17 October 2017 Stable and accurate methods for identification of water bodies from Landsat series imagery using meta-heuristic algorithms
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J. of Applied Remote Sensing, 11(4), 045005 (2017). doi:10.1117/1.JRS.11.045005
Abstract
Identification of water extents in Landsat images is challenging due to surfaces with similar reflectance to water extents. The objective of this study is to provide stable and accurate methods for identifying water extents in Landsat images based on meta-heuristic algorithms. Then, seven Landsat images were selected from various environmental regions in Iran. Training of the algorithms was performed using 40 water pixels and 40 nonwater pixels in operational land imager images of Chitgar Lake (one of the study regions). Moreover, high-resolution images from Google Earth were digitized to evaluate the results. Two approaches were considered: index-based and artificial intelligence (AI) algorithms. In the first approach, nine common water spectral indices were investigated. AI algorithms were utilized to acquire coefficients of optimal band combinations to extract water extents. Among the AI algorithms, the artificial neural network algorithm and also the ant colony optimization, genetic algorithm, and particle swarm optimization (PSO) meta-heuristic algorithms were implemented. Index-based methods represented different performances in various regions. Among AI methods, PSO had the best performance with average overall accuracy and kappa coefficient of 93% and 98%, respectively. The results indicated the applicability of acquired band combinations to extract accurately and stably water extents in Landsat imagery.
© 2017 Society of Photo-Optical Instrumentation Engineers (SPIE)
Mohammad Hossein Gamshadzaei, Majid Rahimzadegan, "Stable and accurate methods for identification of water bodies from Landsat series imagery using meta-heuristic algorithms," Journal of Applied Remote Sensing 11(4), 045005 (17 October 2017). http://dx.doi.org/10.1117/1.JRS.11.045005 Submission: Received 22 May 2017; Accepted 26 September 2017
Submission: Received 22 May 2017; Accepted 26 September 2017
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KEYWORDS
Earth observing sensors

Evolutionary algorithms

Landsat

Particle swarm optimization

Particles

Satellite imaging

Satellites

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