Abstract
The Satellite image classification is a complex process. It gives the information about the land cover and thematic land use in remotely sensed data. Image classification is the one of the important application of remote sensing. It is the process of obtaining details about the objects without physical contact. The main objective of image classification is to classify various objects or classes in satellite images. This paper gives an automatic method for the hyper spectral image classification and it classifies the images into multiple land cover objects. The proposed method assigns each pixel in the image to a group of pixels based on reflectance. In this work the acquired input satellite image is preprocessed and then applied classification methods. The proposed classification method includes both spectral and spatial information. In this paper the proposed method uses K-Means clustering algorithm for segmentation and Artificial Bee Colony optimization algorithm is used for classification. The existing method is Particle Swarm Optimization. Both the methods are compared in terms of sensitivity, specificity, overall accuracy and kappa coefficient. The Proposed method has better statistical values compared to the proposed method. This work is simulated in MATLAB R2017b version with system configuration of i3 processor and 8 GB.
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Acknowledgements
The First author express sincere thanks to JNTUA, Anantapuramu, Andhra Pradesh, where he is a research scholar and AITS, Rajampet, A. P. for providing good research facilities.
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Venkata Dasu, M., Reddy, P.V.N., Chandra Mohan Reddy, S. (2020). Classification of Remote Sensing Images Based on K-Means Clustering and Artificial Bee Colony Optimization. In: Gunjan, V., Senatore, S., Kumar, A., Gao, XZ., Merugu, S. (eds) Advances in Cybernetics, Cognition, and Machine Learning for Communication Technologies. Lecture Notes in Electrical Engineering, vol 643. Springer, Singapore. https://doi.org/10.1007/978-981-15-3125-5_7
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