Indian River Watershed Image Analysis Using Fuzzy-CA Hybrid Approach

IF 1.3 Q4 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS International Journal of Agricultural and Environmental Information Systems Pub Date : 2019-01-01 DOI:10.4018/978-1-5225-0498-6.CH008
K. Mahata, Subhasish Das, R. Das, Anasua Sarkar
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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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基于模糊- ca混合方法的印度河流域图像分析
卫星图像中重叠土地覆盖区域的图像分割是一项非常关键的任务。归属检测是混合像素分类的重要问题。本文提出了一种混合模糊c均值和元胞自动机方法的像素分类方法。该方法采用基于模糊分割的二维元胞自动机模型进行聚类检测。该方法利用模糊集隶属度值的不确定性来检测遥感图像中的重叠区域。作为一个离散的动态系统,元胞自动机探索具有状态的均匀相互连接的细胞。在我们的方法的第二阶段,我们利用二维元胞自动机来优先分配重叠的土地覆盖区域之间的混合像素。我们在印度阿乔伊河流域进行了试验。将聚类区域与已知的FCM和K-Means方法以及地面真值知识进行比较。结果表明了新方法的优越性。
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来源期刊
International Journal of Agricultural and Environmental Information Systems
International Journal of Agricultural and Environmental Information Systems COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS-
CiteScore
6.70
自引率
0.00%
发文量
10
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