Journal
IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING
Volume 12, Issue 4, Pages 1314-1326Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/JSTARS.2019.2899033
Keywords
Feature extraction; feature selection; land cover classification; learning algorithms; multispectral LiDAR data
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Funding
- National Natural Science Foundation of China [41671454, 61603146, 41801386]
- Natural Science Foundation of Jiangsu Province [BK20160427, BK20180797]
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Multispectral LiDAR, characterization of completeness, and consistency of spectrum and spatial geometric data provide a new data source for land cover classification. However, how to choose the optimal features for a given set of land covers is an open problem for effective land cover classification. To address this problem, we propose a comparative scheme, which investigates a popular deep learning (deep Boltzmann machine, DBM) model for high-level feature representation and widely used machine learning methods for low-level feature extraction and selection [principal component analysis (PCA) and random forest (RF)] in land cover classification. The comparative study was conducted on the multispectral LiDAR point clouds, acquired by a Teledyne Optech's Titan airborne system. The deep learning-based highlevel feature representation experimental results showed that, on an ordinary personal computer or workstation, this method required larger training samples andmore computational complexity than the machine learning-based low-level feature extraction and selection methods. However, our comparative experiments demonstrated that the classification accuracies of the DBM-based method were higher than those of the RF-based and PCA-based methods using multispectral LiDAR data.
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