Fuzzy Multi-level Color Satellite Image Segmentation Using Nature-Inspired Optimizers: A Comparative Study
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RESEARCH ARTICLE
Fuzzy Multi-level Color Satellite Image Segmentation Using NatureInspired Optimizers: A Comparative Study Krishna Gopal Dhal1
•
Swarnajit Ray2 • Arunita Das3 • Jorge Ga´lvez4 • Sanjoy Das5
Received: 14 November 2018 / Accepted: 26 May 2019 Indian Society of Remote Sensing 2019
Abstract In the realm of image processing domain, segmentation is an indispensable method for various applications. One can segment an image according to shape, size, regularities, structure, color, etc. Multi-level thresholding for image segmentation is one of the most promising methods for segmentation in the recent era. However, multi-level thresholding is computationally expensive, tedious and also challenging because of finding the optimal threshold values. Thus, to address this issue, this study presents a stochastic fractal search (SFS) with fuzzy entropy-based multi-level thresholding model for the proper segmentation of color satellite images. To prove the superiority of SFS algorithm, a comparative study is performed with four well-known nature-inspired optimization algorithms, namely particle swarm optimization (PSO), cuckoo search (CS), harmony search (HS) and artificial bee colony (ABC) algorithms. The experiment has been conducted on various satellite images, and the result shows that SFS with fuzzy entropy-based model provides superior-quality segmented images over other methods in terms of fitness value, computational time and values of quality metrics. The experimental study also shows that computational time of SFS algorithms is 2.5% less than CS algorithms and 8%, 9%, 15% less than ABC, PSO, HS, respectively, on average when the same number function evaluations has been considered as stopping criterion. Keywords Satellite image segmentation Multi-level thresholding Fuzzy entropy Swarm intelligence Optimization
& Krishna Gopal Dhal [email protected] Swarnajit Ray [email protected] Arunita Das [email protected] Jorge Ga´lvez [email protected] Sanjoy Das [email protected] 1
Department of Computer Science and Application, Midnapore College (Autonomous), Paschim Medinipur, West Bengal, India
2
Skybound Digital LLC, Kolkata, West Bengal, India
3
Department of Information Technology, Kalyani Government Engineering College, Kalyani, Nadia, India
4
Departamento de Electro´nica, Universidad de Guadalajara, CUCEI Av. Revolucio´n 1500, 44430 Guadalajara, Mexico
5
Department of Engineering and Technological Studies, University of Kalyani, Kalyani, Nadia, India
Introduction Image segmentation plays a significant role in the image processing domain. Image segmentation is a process of partitioning the images into multiple segments according to unique feature and with meaningful information. Basically, the segmentation method subdivided the image into multiple regions based on size, shape, color, texture or position of regions. Lots of work has already been done in image segmentation domain, but research work on satellite images is not very rich. In p
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