Automatic mixed pixel detection using a new hybrid Cellular automata approach on satellite image
(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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- author
- Mahata, Kalyan ; Das, Rajib LU ; Das, Subhasish and Sarkar, Anasua LU
- publishing date
- 2017-10-19
- 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
- IEEE - 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
- 2022-02-15 05:13:09
@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}}, keywords = {{Cellular Automata; Pixel Classification; Remote Sensing; River Catchment Analysis}}, language = {{eng}}, month = {{10}}, publisher = {{IEEE - 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}}, }