The DOI matches its Crossref record; 88 of the 93 references it cites were found in a registry or an index.
Remote Sensing Satellite Image Processing Techniques for Image Classification: A Comprehensive Survey
Open accessAbstract
This paper is a brief survey of advance technological aspects of Digital Image Processing which are applied to remote sensing images obtained from various satellite sensors.In remote sensing, the image processing techniques can be categories in to four main processing stages: Image preprocessing, Enhancement, Transformation and Classification.Image pre-processing is the initial processing which deals with correcting radiometric distortions, atmospheric distortion and geometric distortions present in the raw image data.Enhancement techniques are applied to preprocessed data in order to effectively display the image for visual interpretation.It includes techniques to effectively distinguish surface features for visual interpretation.Transformation aims to identify particular feature of earth's surface and classification is a process of grouping the pixels, that produces effective thematic map of particular land use and land cover.
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“In addition, satellite sensor variations (such as radiometric resolution, spatial, and spectra) and a change of viewpoint bring tremendous diversities into satellite image representations [143].”
From Applications and Challenges of Artificial Intelligence in Space Missions (2024) · cites this paper as [143]
Citations
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Cited by
Applications and Challenges of Artificial Intelligence in Space Missions
Oche, P. A., Ewa, G. A., & Ibekwe, N. (2024) · IEEE Access · doi:10.1109/access.2021.3132500 · Cited by 68
“2) CLASSIFICATION In a general sense, image classification can be defined as ―the process of categorizing all pixels in an image or raw remotely sensed satellite data to obtain a given set of labels or land cover themes [141].”
BackgroundMollick, T., Azam, M. G., & Karim, S. (2022) · Remote Sensing Applications Society and Environment · doi:10.1016/j.rsase.2022.100859 · Cited by 51
Deep Transfer Learning Technique for Multimodal Disease Classification in Plant Images
Balaji, V., Anushkannan, N. K., Narahari, S. C., Rattan, P., Verma, D., Awasthi, D. K., Pandian, A. A., Veeramanickam, M. R. M., & Mulat, M. B. (2023) · Contrast Media & Molecular Imaging · doi:10.1155/2023/5644727 · Cited by 42
Effect of Climate Change on Wetland Areas in West Iraq Using Satellite Data and GIS Techniques
Hassan, W. H., Khazaal, S. T., & Alshammari, M. H. (2024) · Civil Engineering Journal · doi:10.28991/cej-2024-010-09-013 · Cited by 40
Gebresellase, S. H., Wu, Z., Xu, H., & Muhammad, W. I. (2023) · Sustainability · doi:10.3390/su15021683 · Cited by 31
Metadata
- Authors
- D., S., Deepa, P., & K., V.
- Affiliations
- Bangalore University (IN)
- Venue
- International Journal of Computer Applications
- Publisher
- Foundation of Computer Science
- Published
- 2017-03-15
- Volume
- 161
- Issue
- 11
- Pages
- 24-37
- ISSN
- 0975-8887 (ISSN-L)
- OpenAlex
- W2617111643
- Cited by
- 65
- References
- 93
- Citations by year
- 2026: 7 · 2025: 10 · 2024: 10 · 2023: 11 · 2022: 10 · 2021: 5 · 2020: 11 · 2019: 1
- FWCI
- 2.72
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- International Journal of Computer Applications (publishedVersion)
- ePrints@Bangalore University (Bangalore University) (submittedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Remote-Sensing Image Classification
- Social Sciences › Geography, Planning and Development › Remote Sensing and Land Use
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Satellite Image Processing and Photogrammetry
- Keywords
- remote sensing image processing, image classification, image preprocessing, image enhancement, image transformation, radiometric correction, atmospheric distortion correction, geometric distortion correction, visual interpretation, land use and land cover mapping
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
