Spectral–Spatial Classification of Hyperspectral Imagery with 3D Convolutional Neural Network
- Submitting institution
-
Aberystwyth University / Prifysgol Aberystwyth
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 10373441
- Type
- D - Journal article
- DOI
-
10.3390/rs9010067
- Title of journal
- Remote Sensing
- Article number
- 67
- First page
- -
- Volume
- 9
- Issue
- 1
- ISSN
- 2072-4292
- Open access status
- Compliant
- Month of publication
- January
- Year of publication
- 2017
- URL
-
-
- Supplementary information
-
-
- Request cross-referral to
- -
- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
- -
- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- No
- Number of additional authors
-
2
- Research group(s)
-
-
- Citation count
- 141
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- Presents an original technique for enabling deep learning networks to employ both spectral and spatial information for remote sensing image classification. Attracted worldwide attention and ranked by the journal Remote Sensing as the most cited article amongst all those papers published within their first 3 years, and as one of the top 20 best cited articles of this prestigious journal at all times (https://www.mdpi.com/search?q=&journal=remotesensing&sort=article_citedby&page_count=50). Inspired considerable follow-on developments, including ten that have each attracted over 100 cites themselves.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -
