A hybrid MLP-CNN classifier for very fine resolution remotely sensed image classification
- Submitting institution
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University of Southampton
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 34075877
- Type
- D - Journal article
- DOI
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10.1016/j.isprsjprs.2017.07.014
- Title of journal
- ISPRS Journal of Photogrammetry and Remote Sensing
- Article number
- -
- First page
- 133
- Volume
- 140
- Issue
- -
- ISSN
- 0924-2716
- Open access status
- Compliant
- Month of publication
- August
- 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
-
6
- Research group(s)
-
-
- Citation count
- 105
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- We are involved in a interdisciplinary collaboration involving Ordnance Survey and the Lancaster Environment Centre. This unique work, which funded a PDRA and two PhD students, investigates novel neural networks for analysis of remotely sensed images, and bridges geography and CS research. This paper is just one of many outcomes of our collaboration, which has resulted in publications in top venues (IEEE Trans Geoscience and Remote Sensing [https://doi.org/10.1109/TGRS.2018.2822783], Remote sensing of environment [https://doi.org/10.1016/j.rse.2018.11.014, https://doi.org/10.1016/j.rse.2018.06.034], etc) as well as a patent application (GB1702095.9). Our "Imaging Technologies" impact case study demonstrates significant business change innovation at OS that resulted from this.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -
