Hyperspectral and LiDAR Data Classification Using Kernel Collaborative Representation Based Residual Fusion

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, pp. 1-11, 2019.

Cited by: 0|Bibtex|Views1|DOI:https://doi.org/10.1109/jstars.2019.2913206
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Other Links: academic.microsoft.com|dblp.uni-trier.de

Abstract:

A new framework is proposed for the fusion of hyperspectral and light detection and ranging (LiDAR) data based on the extinction profiles (EPs), local binary pattern (LBP), and kernel collaborative representation classification. Specifically, EP and LBP features are extracted from both sources. Then, the derived features of each source ar...More

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