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20 May 2020 Hyperspectral and LiDAR data fusion using collaborative representation
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This paper investigates an efficient framework for the fusion of hyperspectral and LiDAR-derived digital surface model to improve classification performance, where collaborative representation based classifier is chosen due to its high computational efficiency with an analytical solution. Local binary pattern (LBP) and extinction profile (EP) features are extracted from both the sources, which include different spatial attributes. Then the derived spatial features are fed to a collaborative representation-based classifier with Tikhonov regularization (CRT) to produce representation residuals. Weighted residuals are calculated, and class label is assigned according to the minimal residual class to generate the classification map ultimately. To improve classification accuracy, the kernel CRT (KCRT) is used and residual fusion (RF) is conducted for the representation residuals from different sources and features. In this paper, spatial filtering for KCRT-RF is investigated. Experimental results demonstrate that a guided filter can help improve the fusion performance of KCRT-RF without significantly increasing computing cost.
Conference Presentation
© (2020) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Qian Du, John E. Ball, and Chiru Ge "Hyperspectral and LiDAR data fusion using collaborative representation", Proc. SPIE 11392, Algorithms, Technologies, and Applications for Multispectral and Hyperspectral Imagery XXVI, 1139208 (20 May 2020);

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