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A Terrain Elevation Map Generation Method Based on Self-Attention Mechanism and Multifeature Sketch
Author(s) -
Xingquan Cai,
Mengyao Xi,
Nu Yu,
Zhe Yang,
Haiyan Sun
Publication year - 2022
Publication title -
computational intelligence and neuroscience
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.605
H-Index - 52
eISSN - 1687-5273
pISSN - 1687-5265
DOI - 10.1155/2022/9481445
Subject(s) - terrain , computer science , discriminator , artificial intelligence , elevation (ballistics) , sketch , computer vision , feature (linguistics) , generator (circuit theory) , mechanism (biology) , cartography , algorithm , geography , telecommunications , linguistics , philosophy , power (physics) , geometry , mathematics , physics , epistemology , quantum mechanics , detector
To address the issues of low efficiency in manual terrain feature map annotating and poor realism in terrain elevation map generation, this paper proposes a terrain elevation map generation method based on self-attention mechanism and multifeature sketch. Firstly, the proposed method extracts features from a terrain elevation map using an adaptive feature enhancement method. Afterwards, our method adds a self-attention mechanism to the generator and discriminator of conditional generative adversarial network to capture the global spatial features and generates a realistic terrain elevation map. Finally, a level of detail method is used to visualize the three-dimensional terrain, and an interactive terrain editing tool for roaming interaction is implemented. Experimental data show that the proposed method performs well in subjective visual performance and objective criteria and has obvious advantages over other current typical methods.

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