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Reduced-Complexity End-to-End Variational Autoencoder for on Board Satellite Image Compression

Vinicius Alves de Oliveira, Marie Chabert, Thomas Oberlin, Charly Poulliat, Mickael Bruno, Christophe Latry, Mikael Carlavan, Simon Henrot, Frederic Falzon, Roberto Camarero
2021 Remote Sensing  
However, in the context of on board satellite compression, time and memory complexities are submitted to strong constraints.  ...  The aim of this paper is to design a complexity-reduced variational autoencoder in order to meet these constraints while maintaining the performance.  ...  In this paper, we start from the state-of-the-art CNN image compression schemes [13, 16] to design a reduced-complexity framework in order to adapt to satellite image compression.  ... 
doi:10.3390/rs13030447 fatcat:jw252bqzxrfvtfhqnwqnwitp34

Editorial to Special Issue "Remote Sensing Data Compression"

Benoit Vozel, Vladimir Lukin, Joan Serra-Sagristà
2021 Remote Sensing  
Due to different limitations, compression has to be applied on-board and/or on-the-ground.  ...  , investigating the suitability of neural networks for compression, and researching on low complexity hardware and software approaches to deliver competitive coding performance.  ...  Acknowledgments: We are grateful for having the opportunity to lead this Special Issue.  ... 
doi:10.3390/rs13183727 fatcat:eap6j7idm5h6rcpd7dj5xglvj4

Hyperspectral Data Compression Using Fully Convolutional Autoencoder

Riccardo La Grassa, Cristina Re, Gabriele Cremonese, Ignazio Gallo
2022 Remote Sensing  
However, the huge data volume gained by the complex on-board satellite instruments becomes a problem that needs to be managed carefully.  ...  To reduce the data volume to be stored and transmitted on-ground, the signals received should be compressed, allowing a good original source representation in the reconstruction step.  ...  In fact, autoencoder models can reduce the access memory (writing and reading) from internal satellite storage due to compression data learned in the training step, and depending on the compression ratio  ... 
doi:10.3390/rs14102472 fatcat:kaxwwkd6jrczpmj7fozrf7z5ai

Adaptively Lossy Image Compression for Onboard Processing

Justin Goodwill, David Wilson, Sebastian Sabogal, Alan D. George, Christopher Wilson
2020 2020 IEEE Aerospace Conference  
For this research, we apply two compression algorithms for deployment on modern flight hardware: (1) end-to-end, neural-network-based, image compression (CNN-JPEG); and (2) adaptive image compression through  ...  More efficient image-compression codecs are an emerging requirement for spacecraft because increasingly complex, onboard image sensors can rapidly saturate downlink bandwidth of communication transceivers  ...  Earth-observation satellites are an ideal use case since satellite data is widely available and easily accessible for training the end-to-end neural network.  ... 
doi:10.1109/aero47225.2020.9172536 fatcat:ou6b74ywjnckfjxwgicb7nlx4i

Task-relevant Representation Learning for Networked Robotic Perception [article]

Manabu Nakanoya, Sandeep Chinchali, Alexandros Anemogiannis, Akul Datta, Sachin Katti, Marco Pavone
2020 arXiv   pre-print
Our algorithm aggressively compresses robotic sensory data by up to 11x more than competing methods.  ...  Often, such robots, ranging from low-power drones to space and subterranean rovers, need to transmit high-bitrate sensory data to a remote compute server if they are uncertain or cannot scalably run complex  ...  DISCUSSION AND CONCLUSIONS This paper presents a novel framework to aggressively compress rich robotic sensory data for the ultimate needs of a machine sensing task, which allows robots to reduce on-board  ... 
arXiv:2011.03216v1 fatcat:btsfsmatu5djph3bizfrd2oftm

The deep kernelized autoencoder

Michael Kampffmeyer, Sigurd Løkse, Filippo M. Bianchi, Robert Jenssen, Lorenzo Livi
2018 Applied Soft Computing  
Additionally, we show that our method is capable to emulate kernel principal component analysis on a denoising task, obtaining competitive results at a much lower computational cost.  ...  Autoencoders learn data representations (codes) in such a way that the input is reproduced at the output of the network.  ...  Acknowledgment We gratefully acknowledge the support of NVIDIA Corporation with the donation of the GPU used for this research.  ... 
doi:10.1016/j.asoc.2018.07.029 fatcat:atrii2db7fhafanko36afpz6wy

Review on Semantic Segmentation of Satellite Images Using Deep Learning

Chandra Pal Kushwah
2021 International Journal for Research in Applied Science and Engineering Technology  
Image segmentation for applications like scene understanding, medical image analysis, robotic vision, video tracking, improving reality, and image compression is a key subject of image processing and image  ...  Semantic segmentation is an integral aspect of image comprehension and is essential for image processing tasks. Semantic segmentation is a complex process in computer vision applications.  ...  The network now provides one single differential score feature from raw pixels on one end to class score on the other.  ... 
doi:10.22214/ijraset.2021.37204 fatcat:nroebivs7nfsndxazsuhrmiece

Unsupervised anomaly detection in railway catenary condition monitoring using autoencoders

Hongrui Wang
2020 IECON 2020 The 46th Annual Conference of the IEEE Industrial Electronics Society  
The approach trains autoencoders to reduce the dimensionality of multisensor data and generate discriminative features between healthy and anomalous data.  ...  This study focuses on one of the main railway infrastructures, namely the catenary (overhead line) system that transmits power to trains.  ...  ACKNOWLEDGMENT The author would like to thank Prof.  ... 
doi:10.1109/iecon43393.2020.9254633 dblp:conf/iecon/Wang20 fatcat:jvxg4cco3zbf7aev3xfhdejuji

Cloud Deep Networks for Hyperspectral Image Analysis

J. M. Haut, J. A. Gallardo, M. E. Paoletti, G. Cavallaro, J. Plaza, A. Plaza, M. Riedel
2022 Zenodo  
An important approach to deal with massive volumes of information is data compression, related to how data are compressed before their storage or transmission.  ...  Advances in remote sensing hardware have led to a significantly increased capability for high quality data acquisition, which allows the collection of remotely sensed images with very high spatial, spectral  ...  Last but not least, we gratefully thank the Editors and the Anonymous Reviewers for their outstanding comments and suggestions, that greatly helped us to improve the technical quality and presentation  ... 
doi:10.5281/zenodo.6413835 fatcat:2jzymrq3vrcnbgljzwxms3qlsq

Radar and Sonar Imaging and Processing (2nd Edition)

Andrzej Stateczny, Witold Kazimierski, Krzysztof Kulpa
2021 Remote Sensing  
The 14 papers (from 29 submitted) published in the Special Issue "Radar and Sonar Imaging Processing (2nd Edition)" highlight a variety of topics related to remote sensing with radar and sonar sensors.  ...  The sequence of articles included in the SI deal with a broad profile of aspects of the use of radar and sonar images in line with the latest scientific trends, in which the latest developments in science  ...  Institutional Review Board Statement: Not applicable. Informed Consent Statement: Not applicable.  ... 
doi:10.3390/rs13224656 fatcat:qvxifyoi6fev3ejsbz5hkrcv7a

Deep Learning Meets SAR [article]

Xiao Xiang Zhu, Sina Montazeri, Mohsin Ali, Yuansheng Hua, Yuanyuan Wang, Lichao Mou, Yilei Shi, Feng Xu, Richard Bamler
2021 arXiv   pre-print
With this effort, we hope to stimulate more research in this interesting yet under-exploited research field and to pave the way for use of deep learning in big SAR data processing workflows.  ...  Deep learning in remote sensing has become an international hype, but it is mostly limited to the evaluation of optical data.  ...  Autoencoders and their extensions, such as variational autoencoders (VAEs) and deep embedded clustering algorithms, are popular choices.  ... 
arXiv:2006.10027v2 fatcat:s3tiroz4qve6nbhavtz77fbis4

OUP accepted manuscript

2019 European Review of Agricultural Economics  
We first introduce the key ML methods drawing connections to econometric practice.  ...  Finally, we argue that economists have a vital role in addressing the shortcomings of ML when used for quantitative economic analysis.  ...  Acknowledgements Thanks go to an anonymous reviewer, Patrick Baylis, Robert Brunner, Svetlana Fedoseeva Supplementary data Supplementary data are available at European Review of Agricultural Economics  ... 
doi:10.1093/erae/jbz033 fatcat:qjt3ruptdzhfncytl6l2qm7tji

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

2019 IEEE Transactions on Geoscience and Remote Sensing  
., 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 on Arbitrary  ...  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., and Hanssen, R.F  ...  ., +, TGRS June 2019 4050-4061 Compressed Imaging to Reduce Storage in Adjoint-State Calculations.  ... 
doi:10.1109/tgrs.2020.2967201 fatcat:kpfxoidv5bgcfo36zfsnxe4aj4

Guest Editorial Multimedia Computing With Interpretable Machine Learning

Y. Tian, C. Snoek, J. Wang, Z. Liu, Lienhart, S. Boll
2020 IEEE transactions on multimedia  
CNN and can be embedded into an end-to-end autoencoder.  ...  She serves on the executive board of the OFFIS Institute for Information Technology, in Oldenburg, Germany.  ... 
doi:10.1109/tmm.2020.2991292 fatcat:mdwlpuwarvdtrfiofjbi2j4hu4

AI-Inspired Non-Terrestrial Networks for IIoT: Review on Enabling Technologies and Applications

Emmanouel T. Michailidis, Stelios M. Potirakis, Athanasios G. Kanatas
2020 IoT  
with significantly lower complexity compared to typical optimization methods.  ...  NTNs are engineered to utilize satellites, airships, and aircrafts, which can be employed to extend the radio coverage and provide remote monitoring and sensing services.  ...  Figure 5 . 5 Summary of potential and powerful AI technologies for the NTN-based IIoT that intend to enable end-to-end (E2E) optimization of network operations in complex environments without human intervention  ... 
doi:10.3390/iot1010003 fatcat:xkjxfh6r2fd27jyuxazfc6lbqu
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