Journal
REMOTE SENSING
Volume 9, Issue 1, Pages -Publisher
MDPI
DOI: 10.3390/rs9010022
Keywords
oil palm trees; deep learning; convolutional neural network (CNN); object detection
Categories
Funding
- National Natural Science Foundation of China [61303003, 41374113]
- National High-Tech R&D (863) Program of China [2013AA01A208]
- Tsinghua University Initiative Scientific Research Program [20131089356]
- National Key Research and Development Plan of China [2016YFA0602200]
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Oil palm trees are important economic crops in Malaysia and other tropical areas. The number of oil palm trees in a plantation area is important information for predicting the yield of palm oil, monitoring the growing situation of palm trees and maximizing their productivity, etc. In this paper, we propose a deep learning based framework for oil palm tree detection and counting using high-resolution remote sensing images for Malaysia. Unlike previous palm tree detection studies, the trees in our study area are more crowded and their crowns often overlap. We use a number of manually interpreted samples to train and optimize the convolutional neural network (CNN), and predict labels for all the samples in an image dataset collected through the sliding window technique. Then, we merge the predicted palm coordinates corresponding to the same palm tree into one palm coordinate and obtain the final palm tree detection results. Based on our proposed method, more than 96% of the oil palm trees in our study area can be detected correctly when compared with the manually interpreted ground truth, and this is higher than the accuracies of the other three tree detection methods used in this study.
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