An Integrated Object and Machine Learning Approach for Tree Canopy Extraction from UAV Datasets
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RESEARCH ARTICLE
An Integrated Object and Machine Learning Approach for Tree Canopy Extraction from UAV Datasets Abhishek Adhikari1
•
Minakshi Kumar1 • Shefali Agrawal2 • Raghavendra S1
Received: 2 October 2020 / Accepted: 22 October 2020 Ó Indian Society of Remote Sensing 2020
Abstract Unmanned aerial vehicles (UAV) have emerged as new platforms for acquiring ultra-high resolution images, which are challenging for extraction of features using conventional image processing approaches. Tree canopies are required to be constantly monitored for better planning and management. UAV is currently one way to survey canopies over a large area for precisely estimating their geometry. Conventional segmentation techniques are extensively used for image feature extraction. However, they lack in accuracy and require high computational processing when used for ultra-high resolution UAV datasets. These issues can be handled by superpixel segmentation algorithms which have good boundary adherence and are computationally efficient. Simple linear iterative clustering (SLIC) is a subset of superpixel segmentation technique which uses minimum tuning parameters making it most efficient. As the random forest is known for handling multiple attributes and robustness, it can be used for classifying and extracting features from segmented image generated using SLIC. The present study mainly focuses on the automation for extraction of tree canopies along with their object-based attributes from the UAV dataset. The data acquisition was carried out using Trimble UX5 fixed-wing UAV which was further orthorectified at a spatial resolution of 13 cm. The ortho-image was further segmented using SLIC algorithm. Canopy segments are then identified and classified using random forest, which is then merged into trees objects on the basis of their proximity. Accuracy assessment was then carried out for extracted tree canopies and was found that the aforesaid approach could achieve 93% similarity index. The current study highlights the potential of using SLIC segmentation and random forest classification method for tree canopy extraction from the ultra-high resolution ortho-image derived from UAV platforms. Keywords Tree canopy UAV Random forest SLIC Machine learning
Introduction Trees play a vital role in sustaining the environment and one’s state economy. They are major contributors in the climate amelioration, soil preservation, managing water cycle, nursing flora and fauna and survival of humans. These life-forms have always been center for studies with respect to their role in the ecology, culture and economy. To properly manage these resources, a proper evaluation of & Abhishek Adhikari [email protected] 1
Photogrammetry and Remote Sensing Department, IIRSISRO, Dehradun, India
2
Geospatial Technology and Outreach Programme Group, IIRS-ISRO, Dehradun, India
their qualitative and quantitative properties is required. There are two broadways to collect the data from the site: field survey and remote survey (Lawley, Lewis, Clarke, &
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