Hybrid rough-PSO approach in remote sensing imagery analysis
Sarkar, Anasua LU
and Das, Rajib
LU
(2016)
In Studies in Computational Intelligence
611.
p.305-327
- Abstract
Pixel classification among overlapping land cover regions in remote sensing imagery is a very challenging task. Detection of uncertainty and vagueness are always the key features for classifying mixed pixels. This paper proposes an approach for pixel classification using a hybrid approach of rough set theory and particle swarm optimization methods. Rough set theory deals with incompleteness and vagueness among data, which property may be utilized for detecting arbitrarily shaped and sized clusters in satellite images. To enable fast automatic clustering of multispectral remote sensing imagery, in this article, we propose a rough set-based heuristical decision rule generation algorithm. For rough-set-theoretic decision rule generation,... (More)
Pixel classification among overlapping land cover regions in remote sensing imagery is a very challenging task. Detection of uncertainty and vagueness are always the key features for classifying mixed pixels. This paper proposes an approach for pixel classification using a hybrid approach of rough set theory and particle swarm optimization methods. Rough set theory deals with incompleteness and vagueness among data, which property may be utilized for detecting arbitrarily shaped and sized clusters in satellite images. To enable fast automatic clustering of multispectral remote sensing imagery, in this article, we propose a rough set-based heuristical decision rule generation algorithm. For rough-set-theoretic decision rule generation, each cluster is classified using heuristically searched optimal reducts to overcome overlapping cluster problem. This proposed unsupervised algorithm is able to identify clusters utilizing particle swarm optimization based on rough set generated membership values. This approach addresses the overlapping regions in remote sensing images by uncertainties using rough set generated membership values. Particle swarm optimization is a population-based stochastic optimization technique, inspired from the social behavior of bird flock. Therefore, to predict pixel classification of remote sensing imagery, we propose a particle swarm optimization-based membership correction approach over rough set-based initial decision rule generation. We demonstrate our algorithm for segmenting a LANDSAT image of the catchment area of Ajoy River. The newly developed algorithm is compared with fuzzy C-means and K-means algorithms. The new algorithm generated clustered regions are verified with the available ground truth knowledge. The validity analysis is performed to demonstrate the superior performance of our new algorithms with K-means and fuzzy C-means algorithms.
(Less)
- author
- Sarkar, Anasua
LU
and Das, Rajib
LU
- publishing date
- 2016
- type
- Chapter in Book/Report/Conference proceeding
- publication status
- published
- keywords
- Particle swarm optimization, Pixel classification, Remote sensing, Rough membership value, Rough set
- host publication
- Hybrid Soft Computing Approaches : Research and Applications - Research and Applications
- series title
- Studies in Computational Intelligence
- volume
- 611
- pages
- 23 pages
- external identifiers
-
- scopus:84940183164
- ISSN
- 1860-949X
- ISBN
- 9788132225430
- 9788132225447
- DOI
- 10.1007/978-81-322-2544-7_10
- language
- English
- LU publication?
- no
- id
- 6943be9d-cc0a-45f1-ac4d-a21694669fdc
- date added to LUP
- 2018-10-09 09:43:51
- date last changed
- 2024-03-02 03:40:26
@inbook{6943be9d-cc0a-45f1-ac4d-a21694669fdc,
abstract = {{<p>Pixel classification among overlapping land cover regions in remote sensing imagery is a very challenging task. Detection of uncertainty and vagueness are always the key features for classifying mixed pixels. This paper proposes an approach for pixel classification using a hybrid approach of rough set theory and particle swarm optimization methods. Rough set theory deals with incompleteness and vagueness among data, which property may be utilized for detecting arbitrarily shaped and sized clusters in satellite images. To enable fast automatic clustering of multispectral remote sensing imagery, in this article, we propose a rough set-based heuristical decision rule generation algorithm. For rough-set-theoretic decision rule generation, each cluster is classified using heuristically searched optimal reducts to overcome overlapping cluster problem. This proposed unsupervised algorithm is able to identify clusters utilizing particle swarm optimization based on rough set generated membership values. This approach addresses the overlapping regions in remote sensing images by uncertainties using rough set generated membership values. Particle swarm optimization is a population-based stochastic optimization technique, inspired from the social behavior of bird flock. Therefore, to predict pixel classification of remote sensing imagery, we propose a particle swarm optimization-based membership correction approach over rough set-based initial decision rule generation. We demonstrate our algorithm for segmenting a LANDSAT image of the catchment area of Ajoy River. The newly developed algorithm is compared with fuzzy C-means and K-means algorithms. The new algorithm generated clustered regions are verified with the available ground truth knowledge. The validity analysis is performed to demonstrate the superior performance of our new algorithms with K-means and fuzzy C-means algorithms.</p>}},
author = {{Sarkar, Anasua and Das, Rajib}},
booktitle = {{Hybrid Soft Computing Approaches : Research and Applications}},
isbn = {{9788132225430}},
issn = {{1860-949X}},
keywords = {{Particle swarm optimization; Pixel classification; Remote sensing; Rough membership value; Rough set}},
language = {{eng}},
pages = {{305--327}},
series = {{Studies in Computational Intelligence}},
title = {{Hybrid rough-PSO approach in remote sensing imagery analysis}},
url = {{http://dx.doi.org/10.1007/978-81-322-2544-7_10}},
doi = {{10.1007/978-81-322-2544-7_10}},
volume = {{611}},
year = {{2016}},
}