Multi-Spectral Image Segmentation Based on the K-means Clustering
Mohamed A.Hamada1, Yeleussiz Kanat2, Adejor Egahi Abiche3

1Mohamed A.Hamada*, Associate Professor of Information System, IITU Almaty, Kazakhstan.
2Yeleussiz Kanat, IS Department, IIT University, Almaty Kazakhstan 3Adejor Egahi Abiche, Senior Lecturer in Computer & Information Security, IITU Almaty, Kazakhstan

Manuscript received on November 15, 2019. | Revised Manuscript received on 20 November, 2019. | Manuscript published on December 10, 2019. | PP: 1016-1019 | Volume-9 Issue-2, December 2019. | Retrieval Number: K15960981119/2019©BEIESP | DOI: 10.35940/ijitee.K1596.129219
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Abstract: Agriculture is one of the oldest economic aspects of human civilisation, and it is still undergoing a dynamic makeover in the course of the application of IT innovative mechanisms in farming methodology. Remote sensing has vied a significant role in crop classification, crop health and yield assessment. Multispectral remote sensing plays a vital role in providing enhancement of more detailed analysis of crop segmentation. In this article, pixel-based clustering of 12 channels is carried out using the satellite image from Sentinel 2 remote sensing satellite via k-means clustering. K-means clustering algorithm is usually a better method of classifying high-resolution satellite imagery. The extracted regions are classified using a minimum distance decision rule. 
Keywords:  k-means a Clustering, Agriculture, Remote Sensing, Sentinel-2.
Scope of the Article: Clustering