN: Deep Autoencoder Networks for Hyperspectral Unmixing , 2019, IEEE Transactions on Geoscience and Remote Sensing.

[24]  J. R. Jensen,et al.  Remote Sensing Change Detection in Urban Environments , 2007 .

[25]  Aly A. Farag,et al.  A unified framework for MAP estimation in remote sensing image segmentation , 2005, IEEE Transactions on Geoscience and Remote Sensing.

[26]  John R. Tower,et al.  Design of a multispectral, wedge filter, remote-sensing instrument incorporating a multiport, thinned, CCD area array , 1995, Defense, Security, and Sensing.

[27]  Alan Q. Li,et al.  INTELLIGENT SYSTEM TECHNOLOGIES FOR REMOTE SENSING REPOSITORIES , 1999 .

[28]  Filiberto Pla,et al.  Capsule Networks for Hyperspectral Image Classification , 2019, IEEE Transactions on Geoscience and Remote Sensing.

[29]  Lorenzo Bruzzone,et al.  An adaptive semiparametric and context-based approach to unsupervised change detection in multitemporal remote-sensing images , 2002, IEEE Trans. Image Process..

[30]  Peijuan Wang,et al.  Fusing Landsat and MODIS Data for Vegetation Monitoring , 2015, IEEE Geoscience and Remote Sensing Magazine.

[31]  Erkki Oja,et al.  The FastICA Algorithm Revisited: Convergence Analysis , 2006, IEEE Transactions on Neural Networks.

[32]  J. Chanussot,et al.  Hyperspectral Remote Sensing Data Analysis and Future Challenges , 2013, IEEE Geoscience and Remote Sensing Magazine.

[33]  Yu Li,et al.  Automatic Target Detection in High-Resolution Remote Sensing Images Using Spatial Sparse Coding Bag-of-Words Model , 2012, IEEE Geoscience and Remote Sensing Letters.

[34]  I. Colomina,et al.  Unmanned aerial systems for photogrammetry and remote sensing: A review , 2014 .

[35]  Daniel Mozos,et al.  FPGA implementation of the principal component analysis algorithm for dimensionality reduction of hyperspectral images , 2019, Journal of Real-Time Image Processing.

[36]  Xiaolin Zhu,et al.  Spatiotemporal Fusion of Multisource Remote Sensing Data: Literature Survey, Taxonomy, Principles, Applications, and Future Directions , 2018, Remote. Sens..

[37]  Enrique Herrera-Viedma,et al.  Dynamics of Public Opinions in an Online and Offline Social Network , 2017, IEEE Transactions on Big Data.

[38]  Anthony Widjaja,et al.  Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond , 2003, IEEE Transactions on Neural Networks.

[39]  Qian Du,et al.  Hyperspectral Band Selection: A Review , 2019, IEEE Geoscience and Remote Sensing Magazine.

[40]  Ben Y. Zhao,et al.  User interactions in social networks and their implications , 2009, EuroSys '09.

[41]  Ulrike von Luxburg,et al.  A tutorial on spectral clustering , 2007, Stat. Comput..

[42]  Eric A. Smith,et al.  Spatial resolution enhancement of terrestrial features using deconvolved SSM/I microwave brightness temperatures , 1992, IEEE Trans. Geosci. Remote. Sens..

[43]  Yi Guo,et al.  Enhancement of Spectral Resolution for Remotely Sensed Multispectral Image , 2015, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[44]  P. Couteron,et al.  Textural approaches for vineyard detection and characterization using very high spatial resolution remote sensing data , 2008 .

[45]  Jocelyn Chanussot,et al.  Challenges and Opportunities of Multimodality and Data Fusion in Remote Sensing , 2014, Proceedings of the IEEE.

[46]  Prerna Mahajan,et al.  A Study of Encryption Algorithms AES, DES and RSA for Security , 2013 .

[47]  Ying Wang,et al.  Urban Observation: Integration of Remote Sensing and Social Media Data , 2019, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[48]  Min Han,et al.  Remote sensing image classification based on neural network ensemble algorithm , 2012, Neurocomputing.

[49]  Eric Harwit.  WeChat: social and political development of China’s dominant messaging app , 2017 .

[50]  Fangju Wang,et al.  Fuzzy supervised classification of remote sensing images , 1990 .

[51]  Daniel Zízala,et al.  Assessment of Soil Degradation by Erosion Based on Analysis of Soil Properties Using Aerial Hyperspectral Images and Ancillary Data, Czech Republic , 2017, Remote. Sens..

[52]  Simon Haykin,et al.  Neural Networks and Learning Machines , 2010 .

[53]  Myeong-Ryong Nam,et al.  Fusion of multispectral and panchromatic Satellite images using the curvelet transform , 2005, IEEE Geoscience and Remote Sensing Letters.

[54]  Luz Estela Valencia Ayala,et al.  INTRODUCCIÓN A LA CALIDAD DE SOFTWARE , 2008 .

[55]  Antonio J. Plaza,et al.  A Signal Processing Perspective on Hyperspectral Unmixing: Insights from Remote Sensing , 2014, IEEE Signal Processing Magazine.

[56]  Antonio J. Plaza,et al.  Recent Developments in High Performance Computing for Remote Sensing: A Review , 2011, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[57]  Filiberto Pla,et al.  Multimodal Probabilistic Latent Semantic Analysis for Sentinel-1 and Sentinel-2 Image Fusion , 2018, IEEE Geoscience and Remote Sensing Letters.

[58]  Antonio J. Plaza,et al.  Sparse Unmixing of Hyperspectral Data , 2011, IEEE Transactions on Geoscience and Remote Sensing.

[59]  D. Roberts,et al.  Hyperspectral remote sensing of fire: State-of-the-art and future perspectives , 2018, Remote Sensing of Environment.

[60]  Yao Yao,et al.  Mapping fine‐scale urban housing prices by fusing remotely sensed imagery and social media data , 2018, Trans. GIS.

[61]  Yoram J. Kaufman,et al.  Remote sensing of water vapor in the near IR from EOS/MODIS , 1992, IEEE Trans. Geosci. Remote. Sens..

[62]  Jun Li,et al.  Social Media: New Perspectives to Improve Remote Sensing for Emergency Response , 2017, Proceedings of the IEEE.

[63]  A. Ganapathiraju,et al.  LINEAR DISCRIMINANT ANALYSIS - A BRIEF TUTORIAL , 1995 .

[64]  Nikos A. Vlassis,et al.  The global k-means clustering algorithm , 2003, Pattern Recognit..

[65]  Lorenzo Bruzzone,et al.  Classification of hyperspectral remote sensing images with support vector machines , 2004, IEEE Transactions on Geoscience and Remote Sensing.

[66]  Lifei Wei,et al.  Mini-UAV-Borne Hyperspectral Remote Sensing: From Observation and Processing to Applications , 2018, IEEE Geoscience and Remote Sensing Magazine.

[67]  Jun Li,et al.  Active Learning With Convolutional Neural Networks for Hyperspectral Image Classification Using a New Bayesian Approach , 2018, IEEE Transactions on Geoscience and Remote Sensing.

[68]  Yeqiao Wang,et al.  Remote sensing change detection tools for natural resource managers: Understanding concepts and tradeoffs in the design of landscape monitoring projects , 2009 .

[69]  E. Chuvieco,et al.  Development of a framework for fire risk assessment using remote sensing and geographic information system technologies , 2010 .

[70]  Chein-I Chang,et al.  Hyperspectral image classification and dimensionality reduction: an orthogonal subspace projection approach , 1994, IEEE Trans. Geosci. Remote. Sens..

[71]  Margaret E. Gardner,et al.  Spectrometry for urban area remote sensing—Development and analysis of a spectral library from 350 to 2400 nm , 2004 .

[72]  M. S. Moran,et al.  Opportunities and limitations for image-based remote sensing in precision crop management , 1997 .

[73]  Xiao Xiang Zhu,et al.  Deep Learning in Remote Sensing: A Comprehensive Review and List of Resources , 2017, IEEE Geoscience and Remote Sensing Magazine.

[74]  J. B. Lee,et al.  Enhancement of high spectral resolution remote-sensing data by a noise-adjusted principal components transform , 1990 .

[75]  Xiaoqiang Lu,et al.  Remote Sensing Image Scene Classification: Benchmark and State of the Art , 2017, Proceedings of the IEEE.

[76]  Gene Tsudik,et al.  Diffie-Hellman key distribution extended to group communication , 1996, CCS '96.

[77]  Y. G. Prasad,et al.  Use of ground based hyperspectral remote sensing for detection of stress in cotton caused by leafhopper (Hemiptera: Cicadellidae) , 2011 .

[78]  Chein-I Chang,et al.  Constrained subpixel target detection for remotely sensed imagery , 2000, IEEE Trans. Geosci. Remote. Sens..

[79]  Sneha A. Dalvi,et al.  Internet of Things for Smart Cities , 2017 .

[80]  A. Kalra,et al.  Estimating soil moisture using remote sensing data: A machine learning approach , 2010 .

[81]  Donald F. Heath.  Large-aperture spectral radiance calibration source for ultraviolet remote sensing instruments , 2003, SPIE Asia-Pacific Remote Sensing.

[82]  Michael I. Jordan,et al.  Latent Dirichlet Allocation , 2001, J. Mach. Learn. Res..

[83]  José M. Bioucas-Dias,et al.  Vertex component analysis: a fast algorithm to unmix hyperspectral data , 2005, IEEE Transactions on Geoscience and Remote Sensing.

[84]  Liangpei Zhang,et al.  Hyperspectral Image Restoration Using Low-Rank Matrix Recovery , 2014, IEEE Transactions on Geoscience and Remote Sensing.

[85]  Bo Du,et al.  Deep Learning for Remote Sensing Data: A Technical Tutorial on the State of the Art , 2016, IEEE Geoscience and Remote Sensing Magazine.

[86]  D. Mulla.  Twenty five years of remote sensing in precision agriculture: Key advances and remaining knowledge gaps , 2013 .

[87]  Jon Atli Benediktsson,et al.  Spatial Technology and Social Media in Remote Sensing: A Survey , 2017, Proceedings of the IEEE.

[88]  Philippa J. Mason,et al.  Hyperspectral remote sensing for mineral exploration in Pulang, Yunnan Province, China , 2011 .

[89]  Peng Ren,et al.  Simultaneously Counting and Extracting Endmembers in a Hyperspectral Image Based on Divergent Subsets , 2020, IEEE Transactions on Geoscience and Remote Sensing.

[90]  David G. Long,et al.  Spatial resolution enhancement of SSM/I data , 1998, IEEE Trans. Geosci. Remote. Sens..

[91]  Junwei Han,et al.  A Survey on Object Detection in Optical Remote Sensing Images , 2016, ArXiv.

[92]  M. Borengasser,et al.  Hyperspectral Remote Sensing: Principles and Applications , 2007 .

[93]  Vittorio E. Brando,et al.  Satellite hyperspectral remote sensing for estimating estuarine and coastal water quality , 2003, IEEE Trans. Geosci. Remote. Sens..

[94]  M. Bauer,et al.  Digital change detection in forest ecosystems with remote sensing imagery , 1996 .

[95]  Gustavo Camps-Valls,et al.  Machine learning in remote sensing data processing , 2009, 2009 IEEE International Workshop on Machine Learning for Signal Processing.

[96]  Qiang Li,et al.  Hyperspectral Band Selection via Adaptive Subspace Partition Strategy , 2019, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[97]  Wojciech Zaremba,et al.  Improved Techniques for Training GANs , 2016, NIPS.

[98]  Heng Tao Shen,et al.  Principal Component Analysis , 2009, Encyclopedia of Biometrics.

[99]  Joan Serra-Sagrista,et al.  Remote Sensing Data Compression , 2008 .

[100]  Jocelyn Chanussot,et al.  A Critical Comparison Among Pansharpening Algorithms , 2015, IEEE Transactions on Geoscience and Remote Sensing.

[101]  Xiao Xiang Zhu,et al.  Learning to Pay Attention on Spectral Domain: A Spectral Attention Module-Based Convolutional Network for Hyperspectral Image Classification , 2020, IEEE Transactions on Geoscience and Remote Sensing.

[102]  Filippo Menczer,et al.  Online Human-Bot Interactions: Detection, Estimation, and Characterization , 2017, ICWSM.

[103]  Robert A. Schowengerdt,et al.  Remote sensing, models, and methods for image processing , 1997 .

[104]  Leo Breiman,et al.  Random Forests , 2001, Machine Learning.

[105]  Hao Li,et al.  Regions of Interest Detection in Panchromatic Remote Sensing Images Based on Multiscale Feature Fusion , 2014, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[106]  Jakob Jakobsen,et al.  On the CCA (in)Security of MTProto , 2015, SPSM@CCS.

[107]  N. Koutsias,et al.  Historical background and current developments for mapping burned area from satellite Earth observation , 2019, Remote Sensing of Environment.

[108]  D. Böhning.  Multinomial logistic regression algorithm , 1992 .

[109]  Mercedes Eugenia Paoletti,et al.  Deep learning classifiers for hyperspectral imaging: A review , 2019 .

[110]  Shawn D. Newsam,et al.  Bag-of-visual-words and spatial extensions for land-use classification , 2010, GIS '10.

[111]  Rohitash Chandra,et al.  Integration of Selective Dimensionality Reduction Techniques for Mineral Exploration Using ASTER Satellite Data , 2020, Remote. Sens..

[112]  R. K. Hawkins,et al.  The CCRS Airborne SAR Systems: Radar for Remote Sensing Research , 1995 .

[113]  Susan L. Ustin,et al.  Hyperspectral remote sensing for invasive species detection and mapping , 2002, IEEE International Geoscience and Remote Sensing Symposium.

[114]  Michael Jendryke,et al.  Putting people in the picture: Combining big location-based social media data and remote sensing imagery for enhanced contextual urban information in Shanghai , 2017, Comput. Environ. Urban Syst..

[115]  Junwei Han,et al.  Object detection in remote sensing imagery using a discriminatively trained mixture model , 2013 .

[116]  Thomas Hofmann,et al.  Probabilistic Latent Semantic Analysis , 1999, UAI.

[117]  Sudeep TanwarSudhanshu TyagiNeeraj Kumar.  Multimedia Big Data Computing for IoT Applications , 2020 .

[118]  Krishna P. Gummadi,et al.  Measurement and analysis of online social networks , 2007, IMC '07.

[119]  Antonio J. Plaza,et al.  A New Digital Repository for Hyperspectral Imagery With Unmixing-Based Retrieval Functionality Implemented on GPUs , 2014, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[120]  Gonzalo Pajares,et al.  Overview and Current Status of Remote Sensing Applications Based on Unmanned Aerial Vehicles (UAVs) , 2015 .

[121]  Michael Trusov,et al.  Determining Influential Users in Internet Social Networks , 2010 .

[122]  Gerhard Krieger,et al.  Smart Multi-Aperture Radar Techniques for Spaceborne Remote Sensing , 2008, IGARSS 2008 - 2008 IEEE International Geoscience and Remote Sensing Symposium.

[123]  Guoqing Li,et al.  Remote-Sensing Image Denoising Using Partial Differential Equations and Auxiliary Images as Priors , 2012, IEEE Geoscience and Remote Sensing Letters.

[124]  D. Leibovici,et al.  Rapid flood inundation mapping using social media, remote sensing and topographic data , 2017, Natural Hazards.

[125]  Hao Zhang,et al.  Application of Hyper Spectral Remote Sensing for Urban Forestry Monitoring in Natural Disaster Zones , 2011, 2011 International Conference on Computer and Management (CAMAN).

[126]  Peter E. Hart,et al.  Nearest neighbor pattern classification , 1967, IEEE Trans. Inf. Theory.

[127]  X. Ceamanos,et al.  Processing Hyperspectral Images , 2016 .

[128]  Guofeng Cao,et al.  Forecasting People’s Needs in Hurricane Events from Social Network , 2018, IEEE Transactions on Big Data.

[129]  Sankar K. Pal,et al.  Segmentation of multispectral remote sensing images using active support vector machines , 2004, Pattern Recognit. Lett..

[130]  C. King,et al.  The application of remote-sensing data to monitoring and modelling of soil erosion , 2005 .

[131]  P. D. Heermann,et al.  Classification of multispectral remote sensing data using a back-propagation neural network , 1992, IEEE Trans. Geosci. Remote. Sens..

[132]  Amy W. Apon,et al.  Middleware , 2001, 2006 ACS/IEEE International Conference on Pervasive Services.

[133]  Liangpei Zhang,et al.  Non-Local Sparse Unmixing for Hyperspectral Remote Sensing Imagery , 2014, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[134]  Tengfei Su,et al.  Scale-variable region-merging for high resolution remote sensing image segmentation , 2019, ISPRS Journal of Photogrammetry and Remote Sensing.