Agriculture Crop Area mapping in images acquired using Low Altitude Remote Sensing
|Ramesh K.N1 , Meenavathi M.B2|
1 Department of Electronics and Instrumentation Engineering, Bangalore Institute of Technology, Bangalore, India.
2 Department of Electronics and Instrumentation Engineering, Bangalore Institute of Technology, Bangalore, India .
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Section:Research Paper, Product Type: Journal Paper
Volume-6 , Issue-1 , Page no. 55-62, Jan-2018
Online published on Jan 31, 2018
Copyright © Ramesh K.N, Meenavathi M.B . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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IEEE Style Citation: Ramesh K.N, Meenavathi M.B , “Agriculture Crop Area mapping in images acquired using Low Altitude Remote Sensing”, International Journal of Computer Sciences and Engineering, Vol.6, Issue.1, pp.55-62, 2018.
MLA Style Citation: Ramesh K.N, Meenavathi M.B "Agriculture Crop Area mapping in images acquired using Low Altitude Remote Sensing." International Journal of Computer Sciences and Engineering 6.1 (2018): 55-62.
APA Style Citation: Ramesh K.N, Meenavathi M.B , (2018). Agriculture Crop Area mapping in images acquired using Low Altitude Remote Sensing. International Journal of Computer Sciences and Engineering, 6(1), 55-62.
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|Ongoing research on Unmanned Aerial Vehicles (UAVs) is aimed at determin¬ing the utility of UAVs for agricultural remote sensing applications. Aerial pho¬tography from unmanned aerial vehicles bridges the gap between ground-based observations and remotely sensed imagery from aerial and satellite platforms. In the present study, Crop area measurements are carried out by analysis of aerial imagery acquired through Low Altitude Remote Sensing (LARS) carried out using a Quadcopter UAV. The area per pixel or the Ground Separation Distance (GSD) is computed using the altitude measurements from a barometer. Image processing clus¬tering techniques are applied to classify non crop and crop area in the image extent. Further the physical crop area and non crop area is determined using GSD. In this study K-Means and Mean shift clustering techniques are used to classify crop and non crop area. Performance of determining crop area is compared for K-means and Mean shift techniques. The results indicate crop area classification using Meanshift outperforms classification using K-means.|
|Key-Words / Index Term :|
|Unmanned Aerial Vehicle, Low Altitude Remote Sensing, Crop Area Mapping, Image clustering|
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