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Multimodal hyperspectral remote sensing: an overview and perspective

Yanfeng Gu, Tianzhu Liu, Guoming Gao, Guangbo Ren, Yi Ma, Jocelyn Chanussot, Xiuping Jia
2021 Science China Information Sciences  
Multimodal hyperspectral remote sensing: an overview and perspective. Sci China Inf Sci, 2021, 64(2): 121301, https://doi.  ...  Along this perspective, firstly, the current researches on hyperspectral remote sensing and image processing are briefly reviewed, and then, comprehensive descriptions of the aforementioned three main  ...  In this paper, we try to set forth a new perspective of multimodal hyperspectral remote sensing from the point of imaging detection, and give an overview of relevant data processing as comprehensive as  ... 
doi:10.1007/s11432-020-3084-1 fatcat:tivcc4l5efh5zg62t37stswqgu

Special Section Guest Editorial: Feature and Deep Learning in Remote Sensing Applications

John E. Ball, Derek T. Anderson, Chee Seng Chan
2018 Journal of Applied Remote Sensing  
Remote sensing is an extremely active area of research that impacts global topics like agriculture, disaster monitoring and response, defense and security, weather, and non-earth observations.  ...  The technologies that power remote sensing-i.e., allow us to observe the universe-include hyperspectral imaging, synthetic aperture radar (SAR), electro-optical, thermal, light detection and ranging (LiDAR  ...  and open problems in deep learning for remote sensing, discusses modifications of DL architectures for remote sensing, provides an overview of deep learning tools, and gives an extensive summary of remote  ... 
doi:10.1117/1.jrs.11.042601 fatcat:pq3xg2sggfdtljjs3hrmp7tzdm

Editorial for the Special Issue "Frontiers in Spectral Imaging and 3D Technologies for Geospatial Solutions"

Eija Honkavaara, Konstantinos Karantzalos, Xinlian Liang, Erica Nocerino, Ilkka Pölönen, Petri Rönnholm
2019 Remote Sensing  
This Special Issue hosts papers on the integrated use of spectral imaging and 3D technologies in remote sensing, including novel sensors, evolving machine learning technologies for data analysis, and the  ...  The presented results showed improved results when multimodal data was used in object analysis.  ...  The Remote Sensing editorial team is gratefully acknowledged for its support during all phases of the endeavor to successfully complete this volume.  ... 
doi:10.3390/rs11141714 fatcat:jmyg2y523jfyhm5ykkc3jsqbpa

Computational Intelligence in Remote Sensing: An Editorial

Manuel Graña, Michal Wozniak, Sebastian Rios, Javier de de Lope
2020 Sensors  
In this editorial paper we provide the setting of the special issue "Computational Intelligence in Remote Sensing" and an overview of the published papers.  ...  Remote sensing data has been a salient field of application of computational intelligence algorithms, both for the exploitation of the data and for the research/development of new data analysis tools.  ...  Additional support come from project CybSPEED funded in 2017 call of the H2020 MSCA-RISE with grant 777720, and project KK-2018/00071 of the Elkartek 2018 funding program of the Basque Government.  ... 
doi:10.3390/s20030633 pmid:31979240 pmcid:PMC7038229 fatcat:inwh36whqffs5fy7i5hzrfzmb4

Fusion Levels [chapter]

2016 Remote Sensing Image Fusion  
This book gives an introduction to remote sensing image fusion providing an overview on the sensors and applications.  ...  This book gives an introduction to remote sensing image fusion (RSIF) providing an overview of the sensors and applications.  ... 
doi:10.1201/9781315370101-11 fatcat:jgjyqtml7zh7tjnk6jhp4mvqfu

Remote Sensing of Savannas and Woodlands: Editorial

Michael J. Hill
2021 Remote Sensing  
Savannas and woodlands represent one of the most challenging targets for remote sensing [...]  ...  Acknowledgments: I wish to all thank authors for submitting to the Special Issue and for contributing to what is a distinctive perspective on the current state of remote sensing of savannas and woodlands  ...  The Papers The papers provide a very current perspective on remote sensing of savannas and woodlands and reflect both methodological trends, and geographical imperatives driven by threats.  ... 
doi:10.3390/rs13081490 fatcat:evudpaamuza4lc4s6tvi4unesa

2020 Index IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Vol. 13

2020 IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing  
., +, JSTARS 2020 4070-4084 Toward Remote Sensing Image Retrieval Under a Deep Image Captioning Perspective.  ...  ., +, JSTARS 2020 3503-3520 Toward Remote Sensing Image Retrieval Under a Deep Image Captioning Perspective.  ... 
doi:10.1109/jstars.2021.3050695 fatcat:ycd5qt66xrgqfewcr6ygsqcl2y

2019 Index IEEE Transactions on Geoscience and Remote Sensing Vol. 57

2019 IEEE Transactions on Geoscience and Remote Sensing  
and Hanssen, R.F., Incorporating Temporary Coherent Li, X., Yeo, T.S., Yang, Y., Chi, C., Zuo, F., Hu, X., and Pi, Y., Refo-cusing and Zoom-In Polar Format Algorithm for Curvilinear Spotlight SAR Imaging  ...  Hu, C., Zhang, B., Dong, X., and Li, Y., Geosynchronous SAR Tomography: Theory and First Experimental Verification Using Beidou IGSO Satellite; TGRS Sept. 2019 6591-6607 Hu, F., Wu, J., Chang, L.,  ...  ., +, TGRS April 2019 2290-2304 Deep Learning for Hyperspectral Image Classification: An Overview.  ... 
doi:10.1109/tgrs.2020.2967201 fatcat:kpfxoidv5bgcfo36zfsnxe4aj4

X-ModalNet: A Semi-Supervised Deep Cross-Modal Network for Classification of Remote Sensing Data [article]

Danfeng Hong, Naoto Yokoya, Gui-Song Xia, Jocelyn Chanussot, Xiao Xiang Zhu
2020 arXiv   pre-print
We evaluate X-ModalNet on two multi-modal remote sensing datasets (HSI-MSI and HSI-SAR) and achieve a significant improvement in comparison with several state-of-the-art methods.  ...  This paper addresses the problem of semi-supervised transfer learning with limited cross-modality data in remote sensing.  ...  Figure 2 : 2 An overview of the proposed X-ModalNet.  ... 
arXiv:2006.13806v1 fatcat:3b47auxsb5fzvc74uim5kkhwhm

A Meta-Analysis of Convolutional Neural Networks for Remote Sensing Applications

Masoud Mahdianpari, Hamid Ghanbari, Fariba Mohammadimanesh, Saeid Homayouni
2021 IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing  
Index Terms-Convolutional neural network (CNN), deep learning (DL), meta-analysis, remote sensing (RS).  ...  Since the rise of deep learning in the past few years, convolutional neural networks (CNNs) have quickly found their place within the remote sensing (RS) community.  ...  First, CNN models were employed for high-resolution remotely sensed image change detection in 2018 using faster R-CNN [119] and have gained attention since then.  ... 
doi:10.1109/jstars.2021.3065569 fatcat:jn4dzywhuvaublx2aoat2tsfga

Fusing Multimodal Video Data for Detecting Moving Objects/Targets in Challenging Indoor and Outdoor Scenes

Zacharias Kandylakis, Konstantinos Vasili, Konstantinos Karantzalos
2019 Remote Sensing  
Towards this direction, we have designed a multisensor system based on thermal, shortwave infrared, and hyperspectral video sensors and propose a processing pipeline able to perform in real-time object  ...  In particular, in order to avoid the computationally intensive coregistration of the hyperspectral data with other imaging modalities, the initially detected targets are projected through a local coordinate  ...  Author Contributions: Z.K. designed the methodology, implemented the software, performed experiments and validation, and wrote, edited, and reviewed the manuscript.  ... 
doi:10.3390/rs11040446 fatcat:xowwfb4mfvfupkptyfcsugvwqy

Multisource and Multitemporal Data Fusion in Remote Sensing [article]

Pedram Ghamisi, Behnood Rasti, Naoto Yokoya, Qunming Wang, Bernhard Hofle, Lorenzo Bruzzone, Francesca Bovolo, Mingmin Chi, Katharina Anders, Richard Gloaguen, Peter M. Atkinson, Jon Atli Benediktsson
2018 arXiv   pre-print
Such an increase in remote sensing and ancillary datasets, however, opens up the possibility of utilizing multimodal datasets in a joint manner to further improve the performance of the processing approaches  ...  processing of remotely sensed data.  ...  Section IV is devoted to hyperspectral and LiDAR data fusion. Section V presents an overview of multitemporal data fusion.  ... 
arXiv:1812.08287v1 fatcat:hmojxdoaybc6vjeto5s3x7b6z4

Computer Vision, IoT and Data Fusion for Crop Disease Detection Using Machine Learning: A Survey and Ongoing Research

Maryam Ouhami, Adel Hafiane, Youssef Es-Saady, Mohamed El Hajji, Raphael Canals
2021 Remote Sensing  
However, the increasing number and diversity of research studies requires a literature review for further developments and contributions in this area.  ...  It lists traditional and deep learning methods associated with the main data acquisition modalities, namely IoT, ground imaging, unmanned aerial vehicle imaging and satellite imaging.  ...  . 2019 [26] Monitoring plant diseases and pests through remote sensing technology. 2019 [9] Applications of remote sensing in precision agriculture. 2020 [27] High-resolution satellite imagery  ... 
doi:10.3390/rs13132486 fatcat:f6u2vvmgvjggrhoqsph6odas3i

Table of Contents

2021 2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS  
.O-14.1: AN OVERVIEW OF MULTIMODAL REMOTE SENSING DATA FUSION: FROM .........................  ...  TH1.O-13.1: REALIZING THE POTENTIAL OF HYPERSPECTRAL REMOTE SENSING IN .............................  ... 
doi:10.1109/igarss47720.2021.9553380 fatcat:zm7sioez6vgblpj25frkkxtfc4

YOLOrs: Object Detection in Multimodal Remote Sensing Imagery

Manish Sharma, Mayur Dhanaraj, Srivallabha Karnam, Dimitris G. Chachlakis, Raymond Ptucha, Panos P. Markopoulos, Eli Saber
2021 IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing  
Detection performance can improve by fusing data from multiple remote sensing modalities, including red, green, blue, infrared, hyperspectral, multispectral, synthetic aperture radar, and light detection  ...  Our experimental studies compare YOLOrs with contemporary alternatives and corroborate its merits. Index Terms-Aerial imagery, fusion, multimodal, object detection, remote sensing (RS).  ...  INTRODUCTION O BJECT detection is a fundamental task in computer vision and remote sensing (RS) with a plethora of civilian and military applications, including medical diagnosis, autonomousvehicle navigation  ... 
doi:10.1109/jstars.2020.3041316 fatcat:6xzsvwkeava7nedb7cnn2ejhye
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