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Indian River Watershed Image Analysis Using Fuzzy-CA Hybrid Approach

Indian River Watershed Image Analysis Using Fuzzy-CA Hybrid Approach

Kalyan Mahata, Subhasish Das, Rajib Das, Anasua Sarkar
Copyright: © 2017 |Pages: 15
ISBN13: 9781522504986|ISBN10: 1522504982|EISBN13: 9781522504993
DOI: 10.4018/978-1-5225-0498-6.ch008
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MLA

Mahata, Kalyan, et al. "Indian River Watershed Image Analysis Using Fuzzy-CA Hybrid Approach." Intelligent Analysis of Multimedia Information, edited by Siddhartha Bhattacharyya, et al., IGI Global, 2017, pp. 232-246. https://doi.org/10.4018/978-1-5225-0498-6.ch008

APA

Mahata, K., Das, S., Das, R., & Sarkar, A. (2017). Indian River Watershed Image Analysis Using Fuzzy-CA Hybrid Approach. In S. Bhattacharyya, H. Bhaumik, S. De, & G. Klepac (Eds.), Intelligent Analysis of Multimedia Information (pp. 232-246). IGI Global. https://doi.org/10.4018/978-1-5225-0498-6.ch008

Chicago

Mahata, Kalyan, et al. "Indian River Watershed Image Analysis Using Fuzzy-CA Hybrid Approach." In Intelligent Analysis of Multimedia Information, edited by Siddhartha Bhattacharyya, et al., 232-246. Hershey, PA: IGI Global, 2017. https://doi.org/10.4018/978-1-5225-0498-6.ch008

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Abstract

Image segmentation among overlapping land cover areas in satellite images is a very crucial task. Detection of belongingness is the important problem for classifying mixed pixels. This paper proposes an approach for pixel classification using a hybrid approach of Fuzzy C-Means and Cellular automata methods. This new unsupervised method is able to detect clusters using 2-Dimensional Cellular Automata model based on fuzzy segmentations. This approach detects the overlapping regions in remote sensing images by uncertainties using fuzzy set membership values. As a discrete, dynamical system, cellular automaton explores uniformly interconnected cells with states. In the second phase of our method, we utilize a 2-dimensional cellular automata to prioritize allocations of mixed pixels among overlapping land cover areas. We experiment our method on Indian Ajoy river watershed area. The clustered regions are compared with well-known FCM and K-Means methods and also with the ground truth knowledge. The results show the superiority of our new method.

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