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Large-Scale Oil Palm Tree Detection from High-Resolution Remote Sensing Images Using Faster-RCNN

Oil palm is of great importance in agricultural productivity for many tropic developing countries and accordingly investigating as well as counting oil palms is a meaningful and valuable research. In this paper, we firstly apply Faster-RCNN, one of the most popular object detection algorithms, to detect tree crowns from satellite images. Although Faster-RCNN has an excellent performance in well-known datasets of general object detection, it does not have obvious advantages in oil palm tree detection in this study compared with other classical machine learning based methods. We argue two reasons accounting for the drawbacks of Faster-RCNN: (1) the size of each oil palm tree is too small (only 17 × 17 pixels on average) in 0.6m-resolution QuickBird satellite images; (2) there are lots of other similar trees around the oil palm trees that make it difficult to detect them correctly. In order to reach a satisfying accuracy, we tailored the Region Proposal Network (RPN) and proposed a simple but practical post-processing strategy based on empirical planting rules, filtering out the wrongly detected trees (False Positives) effectively. Eventually we achieved a higher average F1-score of 94.99% (using IOU based evaluation matrices) in our six study regions compared wtih state-of-the-art oil palm detection methods. In addition, we proposed a workflow of large-scale oil palm tree detection using high-resolution remotely sensed images based deep learning methods.