Share Email Print
cover

Proceedings Paper • new

Fast and robust detection of oil palm trees using high-resolution remote sensing images
Format Member Price Non-Member Price
PDF $14.40 $18.00
cover GOOD NEWS! Your organization subscribes to the SPIE Digital Library. You may be able to download this paper for free. Check Access

Paper Abstract

Oil palm tree detection is of great significance for improving the irrigation, estimating the yield of palm oil, and predicting the expansion trend, etc. Existing tree detection methods include traditional image processing, machine learning methods, and sliding window based deep learning methods. In this paper, we proposed a deep learning based end-to-end method for oil palm detection in large scale. First, we built an oil palm sample dataset from 0.1m-resolution Unmanned Aerial Vehicle (UAV) images. Second, we implemented five state-of-the-art object detection algorithms (i.e. Faster- RCNN, VGG-SSD, YOLO-v3, RetinaNet and Mobilenet-SSD) and evaluated their performances for detecting the tree crown size and the location of oil palms. Moreover, we designed an overlapping partition method to improve the oil palm detection results of the UAV images in over 40,000 × 40,000 pixels. Experiment results demonstrate that in terms of the detection accuracy, VGG-SSD achieves the best accuracy of 90.91% on the validation dataset, followed by YOLO-v3, RetinaNet, Mobilenet-SSD and Faster RCNN. Meanwhile, we compared the detection time of the five object detection algorithms. Mobilenet-SSD achieves the highest detection speed among five algorithms (12.81ms per image in 500×500 pixels), with the speedup ratios of 17.5×, 10.2×, 4.51×, and 17.33× compared with Faster-RCNN, VGG-SSD, YOLO-v3 and RetinaNet. The results show that our proposed oil palm detection method is of great practical value to the precision agriculture of the oil palm industry.

Paper Details

Date Published: 14 May 2019
PDF: 9 pages
Proc. SPIE 10988, Automatic Target Recognition XXIX, 109880C (14 May 2019); doi: 10.1117/12.2518352
Show Author Affiliations
Maocai Xia, Tsinghua Univ. (China)
Joint Ctr. for Global Change Studies (China)
Weijia Li, Tsinghua Univ. (China)
Joint Ctr. for Global Change Studies (China)
Haohuan Fu, Tsinghua Univ. (China)
Joint Ctr. for Global Change Studies (China)
Le Yu, Tsinghua Univ. (China)
Joint Ctr. for Global Change Studies (China)
Runmin Dong, Tsinghua Univ. (China)
Joint Ctr. for Global Change Studies (China)
Juepeng Zheng, Tsinghua Univ. (China)
Joint Ctr. for Global Change Studies (China)
Tongji Univ. (China)


Published in SPIE Proceedings Vol. 10988:
Automatic Target Recognition XXIX
Riad I. Hammoud; Timothy L. Overman, Editor(s)

© SPIE. Terms of Use
Back to Top