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9 October 2018 Automatic palm trees detection from multispectral UAV data using normalized difference vegetation index and circular Hough transform
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Abstract
Palm trees are considered a symbolic agricultural heritage in the United Arab Emirates (UAE). Date palms constitute 98% of fruit trees in the UAE, which is one of the world’s top ten producers of dates. This is due to the great efforts carried out in the planting management and applying the best practices in insuring the health status as well as maintaining the production rate which indeed requires frequent mapping and monitoring. The traditional way of mapping palm trees was implemented manually which has resulted in the lack of accuracy, more time consuming and intensive human interactions. Remote sensing including satellites and Unmanned Aerial Vehicles (UAVs) has contributed to providing potential solutions in terms of large areas coverage, spatial and spectral information. In this paper, we propose an automated approach to detect and count individual palm trees from UAV using a combination of spectral and spatial analyses. The proposed approach comprises two main steps; the first step discriminates the vegetation from the surrounding objects by applying the Normalized Difference Vegetation Index (NDVI). The second step detects individual palm trees using a combination of Circular Hough Transform (CHT) and the morphological operators. Precision, recall and F-measure are calculated to assess the performance of the proposed method. Experimental results reveal that more than 95% of the palm trees in the study areas are detected correctly when compared with the manually interpreted ground truth.
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© (2018) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Saeed Al Mansoori, Alavi Kunhu , and Hussain Al Ahmad "Automatic palm trees detection from multispectral UAV data using normalized difference vegetation index and circular Hough transform", Proc. SPIE 10792, High-Performance Computing in Geoscience and Remote Sensing VIII, 1079203 (9 October 2018); https://doi.org/10.1117/12.2325732
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