Remote sensing image classification using fuzzy- pso hybrid approach
Sarkar, Anasua LU
and Das, Rajib
LU
(2015)
p.435-468
- Abstract
Pixel classification among overlapping land cover regions in remote sensing imagery is a challenging task. Detection of uncertainty and vagueness are always key features for classifying mixed pixels. This chapter proposes an approach for pixel classification using hybrid approach of Fuzzy C-Means and Particle Swarm Optimization methods. This new unsupervised algorithm is able to identify clusters utilizing particle swarm optimization based on fuzzy membership values. This approach addresses overlapping regions in remote sensing images by uncertainties using fuzzy set membership values. PSO is a population-based stochastic optimization technique inspired from the social behavior of bird flocks. The authors demonstrate the algorithm for... (More)
Pixel classification among overlapping land cover regions in remote sensing imagery is a challenging task. Detection of uncertainty and vagueness are always key features for classifying mixed pixels. This chapter proposes an approach for pixel classification using hybrid approach of Fuzzy C-Means and Particle Swarm Optimization methods. This new unsupervised algorithm is able to identify clusters utilizing particle swarm optimization based on fuzzy membership values. This approach addresses overlapping regions in remote sensing images by uncertainties using fuzzy set membership values. PSO is a population-based stochastic optimization technique inspired from the social behavior of bird flocks. The authors demonstrate the algorithm for segmenting a LANDSAT image of Shanghai. The newly developed algorithm is compared with FCM and K-Means algorithms. The new algorithm-generated clustered regions are verified with the available ground truth knowledge. The validity and statistical analysis are performed to demonstrate the superior performance of the new algorithm with K-Means and FCM algorithms.
(Less)
- author
- Sarkar, Anasua
LU
and Das, Rajib
LU
- publishing date
- 2015-04-30
- type
- Chapter in Book/Report/Conference proceeding
- publication status
- published
- host publication
- Handbook of Research on Swarm Intelligence in Engineering
- pages
- 435 - 468
- publisher
- IGI Global
- external identifiers
-
- scopus:84957377918
- ISBN
- 9781466682924
- 1466682914
- 9781466682917
- DOI
- 10.4018/978-1-4666-8291-7.ch014
- language
- English
- LU publication?
- no
- id
- 826aeaf1-6ccc-4e52-ab88-38998b91d95d
- date added to LUP
- 2018-10-09 09:49:15
- date last changed
- 2024-01-15 03:14:51
@inbook{826aeaf1-6ccc-4e52-ab88-38998b91d95d,
abstract = {{<p>Pixel classification among overlapping land cover regions in remote sensing imagery is a challenging task. Detection of uncertainty and vagueness are always key features for classifying mixed pixels. This chapter proposes an approach for pixel classification using hybrid approach of Fuzzy C-Means and Particle Swarm Optimization methods. This new unsupervised algorithm is able to identify clusters utilizing particle swarm optimization based on fuzzy membership values. This approach addresses overlapping regions in remote sensing images by uncertainties using fuzzy set membership values. PSO is a population-based stochastic optimization technique inspired from the social behavior of bird flocks. The authors demonstrate the algorithm for segmenting a LANDSAT image of Shanghai. The newly developed algorithm is compared with FCM and K-Means algorithms. The new algorithm-generated clustered regions are verified with the available ground truth knowledge. The validity and statistical analysis are performed to demonstrate the superior performance of the new algorithm with K-Means and FCM algorithms.</p>}},
author = {{Sarkar, Anasua and Das, Rajib}},
booktitle = {{Handbook of Research on Swarm Intelligence in Engineering}},
isbn = {{9781466682924}},
language = {{eng}},
month = {{04}},
pages = {{435--468}},
publisher = {{IGI Global}},
title = {{Remote sensing image classification using fuzzy- pso hybrid approach}},
url = {{http://dx.doi.org/10.4018/978-1-4666-8291-7.ch014}},
doi = {{10.4018/978-1-4666-8291-7.ch014}},
year = {{2015}},
}