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An efficient framework for monitoring tree cover in an area through aerial images
Author(s): Suchit Jain; Rohan Mittal; Arpan Jain; Aakash Sinha
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Paper Abstract

Monitoring tree cover in an area plays an important role in a wide range of applications and advances in UAV technology has made it feasible to capture high resolution imagery which can be used for this purpose. In this study, we adopt a state of the art object detector Mask Region-based CNN (Mask R-CNN1), through transfer learning, for the task of tree segmentation and counting. One bottleneck for the proposed task is the huge amount of data required if the model is required to be scalable to various different geographical regions. Towards this end, we explore the use of a sampling technique based on Gist descriptors and Gabor filtering in order to minimize the amount of training data required for obtaining excellent model performance across images with varied geographical features. This study was conducted across four regions in India, each having a different geographical landscape. We captured a total of 2357 images across all four regions. The final training dataset comprised of 48 images (sampled using the aforementioned method), representative of the entire dataset. Our method demonstrates high quality and scalable tree detection results.

Paper Details

Date Published: 6 September 2019
PDF: 7 pages
Proc. SPIE 11139, Applications of Machine Learning, 1113918 (6 September 2019); doi: 10.1117/12.2529442
Show Author Affiliations
Suchit Jain, Shiv Nadar Univ. (India)
Rohan Mittal, Shiv Nadar Univ. (India)
Arpan Jain, Shiv Nadar Univ. (India)
Aakash Sinha, Shiv Nadar Univ. (India)


Published in SPIE Proceedings Vol. 11139:
Applications of Machine Learning
Michael E. Zelinski; Tarek M. Taha; Jonathan Howe; Abdul A. S. Awwal; Khan M. Iftekharuddin, Editor(s)

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