Deep Learning Based Oil Palm Tree Detection and Counting for High-Resolution Remote Sensing Images
By Weijia Li, Haohuan Fu, Le Yu, Arthur Philip Cracknell. Remote Sensing, 2016
399 citations recorded by the source corpus.
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.
Subfields: Ecology, Environmental Engineering, Plant Science
View Paper: https://www.semanticscholar.org/paper/f9988dda860bde31ca0309c08355638c759e7281
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Citations in the corpus
- A Review of Deep Learning Methods and Applications for Unmanned Aerial Vehicles (2017, methodology): “Several applications have used deep learning techniques for this purpose [12, 49, 50, 55, 56], providing robust systems for monitoring the state of the crops in order to maximize their productivity.”
- Small-Object Detection in Remote Sensing Images with End-to-End Edge-Enhanced GAN and Object Detector Network (2020, background): “There is another work [50] related to plant detection, where authors detected palm trees from satellite imagery using sliding window techniques and an optimized convolutional neural network.”
- Land cover and land use classification performance of machine learning algorithms in a boreal landscape using Sentinel-2 data (2019, methodology): “2017; Zhong, Lina, and Zhou 2019), estimating crop yields (Kuwata and Shibasaki 2015), detecting oil palm trees (Li et al. 2017) and plant diseases (Mohanty, Hughes, and”