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2024 | OriginalPaper | Buchkapitel

Convolutional Neural Network Based on Multiple Attention Mechanisms for Hyperspectral and LiDAR Classification

verfasst von : Yingying Wang, Kun Wang, Zhiming Ding

Erschienen in: Spatial Data and Intelligence

Verlag: Springer Nature Singapore

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Abstract

With the emergence of a large number of remote sensing data sources, how to effectively use the useful information in multi-source data for better earth observation has become an interesting but challenging problem. In this paper, the deep learning method is used to study the joint classification of hyperspectral imagery (HSI) and light detection and ranging (LiDAR) data. The network proposed in this paper is named convolutional neural network based on multiple attention mechanisms (MatNet). Specifically, a convolutional neural network (CNN) with an attention mechanism is used to extract the deep features of HSI and LiDAR respectively. Then the obtained features are introduced into the dual-branch cross-attention fusion module (DCFM) to fuse the information in HSI and LiDAR data effectively. Finally, the obtained features are introduced into the classification module to obtain the final classification results. Experimental results show that our proposed network can achieve better classification performance than existing methods.

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Titel: Convolutional Neural Network Based on Multiple Attention Mechanisms for Hyperspectral and LiDAR Classification
verfasst von: Yingying Wang
Kun Wang
Zhiming Ding
Verlag: Springer Nature Singapore
Buch: Spatial Data and Intelligence
Print ISBN: 978-981-9729-65-4

Electronic ISBN: 978-981-9729-66-1

Copyright-Jahr: 2024
DOI: https://doi.org/10.1007/978-981-97-2966-1_20

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