融合自注意力机制与生成对抗网络的DEM空洞填充

doi:10.3969/j.issn.0255-8297.2023.05.006

Abstract

Abstract: Aiming at the problems of existing DEM data void filling algorithms, such as discontinuous repair effect, narrow applicable null value range and loss of detail reconstruction, this paper proposes a DEM void filling method integrating self-attention mechanism with generative adjunctive network. Firstly, a self-attention mechanism is constructed to extract DEM data feature information to improve the elevation discontinuity and texture detail loss of DEM cavity filling results. Secondly, symmetric convolutional and deconvolution network structures are used in the generator to ensure the generation of high reliability data to realize the filling of the void region, and the discriminator is used to realize the pre-classification of the filling results. Finally, combined with the reconstruction of loss function and the generation of adversarial loss function, the network training was carried out to improve the robustness of DEM cavity filling results to outliers and enhance the regression ability of the model. The experimental results show that compared with the filling results of spatial interpolation and deep learning, the proposed method can greatly improve the filling accuracy and effectively solve the problems of holes in the original data.

Key words: digital elevation model (DEM), void filling, deep learning, self-attention mechanism, generative adversarial networks

CLC Number:

P237

ZHANG Chunsen, ZHU Jiangle, ZHANG Xuefen, LIU Xudong, SHI Shu. Void Filling of DEM in a Generative Adversarial Network Fused with Self-Attention Mechanism[J]. Journal of Applied Sciences, 2023, 41(5): 789-800.

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Void Filling of DEM in a Generative Adversarial Network Fused with Self-Attention Mechanism

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