Recent Advances in Hyperspectral Unmixing Using Sparse Techniques and Deep Learning

Zhang, Shaoquan; Su, Yuanchao; Xu, Xinhe; Li, Jun; Deng, Chengzhi

doi:10.1007/978-3-030-38617-7_13

Cited by 3 publications

(1 citation statement)

References 43 publications

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“…In this context, many tools have been developed to automatize the programming and execution of deep learning algorithms in GPU-based architectures, among which TensorFlow is the most popular option [108]. This has contributed to the extensive use of GPUs for deep learning applied to RS for many operations [109], [110] including, for example, object detection [111] or classification [112]- [116].…”

Section: Specialized Hardware Computingmentioning

confidence: 99%

High-Performance and Disruptive Computing in Remote Sensing: HDCRS—A new Working Group of the GRSS Earth Science Informatics Technical Committee [Technical Committees]

Cavallaro

Heras

et al. 2022

IEEE Geosci. Remote Sens. Mag.

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T he High-Performance and Disruptive Computing in Remote Sensing (HDCRS) Working Group (WG) was recently established under the IEEE Geoscience and Remote Sensing Society (GRSS) Earth Science Informatics (ESI) Technical Committee to connect a community of interdisciplinary researchers in remote sensing (RS) who specialize in advanced computing technologies, parallel programming models, and scalable algorithms. HDCRS focuses on three major research topics in the context of RS: 1) supercomputing and distributed computing, 2) specialized hardware computing, and 3) quantum computing (QC). This article presents these computing technologies as they play a major role for the development of RS applications. The HDCRS disseminates information and knowledge through educational events and publication activities which will also be introduced in this article.

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