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• W2734795280 abstract "In this paper, an approach is proposed to fuse LiDAR and hyperspectral data, which considers both spectral and spatial information in a single framework. Here, an extended self-dual attribute profile (ESDAP) is investigated to extract spatial information from a hyperspectral data set. To extract spectral information, a few well-known classifiers have been used such as support vector machines (SVMs), random forests (RFs), and artificial neural networks (ANNs). The proposed method accurately classify the relatively volumetric data set in a few CPU processing time in a real ill-posed situation where there is no balance between the number of training samples and the number of features. The classification part of the proposed approach is fully-automatic." @default.
• W2734795280 created "2017-07-21" @default.
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• W2734795280 date "2017-07-09" @default.
• W2734795280 modified "2023-09-25" @default.
• W2734795280 title "Integration of LiDAR and Hyperspectral Data for Land-cover Classification: A Case Study" @default.
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• W2734795280 hasPublicationYear "2017" @default.
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