Abstract
Abstract Image segmentation in land cover regions which are overlapping in satellite imagery, is one crucial challenge. To detect true belonging of one pixel becomes a challenging problem while classifying mixed pixels in overlapping regions. In current work, we propose one new approach for image segmentation using a hybrid algorithm of K-Means and Cellular Automata algorithms. This newly implemented unsupervised model can detect cluster groups using hybrid 2-Dimensional Cellular-Automata model based on K-Means segmentation approach. This approach detects different land use land cover areas in satellite imagery by existing K-Means algorithm. Since it is a discrete dynamical system, cellular automaton realizes uniform interconnecting cells containing states. In the second stage of current model, we experiment with a 2-dimensional cellular automata to rank allocations of pixels among different land-cover regions. The method is experimented on the watershed area of Ajoy river (India) and Salinas (California) data set with true class labels using two internal and four external validity indices. The segmented areas are then compared with existing FCM, DBSCAN and K-Means methods and verified with the ground truth. The statistical analysis results also show the superiority of the new method.
Highlights
Cogalton and Green in 1999 introduced Remote sensing to be a method forgetting knowledge of any object without direct physical contact with it
We propose one new approach for image segmentation using a hybrid algorithm of K-Means and Cellular Automata algorithms
It classifies the river water more efficiently than other two approaches. These segmentation analyses show that our KCA approach can detect the overlapping land use land cover regions in more efficient way than well-known K-Means and FCM methods
Summary
Cogalton and Green in 1999 introduced Remote sensing to be a method forgetting knowledge of any object without direct physical contact with it. We can define a set for the remote sensing data set, as shown in. Pijk}︀ denotes the values of n spectral bands for i, j th pixel. To find similar regions among overlapping segments, we partition the chosen watershed remote sensing image by our new hybrid unsupervised algorithm as well as another spatial image data set. Let P (Rn or Zn) defines the image space of selected remote sensing imagery. Points/pixels in P denotes spatial variables -x, y. We define a new method using the cellular automata approach over K-Means algorithm for image segmentation, which is the scope of this article
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