Cloud Deep Networks for Hyperspectral Image Analysis
Haut, J.M., Gallardo, J.A., Paoletti, M.E., Cavallaro, G., Plaza, J., Plaza, A., Riedel, M.
IEEE Transactions on Geoscience and Remote Sensing, PP(99):1-17, 2019
[ JOURNAL ] [ DOI ] [ GOOGLE SCHOLAR ] [ RESEARCHGATE ]
Abstract:
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, and radiometric resolution. This trend calls for the development of new techniques to enhance the way that such unprecedented volumes of data are stored, processed, and analyzed. An important approach to deal with massive volumes of information is data compression, related to how data are compressed before their storage or transmission. For instance, hyperspectral images (HSIs) are characterized by hundreds of spectral bands. In this sense, high-performance computing (HPC) and high-throughput computing (HTC) offer interesting alternatives. Particularly, distributed solutions based on cloud computing can manage and store huge amounts of data in fault-tolerant environments, by interconnecting distributed computing nodes so that no specialized hardware is needed. This strategy greatly reduces the processing costs, making the processing of high volumes of remotely sensed data a natural and even cheap solution. In this paper, we present a new cloud-based technique for spectral analysis and compression of HSIs. Specifically, we develop a cloud implementation of a popular deep neural network for non-linear data compression, known as autoencoder (AE). Apache Spark serves as the backbone of our cloud computing environment by connecting the available processing nodes using a master-slave architecture. Our newly developed approach has been tested using two widely available HSI data sets. Experimental results indicate that cloud computing architectures offer an adequate solution for managing big remotely sensed data sets.
Social Media
Researchgate: Congratulations, Morris! Your article reached 400 reads #Cloud Deep Networks for Hyperspectral Image Analysis @ApacheSpark #Remotesensing #DeepLearning @fz_juelich @fzj_jsc @Haskoli_Islands @uni_iceland @DEEPprojects @helmholtz_ai
full text: https://t.co/QlGsvtFeGP pic.twitter.com/0yNRjfuaZq— Morris Riedel (@MorrisRiedel) April 25, 2020
Researchgate: Great Work, Morris! Your article reached 200 reads: Cloud Deep Networks for Hyperspectral Image Analysis @ApacheSpark #autoencoder #Remotesensing #DeepLearning @fz_juelich @fzj_jsc @Haskoli_Islands @uisens @uni_iceland @DEEPprojects
full text https://t.co/QlGsvtFeGP pic.twitter.com/UOi7Nmkg01— Morris Riedel (@MorrisRiedel) August 27, 2019
Our new paper in IEEE Transactions on Geoscience & Remote Sensing led by J. Haut using @ApacheSpark & #autoencoder:
Cloud Deep Networks for Hyperspectral Image Analysis
Preprint at: https://t.co/QlGsvtFeGP @fz_juelich @fzj_jsc @Haskoli_Islands @uisens @uni_iceland @DEEPprojects pic.twitter.com/Gf1t3BwkHW
— Morris Riedel (@MorrisRiedel) August 17, 2019
https://orcid.org/0000-0003-1810-9330