Landcover change detection using PSO-evaluated quantum CA approach on multi-temporal remote-sensing watershed images
Mahata, Kalyan ; Das, Rajib LU
; Das, Subhasish
and Sarkar, Anasua
LU
(2018)
p.178-212
- Abstract
Computer science plays a major role in image segmentation and image processing applications. Despite the computational cost, PSO evaluated QCA approaches perform comparable to or better than their crisp counterparts. This novel approach, proposed in this chapter, has been found to enhance the functionality of the CA rule base and thus enhance the established potentiality of the fuzzy-based segmentation domain with the help of quantum cellular automata. This new unsupervised method is able to detect clusters using 2-dimensional quantum cellular automata model based on PSO evaluation. As a discrete, dynamical system, cellular automaton explores uniformly interconnected cells with states. In the second phase, it utilizes a 2-dimensional... (More)
Computer science plays a major role in image segmentation and image processing applications. Despite the computational cost, PSO evaluated QCA approaches perform comparable to or better than their crisp counterparts. This novel approach, proposed in this chapter, has been found to enhance the functionality of the CA rule base and thus enhance the established potentiality of the fuzzy-based segmentation domain with the help of quantum cellular automata. This new unsupervised method is able to detect clusters using 2-dimensional quantum cellular automata model based on PSO evaluation. As a discrete, dynamical system, cellular automaton explores uniformly interconnected cells with states. In the second phase, it utilizes a 2-dimensional cellular automata to prioritize allocations of mixed pixels among overlapping land cover areas. The authors experiment on Tilaya Reservoir Catchment on Barakar River. The clustered regions are compared with well-known PSO, FCM, and k-means methods and also with the ground truth knowledge. The results show the superiority of the new method.
(Less)
- author
- Mahata, Kalyan
; Das, Rajib
LU
; Das, Subhasish
and Sarkar, Anasua
LU
- publishing date
- 2018-04-13
- type
- Chapter in Book/Report/Conference proceeding
- publication status
- published
- host publication
- Quantum-Inspired Intelligent Systems for Multimedia Data Analysis
- pages
- 35 pages
- publisher
- IGI Global
- external identifiers
-
- scopus:85049550821
- ISBN
- 1522552197
- 9781522552192
- 9781522552208
- DOI
- 10.4018/978-1-5225-5219-2.ch006
- language
- English
- LU publication?
- no
- id
- 7c5f1397-0ed2-48b1-b28c-d0a6ba68e424
- date added to LUP
- 2018-10-09 09:42:41
- date last changed
- 2024-06-10 19:09:12
@inbook{7c5f1397-0ed2-48b1-b28c-d0a6ba68e424,
abstract = {{<p>Computer science plays a major role in image segmentation and image processing applications. Despite the computational cost, PSO evaluated QCA approaches perform comparable to or better than their crisp counterparts. This novel approach, proposed in this chapter, has been found to enhance the functionality of the CA rule base and thus enhance the established potentiality of the fuzzy-based segmentation domain with the help of quantum cellular automata. This new unsupervised method is able to detect clusters using 2-dimensional quantum cellular automata model based on PSO evaluation. As a discrete, dynamical system, cellular automaton explores uniformly interconnected cells with states. In the second phase, it utilizes a 2-dimensional cellular automata to prioritize allocations of mixed pixels among overlapping land cover areas. The authors experiment on Tilaya Reservoir Catchment on Barakar River. The clustered regions are compared with well-known PSO, FCM, and k-means methods and also with the ground truth knowledge. The results show the superiority of the new method.</p>}},
author = {{Mahata, Kalyan and Das, Rajib and Das, Subhasish and Sarkar, Anasua}},
booktitle = {{Quantum-Inspired Intelligent Systems for Multimedia Data Analysis}},
isbn = {{1522552197}},
language = {{eng}},
month = {{04}},
pages = {{178--212}},
publisher = {{IGI Global}},
title = {{Landcover change detection using PSO-evaluated quantum CA approach on multi-temporal remote-sensing watershed images}},
url = {{http://dx.doi.org/10.4018/978-1-5225-5219-2.ch006}},
doi = {{10.4018/978-1-5225-5219-2.ch006}},
year = {{2018}},
}