NDLI: Efficient Framework for Palm Tree Detection in UAV Images

Content Provider	IEEE Xplore Digital Library
Author	Malek, S. Bazi, Y. Alajlan, N. AlHichri, H. Melgani, F.
Copyright Year	2008
Abstract	The latest developments in unmanned aerial vehicles (UAVs) and associated sensing systems make these platforms increasingly attractive to the remote sensing community. The large amount of spatial details contained in these images opens the door for advanced monitoring applications. In this paper, we use this cost-effective and attractive technology for the automatic detection of palm trees. Given a UAV image acquired over a palm farm, first we extract a set of keypoints using the Scale-invariant Feature Transform (SIFT). Then, we analyze these keypoints with an extreme learning machine (ELM) classifier a priori trained on a set of palm and no-palm keypoints. As output, the ELM classifier will mark each detected palm tree by several keypoints. Then, in order to capture the shape of each tree, we propose to merge these keypoints with an active contour method based on level sets (LSs). Finally, we further analyze the texture of the regions obtained by LS with local binary patterns (LBPs) to distinguish palm trees from other vegetations. Experimental results obtained on UAV images with 3.5 cm of spatial resolution and acquired over two different farms confirm the promising capabilities of the proposed framework.
Starting Page	4692
Ending Page	4703
Page Count	12
File Size	2347275
File Format	PDF
ISSN	19391404
Volume Number	7
Issue Number	12
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-01-01
Publisher Place	U.S.A.
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Vegetation Unmanned aerial vehicles Spatial resolution Feature extraction Remote sensing Histograms unmanned aerial vehicle (UAV) images Extreme learning machine (ELM) level set (LS) local binary pattern (LBP) palm trees scale-invariant feature transform (SIFT)
Content Type	Text
Resource Type	Article
Subject	Atmospheric Science Computers in Earth Sciences

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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