Young and mature oil palm tree detection and counting using convolutional neural network deep learning method-Reference-Cited by-同舟云学术

Young and mature oil palm tree detection and counting using convolutional neural network deep learning method

Published:2019-01-25 Issue:19 Volume:40 Page:7500-7515
ISSN:0143-1161
Container-title:International Journal of Remote Sensing
language:en
Short-container-title:International Journal of Remote Sensing

Author:

Mubin Nurulain Abd¹,Nadarajoo Eiswary¹,Shafri Helmi Zulhaidi Mohd¹²,Hamedianfar Alireza³

Affiliation:

1. Department of Civil Engineering, Faculty of Engineering, Universiti Putra Malaysia (UPM), Serdang, Malaysia

2. Geospatial Information Science Research Centre (GISRC), Faculty of Engineering, Universiti Putra Malaysia (UPM), Serdang, Malaysia

3. Department of Surveying Engineering, Estahban Branch, Islamic Azad University, Estahban, Iran

Funder

Nvidia

Universiti Putra Malaysia

Publisher

Informa UK Limited

Subject

General Earth and Planetary Sciences

Link

https://www.tandfonline.com/doi/pdf/10.1080/01431161.2019.1569282

Reference23 articles.

1. Britz, D. 2017. Understanding Convolutional Neural Networks for NLP. Accessed June 8 2017. http://www.wildml.com/2015/11/understanding-convolutional-neural-networks-fornlp/

2. Cheang, E. K., T. K. Cheang, and Y. H. Tay. 2017. “Using Convolutional Neural Network to Count Palm Trees in Satellite Images.” Accessed May 21 2018. https://arxiv.org/abs/1701.06462.https://arxiv.org/abs/1701.06462

3. Oil palm mapping using Landsat and PALSAR: a case study in Malaysia

4. A review of remote sensing applications for oil palm studies

5. Evaluation of MODIS gross primary productivity and land cover products for the humid tropics using oil palm trees in Peninsular Malaysia and Google Earth imagery

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