Deep Residual Network-Based Fusion Framework for Hyperspectral and LiDAR Data

• Published in: IEEE journal of selected topics in applied earth observations and remote sensing Vol. 14; pp. 2458 - 2472
• Main Authors: Ge, Chiru, Du, Qian, Sun, Weiwei, Wang, Keyan, Li, Jiaojiao, Li, Yunsong
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2021; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Classifiers; Data integration; Data mining; Deep residual network; extinction profile; Feature extraction; Frameworks; Goddard 's LiDAR; hyperspectral; hyperspectral and thermal (G-LiHT) data; Hyperspectral imaging; image fusion; Laser radar; Lidar; light detection and ranging (LiDAR); local binary pattern (LBP); Matrices (mathematics); Methods; Multiplication; probability fusion; Probability theory; Residual neural networks; Stacking; Training

This article presents a deep residual network-based fusion framework for hyperspectral and LiDAR data. In this framework, three new fusion methods are proposed, which are the residual network-based deep feature fusion (RNDFF), the residual network-based probability reconstruction fusion (RNPRF) and...