High-efficiency scene classification based on deep compressed-domain feature
- Author(s): Cheng Li 1, 2 ; Baojun Zhao 1, 2 ; Boya Zhao 1, 2 ; Wenzheng Wang 1, 2 ; Chenhui Duan 1, 2
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View affiliations
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Affiliations:
1:
Radar Research Lab, School of Information and Electronics , Beijing Institute of Technology , Beijing 100081 , People's Republic of China ;
2: Beijing Key laboratory of Embedded Real-time Information Processing Technology , Beijing Institute of technology , Beijing 100081 , People's Republic of China
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Affiliations:
1:
Radar Research Lab, School of Information and Electronics , Beijing Institute of Technology , Beijing 100081 , People's Republic of China ;
- Source:
Volume 2019, Issue 19,
October
2019,
p.
6077 – 6080
DOI: 10.1049/joe.2019.0266 , Online ISSN 2051-3305
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Inspec keywords: data compression; image classification; convolutional neural nets; geophysical image processing; image representation; image coding; feature extraction; remote sensing
Other keywords: body information; compressed-domain convolutional neural network; remote sensing image scene classification; effective classification; high classification accuracy; computing resources; header information; raw JPEG2000 codestream; deep feature representation framework; in-orbit processing capacity; UAV time-sensitive application; RSI; satellite application; high-level representation; compressed-domain feature; high-efficiency scene classification
Subjects: Geography and cartography computing; Computer vision and image processing techniques; Data and information; acquisition, processing, storage and dissemination in geophysics; Instrumentation and techniques for geophysical, hydrospheric and lower atmosphere research; Neural computing techniques; Image recognition; Image and video coding; Geophysical techniques and equipment
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