GPU accelerated waterpixel algorithm for superpixel segmentation of hyperspectral images-Reference-Cited by-同舟云学术

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GPU accelerated waterpixel algorithm for superpixel segmentation of hyperspectral images

Published:2021-02-22 Issue:9 Volume:77 Page:10040-10052
ISSN:0920-8542
Container-title:The Journal of Supercomputing
language:en
Short-container-title:J Supercomput

Author:

Quesada-Barriuso Pablo^ORCID,Blanco Heras Dora^ORCID,Argüello Francisco^ORCID

Funder

Junta de Castilla y León

Conselleri’a de Educación, Universidade e Formación Profesional

Ministerio de Ciencia e Innovación, Government of Spain

Publisher

Springer Science and Business Media LLC

Subject

Hardware and Architecture,Information Systems,Theoretical Computer Science,Software

Link

https://link.springer.com/content/pdf/10.1007/s11227-021-03666-y.pdf

Reference23 articles.

1. Goetz Alexander FH, Gregg Vane, Solomon Jerry E, Rock Barrett N (1985) Imaging spectrometry for earth remote sensing. Science 228(4704):1147–1153

2. Feng Quanlong, Liu Jiantao, Gong Jianhua (2015) Uav remote sensing for urban vegetation mapping using random forest and texture analysis. Remote Sens 7(1):1074–1094

3. Plaza A, Du Q, Chang Y, King RL (2011) High performance computing for hyperspectral remote sensing. IEEE J Sel Top Appl Earth Obs Remote Sens 4(3):528–544

4. Shraddha Tripathi, Krishna Kumar, Singh BK, Singh RP (2012) Image segmentation: a review. Int J Computer Sci Manag Res 1(4):838–843

5. Stutz David, Hermans Alexander, Leibe Bastian (2018) Superpixels: an evaluation of the state-of-the-art. Computer Vis Image Underst 166:1–27

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1. State of the Art: High-Performance and High-Throughput Computing for Remote Sensing Big Data;IEEE Geoscience and Remote Sensing Magazine;2022-12

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