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Automatic mixed pixel detection using a new hybrid Cellular automata approach on satellite image

Mahata, Kalyan ; Das, Rajib LU ; Das, Subhasish and Sarkar, Anasua LU (2017) 1st International Conference on Electronics, Materials Engineering and Nano-Technology, IEMENTech 2017
Abstract

Mixed-pixels classification in land-cover regions is a challenging task in remote sensing imagery. To classify mixed-pixels, vagueness is always the main characteristic by handling uncertainty. We propose a hybrid approach for pixel classification using Rough sets and Cellular automata models to solve this problem. Multiple belongingness and vagueness among data can be handled efficiently using Rough set theory and is appropriate for detecting arbitrarily-shaped clusters in satellite images. We propose a rough-set based automatic heuristically decision-rule generation algorithm to obtain initial set of clusters. As a discrete, dynamical system, cellular automaton comprises of uniformly interconnected cells with states. In the second... (More)

Mixed-pixels classification in land-cover regions is a challenging task in remote sensing imagery. To classify mixed-pixels, vagueness is always the main characteristic by handling uncertainty. We propose a hybrid approach for pixel classification using Rough sets and Cellular automata models to solve this problem. Multiple belongingness and vagueness among data can be handled efficiently using Rough set theory and is appropriate for detecting arbitrarily-shaped clusters in satellite images. We propose a rough-set based automatic heuristically decision-rule generation algorithm to obtain initial set of clusters. As a discrete, dynamical system, cellular automaton comprises of uniformly interconnected cells with states. In the second phase of our method, we utilize a 2-dimensional cellular automaton to prioritize allocations of mixed pixels among overlapping land cover regions. We experiment our algorithm on Ajoy river catchment area. The segmented regions are compared with well-known FCM and K-Means methods and the ground truth knowledge, which shows superiority of our new approach.

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Please use this url to cite or link to this publication:
author
; ; and
publishing date
type
Chapter in Book/Report/Conference proceeding
publication status
published
keywords
Cellular Automata, Pixel Classification, Remote Sensing, River Catchment Analysis
host publication
2017 1st International Conference on Electronics, Materials Engineering and Nano-Technology, IEMENTech 2017
editor
Taki, G. S.
article number
8076985
publisher
Institute of Electrical and Electronics Engineers Inc.
conference name
1st International Conference on Electronics, Materials Engineering and Nano-Technology, IEMENTech 2017
conference location
Science City, Kolkata, India
conference dates
2017-04-28 - 2017-04-29
external identifiers
  • scopus:85039961190
ISBN
9781509053346
DOI
10.1109/IEMENTECH.2017.8076985
language
English
LU publication?
no
id
45aafce3-c5bc-4bd2-8786-80bc98435e7e
date added to LUP
2018-10-09 09:43:05
date last changed
2020-12-22 03:30:12
@inproceedings{45aafce3-c5bc-4bd2-8786-80bc98435e7e,
  abstract     = {<p>Mixed-pixels classification in land-cover regions is a challenging task in remote sensing imagery. To classify mixed-pixels, vagueness is always the main characteristic by handling uncertainty. We propose a hybrid approach for pixel classification using Rough sets and Cellular automata models to solve this problem. Multiple belongingness and vagueness among data can be handled efficiently using Rough set theory and is appropriate for detecting arbitrarily-shaped clusters in satellite images. We propose a rough-set based automatic heuristically decision-rule generation algorithm to obtain initial set of clusters. As a discrete, dynamical system, cellular automaton comprises of uniformly interconnected cells with states. In the second phase of our method, we utilize a 2-dimensional cellular automaton to prioritize allocations of mixed pixels among overlapping land cover regions. We experiment our algorithm on Ajoy river catchment area. The segmented regions are compared with well-known FCM and K-Means methods and the ground truth knowledge, which shows superiority of our new approach.</p>},
  author       = {Mahata, Kalyan and Das, Rajib and Das, Subhasish and Sarkar, Anasua},
  booktitle    = {2017 1st International Conference on Electronics, Materials Engineering and Nano-Technology, IEMENTech 2017},
  editor       = {Taki, G. S.},
  isbn         = {9781509053346},
  language     = {eng},
  month        = {10},
  publisher    = {Institute of Electrical and Electronics Engineers Inc.},
  title        = {Automatic mixed pixel detection using a new hybrid Cellular automata approach on satellite image},
  url          = {http://dx.doi.org/10.1109/IEMENTECH.2017.8076985},
  doi          = {10.1109/IEMENTECH.2017.8076985},
  year         = {2017},
}