Variational Auto-Encoder Reconstruction Networks for Classification of Hyperspectral and LiDAR Data

• Published in: Journal of physics. Conference series Vol. 2562; no. 1; pp. 12051 - 12058
• Main Author: Zhai, Ziming
• Format: Journal Article
• Language: English
• Published: Bristol IOP Publishing 01.08.2023
• Subjects: Artificial neural networks; Classification; Coders; Computer architecture; Data integration; Encoding-Decoding; Feature extraction; Hyperspectral imaging; Lidar; Machine learning; Multilayer perceptrons; Multilayers; Physics; Reconstruction; Remote observing; Remote sensing
• ISSN: 1742-6588; 1742-6596
• DOI: 10.1088/1742-6596/2562/1/012051

The classification of remote sensing scene objects has been the subject of extensive studies in recent years due to the quick advancement of earth observation and remote sensing technology. More concentration, Hyperspectral images, and LiDAR data are complementary, and their combined use for data fusion can better mine the multi-dimensional features of ground objects in remote sensing scenes, which can effectively improve the classification accuracy and reliability of ground objects in remote sensing scenes. Single-modal remote sensing data frequently cannot fully meet the needs of ground feature classification due to the increasingly complex types of ground features. In order to solve this problem, we have developed two distinct multi-source fusion classification approaches using LiDAR and hyperspectral data and deep learning techniques. They are the reconstructed multi-layer perceptron network based on the variational auto-Encoder (encoding-decoding form) and the two-stream input convolutional neural network based on the cross-channel reconstruction mechanism. These two approaches can help us find more effective and deeper feature extraction methods and feature fusion methods in this research direction and design training. We adopted the network architecture model used in this area of research and used experimental data to demonstrate the effectiveness and superiority of the suggested network model.