Hyperspectral and LiDAR data fusion using collaborative representation

Qian Du; John E. Ball; Chiru Ge

doi:10.1117/12.2558967

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20 May 2020 Hyperspectral and LiDAR data fusion using collaborative representation

Qian Du, John E. Ball, Chiru Ge

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Proceedings Volume 11392, Algorithms, Technologies, and Applications for Multispectral and Hyperspectral Imagery XXVI; 1139208 (2020) https://doi.org/10.1117/12.2558967
Event: SPIE Defense + Commercial Sensing, 2020, Online Only

Abstract

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.

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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); https://doi.org/10.1117/12.2558967

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