Normalized Cross Correlation Template Matching for Oil Palm Tree Counting from UAV image

Abstract

Tree crown detection and counting from remote sensing data such as images from Unmanned Aerial Vehicle (UAV) shows significant role in this modern era for vegetation monitoring. Since the data processing would depends on raw data available and for this case the RGB data, thus a suitable method such as template matching is presented. Normalized cross correlation is widely used as an effective measure in similarity between template image and the source or testing images. This paper focuses on six (6) steps involved in the overall process which are: (1) image acquisition, (2) template optimisation, (3) normalized cross correlation, (4) sliding window, (5) matched image and counting, and (6) accuracy assessment. Normalized cross correlation and sliding window techniques proposed for this work resulted in 80% to 89% F-measure values. This result indicates that UAV image data with appropriate image processing method/s have the potential to provide vital information for oil palm tree counting. This would be beneficial in plantation management to estimate yield and productivity. However, there are still rooms for improvement to achieve better results.