Abstract
With unmanned aerial vehicle (UAV) becoming more accessible, remote sensing using UAVs have garnered a lot of attention. UAVs have applications in traffic management, weather monitoring, precision agriculture, orchard management, etc. Now, it is possible to detect and monitor trees from their canopy with the availability of high spatial resolution images acquired from cameras mounted on UAV. Tree canopy detection and counting has been important in orchard management, forest surveys and inventory, monitoring tree health, tree counting, and so on. Previous studies have focused on usage of deep neural networks for detecting tree canopy and in a few cases, they have delineated the tree canopy masks. However, creating training samples of masks by annotation is an extremely challenging task for two important reasons. Firstly, due to the sheer volume of data required for deep neural networks and the effort required for creating labelled masks through bounding boxes can be manifold. Secondly, resolution of the UAV images and irregular shapes of the tree canopies make it a difficult process to hand draw the masks around the canopies. In this work, a two stage semi-supervised approach for detecting the tree canopy is proposed. The first stage comprises of detecting tree canopy through bounding boxes using RetinaNet, and the second stage finds the tree canopy masks using a combination of thresholded ExGI (excess green index) values, neural networks with back propagation and SLIC (simple linear iterative clustering). The results showed a mean average precision of 90% for tree canopy detection and 65% accuracy for the tree canopy extraction.
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Acknowledgements
We are grateful to the International Institute of Information Technology Bangalore (IIITB), India for the infrastructure support. We are thankful to Infosys Foundation for the financial assistance and project grant through the Infosys Foundation Career Development Chair Professor.
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Kumar, U., Dasgupta, A., Venkata Vamsi Krishna, L.S.N., Chintakunta, P.K. (2022). Towards Semi-supervised Tree Canopy Detection and Extraction from UAV Images. In: Raman, B., Murala, S., Chowdhury, A., Dhall, A., Goyal, P. (eds) Computer Vision and Image Processing. CVIP 2021. Communications in Computer and Information Science, vol 1568. Springer, Cham. https://doi.org/10.1007/978-3-031-11349-9_26
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