An Object Based Image Analysis of Multispectral Satellite and Drone Images for Precision Agriculture Monitoring

Accurate information on spatial distribution of crop and vegetation indices for crop health monitoring is important for precision agriculture monitoring. However, freely available multispectral satellite images and unmanned aerial vehicle based multispectral images provides great opportunities for crop area estimation and extraction of vegetation indices. An object-based image analysis is better than pixel-based analysis because it is used statistical, geometrical, and topographic feature of the objects. Therefore, this paper presents an object-based image analysis of multispectral satellite and drone images for crop area estimation and extraction of vegetation indices for precision agriculture monitoring. Object segmentation, feature extraction, and classification of multispectral satellite and drone images was done. The experimental results show that the high-resolution drone imagery provides better crop area estimation and vegetation indices compared to freely available coarse resolution satellite imagery due to mixed pixels especially boundary of the crop classes.