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Texture Features Extraction and Backtracking Search Optimization Algorithm for Satellite Image Clustering


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DOI: https://doi.org/10.15866/irease.v12i5.15224

Abstract


Clustering is one of the data analysis methods used in image processing. It consists in dividing all the pixels of an image into a corresponding class pixel by pixel. In this paper, the Backtracking Search Algorithm (BSA) has been investigated in order to cluster this heterogeneous set of information using a combination of different textural features. The validity index is used to determine the appropriate number of classes representing an image and to evaluate the results of the classification algorithm. The study realized with Calinski Harabasz (CHI) and Davies-Bouldin Index (DBI) allows finding the best validity index to evaluate fitness function. The experiences obtained show the effectiveness and performance of the Davies-Bouldin Index (DBI) with BSA algorithm.
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Keywords


Backtracking Search Optimization; Textural Feature; Unsupervised Clustering; Validity Index

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References


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