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Proceedings Paper

Crop detection and density estimation combing LiDAR points cloud with remote sensing image
Author(s): Yun Yang; Ying Lin
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Paper Abstract

To further improve the accuracy of crop detection and acquire more information for land use investigation and agriculture management, this paper proposes a variational level set model for crop detection by combining airborne LiDAR(Light Detection and Range) points cloud and aerial image simultaneously acquired by LiDAR device. Specifically, normalized digital surface model (nDSM) derived from raw LiDAR points cloud are combined with aerial image so as to alleviate the misclassification caused by insufficient information only based on remote sensing image data. This fusion combines spectral and height information of objects from both sensors. By classifying the combined image using our proposed level set model, crop can be discriminated. Then, the paper suggests a novel method based on classification to predict crop density in a given scene. Experiments have verified that the proposed scheme really improve the accuracy of crop detection and the effectiveness of the proposed scheme of crop density estimation.

Paper Details

Date Published: 30 October 2009
PDF: 7 pages
Proc. SPIE 7494, MIPPR 2009: Multispectral Image Acquisition and Processing, 74941Y (30 October 2009); doi: 10.1117/12.834012
Show Author Affiliations
Yun Yang, Chang'an Univ. (China)
Ying Lin, Harbin Engineering Univ. (China)


Published in SPIE Proceedings Vol. 7494:
MIPPR 2009: Multispectral Image Acquisition and Processing
Faxiong Zhang; Faxiong Zhang, Editor(s)

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