Comparative classification analysis of post-harvest growth detection from terrestrial LiDAR point clouds in precision agriculture

Autor(en): Koenig, Kristina
Hoefle, Bernhard
Haemmerle, Martin
Jarmer, Thomas 
Siegmann, Bastian
Lilienthal, Holger
Stichwörter: Classification; CROP; DENSITY; Geography, Physical; Geology; Geometric feature; Geosciences, Multidisciplinary; IMAGE; Imaging Science & Photographic Technology; LASER-SCANNING DATA; MEASUREMENT SYSTEM; MODEL; Physical Geography; Precision agriculture; Radiometric correction; Radiometric feature; Remote Sensing; SOIL; Terrestrial laser scanning; VEGETATION; WATER-CONTENT; WEED-CONTROL
Erscheinungsdatum: 2015
Herausgeber: ELSEVIER
Journal: ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING
Volumen: 104
Startseite: 112
Seitenende: 125
Zusammenfassung: 
In precision agriculture, detailed geoinformation on plant and soil properties plays an important role, e.g., in crop protection or the application of fertilizers. This paper presents a comparative classification analysis for post-harvest growth detection using geometric and radiometric point cloud features of terrestrial laser scanning (TLS) data, considering the local neighborhood of each point. Radiometric correction of the TLS data was performed via an empirical range-correction function derived from a field experiment. Thereafter, the corrected amplitude and local elevation features were explored regarding their importance for classification. For the comparison, tree induction, Naive Bayes, and k-Means-derived classifiers were tested for different point densities to distinguish between ground and post-harvest growth. The classification performance was validated against highly detailed RGB reference images and the red edge normalized difference vegetation index (NDVI705), derived from a hyperspectral sensor. Using both geometric and radiometric features, we achieved a precision of 99% with the tree induction. Compared to the reference image classification, the calculated post-harvest growth coverage map reached an accuracy of 80%. RGB and LiDAR-derived coverage showed a polynomial correlation to NDVI705 of degree two with R-2 of 0.8 and 0.7, respectively. Larger post-harvest growth patches (>10 x 10 cm) could already be detected by a point density of 2 pts./0.01 m(2). The results indicate a high potential of radiometric and geometric LiDAR point cloud features for the identification of post-harvest growth using tree induction classification. The proposed technique can potentially be applied over larger areas using vehicle-mounted scanners. (c) 2015 International Society for Photogrammetry and Remote Sensing, Inc. (ISPRS). Published by Elsevier B.V. All rights reserved.
ISSN: 09242716
DOI: 10.1016/j.isprsjprs.2015.03.003

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