Automatic palm trees detection from multispectral UAV data using template matching and circular Hough transform


Palm trees are considered to be 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 great efforts carried out in planting management and applying best practices in insuring the health status and 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 and consumed more time and required human interactions. Remote sensing including satellites and Unmanned Aerial Vehicles (UAVs) has contributed to providing potential solutions in this regard in terms of large areas coverage, spatial and spectral information such data contained. In this research, 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 template matching and Circular Hough Transform (CHT). Precision, recall and F1-score are calculated to assess the proposed method.