Watershed image analysis using a PSO-CA hybrid approach
Mahata, Kalyan ; Das, Rajib LU
; Das, Subhasish
and Sarkar, Anasua
LU
(2017)
p.229-246
- Abstract
Pixel classification of watershed satellite image is a challenging task in remote sensing. Uses of Particle Swarm Optimisation and Cellular Automata are significant methods in watershed image segmentation. This paper proposes a method of pixel classification using a new hybrid Particle Swarm Optimization-Cellular Automata approach. The proposed unsupervised method identifies clusters using 2-Dimensional Cellular Automata model over particle swarm optimization. PSO is an optimization stochastic method based on populations, following the social behavior like bird flocks. This new method identifies vague clusters utilizing initial fuzzy membership values. Cellular Automata is a dynamic and discrete model comprises of inter-connected cells... (More)
Pixel classification of watershed satellite image is a challenging task in remote sensing. Uses of Particle Swarm Optimisation and Cellular Automata are significant methods in watershed image segmentation. This paper proposes a method of pixel classification using a new hybrid Particle Swarm Optimization-Cellular Automata approach. The proposed unsupervised method identifies clusters using 2-Dimensional Cellular Automata model over particle swarm optimization. PSO is an optimization stochastic method based on populations, following the social behavior like bird flocks. This new method identifies vague clusters utilizing initial fuzzy membership values. Cellular Automata is a dynamic and discrete model comprises of inter-connected cells uniforming with states. We utilize the 2D Cellular Automata method on the Barakar River catchment area. The segmented regions are compared with existing methods which shows superiority of our new method.
(Less)
- author
- Mahata, Kalyan
; Das, Rajib
LU
; Das, Subhasish
and Sarkar, Anasua
LU
- publishing date
- 2017-01-01
- type
- Chapter in Book/Report/Conference proceeding
- publication status
- published
- host publication
- Hybrid Intelligent Techniques for Pattern Analysis and Understanding
- pages
- 18 pages
- publisher
- CRC Press
- external identifiers
-
- scopus:85052699988
- ISBN
- 9781498769358
- 9781498769372
- DOI
- 10.1201/9781315154152
- language
- English
- LU publication?
- no
- id
- 8c29824f-3889-45c0-bae1-4ccb1fa54c28
- date added to LUP
- 2018-10-09 09:43:26
- date last changed
- 2024-03-02 03:19:59
@inbook{8c29824f-3889-45c0-bae1-4ccb1fa54c28,
abstract = {{<p>Pixel classification of watershed satellite image is a challenging task in remote sensing. Uses of Particle Swarm Optimisation and Cellular Automata are significant methods in watershed image segmentation. This paper proposes a method of pixel classification using a new hybrid Particle Swarm Optimization-Cellular Automata approach. The proposed unsupervised method identifies clusters using 2-Dimensional Cellular Automata model over particle swarm optimization. PSO is an optimization stochastic method based on populations, following the social behavior like bird flocks. This new method identifies vague clusters utilizing initial fuzzy membership values. Cellular Automata is a dynamic and discrete model comprises of inter-connected cells uniforming with states. We utilize the 2D Cellular Automata method on the Barakar River catchment area. The segmented regions are compared with existing methods which shows superiority of our new method.</p>}},
author = {{Mahata, Kalyan and Das, Rajib and Das, Subhasish and Sarkar, Anasua}},
booktitle = {{Hybrid Intelligent Techniques for Pattern Analysis and Understanding}},
isbn = {{9781498769358}},
language = {{eng}},
month = {{01}},
pages = {{229--246}},
publisher = {{CRC Press}},
title = {{Watershed image analysis using a PSO-CA hybrid approach}},
url = {{http://dx.doi.org/10.1201/9781315154152}},
doi = {{10.1201/9781315154152}},
year = {{2017}},
}