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Remote Sens. 2017, 9(1), 22; doi:10.3390/rs9010022

Deep Learning Based Oil Palm Tree Detection and Counting for High-Resolution Remote Sensing Images

1,2
,
1,2,3,* , 1,2
and
4
1
Ministry of Education Key Laboratory for Earth System Modeling, Department of Earth System Science, Tsinghua University, Beijing 100084, China
2
Joint Center for Global Change Studies (JCGCS), Beijing 100084, China
3
National Supercomputing Center in Wuxi, Wuxi 214072, China
4
Division of Electronic Engineering and Physics, University of Dundee, Dundee DDI 4HN, UK
*
Author to whom correspondence should be addressed.
Academic Editors: Janet Nichol and Prasad S. Thenkabail
Received: 5 November 2016 / Revised: 19 December 2016 / Accepted: 28 December 2016 / Published: 30 December 2016
View Full-Text   |   Download PDF [12429 KB, uploaded 30 December 2016]   |  

Abstract

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. View Full-Text
Keywords: oil palm trees; deep learning; convolutional neural network (CNN); object detection oil palm trees; deep learning; convolutional neural network (CNN); object detection
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Li, W.; Fu, H.; Yu, L.; Cracknell, A. Deep Learning Based Oil Palm Tree Detection and Counting for High-Resolution Remote Sensing Images. Remote Sens. 2017, 9, 22.

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