Hyperspectral and LiDAR Data Fusion Classification Using Superpixel Segmentation-Based Local Pixel Neighborhood Preserving Embedding

Autor:	Yunsong Li, Chiru Ge, Weiwei Sun, Jiangtao Peng, Qian Du, Keyan Wang
Jazyk:	angličtina
Rok vydání:	2019
Předmět:	hyperspectral light detection and ranging (LiDAR) image fusion extinction profile (EP) superpixel segmentation local pixel neighborhood preserving embedding (LPNPE) Goddard’s LiDAR hyperspectral and thermal (G-LiHT) data Science
Zdroj:	Remote Sensing, Vol 11, Iss 5, p 550 (2019)
Druh dokumentu:	article
ISSN:	2072-4292
DOI:	10.3390/rs11050550
Popis:	A new method of superpixel segmentation-based local pixel neighborhood preserving embedding (SSLPNPE) is proposed for the fusion of hyperspectral and light detection and ranging (LiDAR) data based on the extinction profiles (EPs), superpixel segmentation and local pixel neighborhood preserving embedding (LPNPE). A new workflow is proposed to calibrate the Goddard’s LiDAR, hyperspectral and thermal (G-LiHT) data, which allows our method to be applied to actual data. Specifically, EP features are extracted from both sources. Then, the derived features of each source are fused by the SSLPNPE. Using the labeled samples, the final label assignment is produced by a classifier. For the open standard experimental data and the actual data, experimental results prove that the proposed method is fast and effective in hyperspectral and LiDAR data fusion.
Databáze:	Directory of Open Access Journals
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