Classification and Segmentation in Satellite Imagery Using Back Propagation Algorithm of ANN and K-Means Algorithm
Open accessAbstract
Now a days unsupervised image classification and segmentation increasingly popular.Existing information system classification tools used the same method for years.These basic classification methods do not provide satisfactory results when it applied on wide database of images. This paper describes the implementation of two algorithms, namely Back Propagation Algorithm of ANN and K-Means Algorithm on wide database of images. It provides the tool for segmentation and classification of remote sensing images. This classified image is given to K-Means Algorithm and Back Propagation Algorithm of ANN to calculate the density count.The density count is stored in database for future reference and for other applications.This tool also has the capability to show the comparison of the results of both the algorithms.High resolution basically means that an image is reproduced with a high level of detail.Usually it is referring to an image that is of very high quality, where there is a lot of detail.
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Greedy algorithm or search is an efficient tool usually employed in optimization problems, especially when dealing with large sets of data.
“Greedy algorithm or search is an efficient tool that is usually employed in optimization problems, especially when dealing with large sets of data [160].”
From Applications and Challenges of Artificial Intelligence in Space Missions (2024) · cites this paper as [160]
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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 · see citing works
“The performance of K-means clustering and neural network with back-propagation for successful image segmentation and classification in satellite images employing dense count values of the pixel intensities were compared in [158].”
MethodAn Overview of Technological Revolution in Satellite Image Analysis
Department of Computer Science & Engineering, K. A. U., Jenice, A. R., Raimond, K., & Department of Computer Science & Engineering, K. A. U. (2016) · Journal of Engineering Science and Technology Review · doi:10.25103/jestr.094.01 · Cited by 11 · see citing works
- Authors
- Sathya, P., & Malathi, L.
- ORCID
- Malathi, L.
- Venue
- International Journal of Machine Learning and Computing
- Publisher
- EJournal Publishing
- Published
- 2011
- Pages
- 422-426
- ISSN
- 2010-3700 (ISSN-L)
- OpenAlex
- W2331846299
- Cited by
- 29
- References
- 0
- Citations by year
- 2025: 1 · 2024: 1 · 2023: 1 · 2021: 2 · 2020: 2 · 2019: 7 · 2018: 4 · 2016: 8 · 2015: 3
- FWCI
- 0.00
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- International Journal of Machine Learning and Computing (publishedVersion)
- Topics
- Physical Sciences › Engineering › Media Technology › Remote-Sensing Image Classification
- Physical Sciences › Earth and Planetary Sciences › Atmospheric Science › Remote Sensing and Land Use
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Keywords
- satellite image classification, image segmentation, unsupervised classification, BP neural network, K-means clustering, remote sensing images, high-resolution imagery, algorithm comparison
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