Algorithms for semantic segmentation of multispectral remote sensing imagery using deep learning
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Results on datasets with higher levels of difficulty (e.g., RIT-18) show that the improvement of networks for semantic segmentation needs to be further explored in the future.
“However, the results on datasets with higher levels of difficulty, e.g., RIT-18 (Kemker et al., 2018) show that the improvement of networks for semantic segmentation need to be further explored in the future.”
From Deep learning in remote sensing applications: A meta-analysis and review (2019) · cites this paper as Kemker et al., 2018
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- Authors
- Kemker, R., Salvaggio, C., & Kanan, C.
- Affiliations
- Rochester Institute of Technology (US)
- Corresponding author
- Kanan, Christopher
- Venue
- ISPRS Journal of Photogrammetry and Remote Sensing
- Publisher
- Elsevier BV
- Published
- 2018-11
- Volume
- 145
- Pages
- 60-77
- ISSN
- 0924-2716 (ISSN-L)
- OpenAlex
- W2760340275
- Cited by
- 558
- References
- 63
- Citations by year
- 2026: 22 · 2025: 73 · 2024: 93 · 2023: 89 · 2022: 74 · 2021: 76 · 2020: 75 · 2019: 41 · 2018: 14 · 2017: 1
- FWCI
- 39.03
- Type
- journal article
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- ISPRS Journal of Photogrammetry and Remote Sensing (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Topics
- Physical Sciences › Engineering › Media Technology › Remote-Sensing Image Classification
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Physical Sciences › Earth and Planetary Sciences › Atmospheric Science › Remote Sensing and Land Use
- Keywords
- semantic segmentation, multispectral imagery, deep convolutional neural networks, synthetic images, limited labeled data, network initialization, very high resolution imagery, overfitting
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