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Remote Sens. 2017, 9(7), 721; doi:10.3390/rs9070721

Papaya Tree Detection with UAV Images Using a GPU-Accelerated Scale-Space Filtering Method

1
,
1,* , 1
,
1,2,3
and
1,2,3
1
Guangdong Open Laboratory of Geospatial Information Technology and Application, Key Laboratory of Guangdong for Utilization of Remote Sensing and Geographical Information System, Engineering Technology Center of Remote Sensing Big Data Application of Guangdong Province, Guangzhou Institute of Geography, Guangzhou 510070, China
2
Guangzhou Institute of Geochemistry, Guangzhou 510640, China
3
University of Chinese Academy of Sciences, Beijing 100049, China
*
Author to whom correspondence should be addressed.
Academic Editors: Jan Dempewolf, Jyoteshwar Nagol, Min Feng and Clement Atzberger
Received: 17 May 2017 / Revised: 29 June 2017 / Accepted: 8 July 2017 / Published: 13 July 2017
View Full-Text   |   Download PDF [6410 KB, uploaded 19 July 2017]   |  

Abstract

The use of unmanned aerial vehicles (UAV) can allow individual tree detection for forest inventories in a cost-effective way. The scale-space filtering (SSF) algorithm is commonly used and has the capability of detecting trees of different crown sizes. In this study, we made two improvements with regard to the existing method and implementations. First, we incorporated SSF with a Lab color transformation to reduce over-detection problems associated with the original luminance image. Second, we ported four of the most time-consuming processes to the graphics processing unit (GPU) to improve computational efficiency. The proposed method was implemented using PyCUDA, which enabled access to NVIDIA’s compute unified device architecture (CUDA) through high-level scripting of the Python language. Our experiments were conducted using two images captured by the DJI Phantom 3 Professional and a most recent NVIDIA GPU GTX1080. The resulting accuracy was high, with an F-measure larger than 0.94. The speedup achieved by our parallel implementation was 44.77 and 28.54 for the first and second test image, respectively. For each 4000 × 3000 image, the total runtime was less than 1 s, which was sufficient for real-time performance and interactive application. View Full-Text
Keywords: compute unified device architecture (CUDA); graphics processing units (GPU); PyCUDA; scale-space; tree detection; unmanned aerial vehicle (UAV) compute unified device architecture (CUDA); graphics processing units (GPU); PyCUDA; scale-space; tree detection; unmanned aerial vehicle (UAV)
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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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Jiang, H.; Chen, S.; Li, D.; Wang, C.; Yang, J. Papaya Tree Detection with UAV Images Using a GPU-Accelerated Scale-Space Filtering Method. Remote Sens. 2017, 9, 721.

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