Abstract
This work explores the suitability of various deep convolutional neural network (CNN) architectures for semantic segmentation of agricultural crops such as cotton, maize etc. from multi-spectral UAV (unmanned aerial vehicle) data. Initially, the UAV data were preprocessed and training samples for each crop type were manually annotated from multiple UAV scenes. Different CNN architectures such as U-Net, SegNet and PSPNet (Pyramid Scene Parsing Network) were trained with various combinations of input spectral bands along with select band derived indices such as NDVI (Normalized Difference Vegetation Index) and EVI (Enhanced Vegetation Index) as additional features. The experimental results indicated that inclusion of NIR (near-infrared) band and NDVI in the input data yielded high segmentation accuracy of more than 90%. U-Net proved to be the best among the three architectures with 97% overall accuracy while dealing with three classes separation problem. This study demonstrated the scope of deep neural network based semantic segmentation techniques in crop classification from multi-spectral UAV data.
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Acknowledgement
We thank CSIR-4PI (Fourth Paradigm Institute), Bangalore and University of Agricultural Sciences (UAS), Dharwad for providing the UAV data. We are grateful to International Institute of Information Technology Bangalore (IIIT-B), India for the infrastructure support and acknowledge Infosys Foundation for the financial assistance through the Infosys Foundation Career Development Chair Professor.
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Sudarshan Rao, B., Hota, M., Kumar, U. (2021). Crop Classification from UAV-Based Multi-spectral Images Using Deep Learning. In: Singh, S.K., Roy, P., Raman, B., Nagabhushan, P. (eds) Computer Vision and Image Processing. CVIP 2020. Communications in Computer and Information Science, vol 1376. Springer, Singapore. https://doi.org/10.1007/978-981-16-1086-8_42
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DOI: https://doi.org/10.1007/978-981-16-1086-8_42
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