Wavelet Transform-Based 3D Landscape Design and Optimization for Digital Cities. (7th September 2022)

• Record Type: Journal Article
• Authors: Chen, Yang; Wang, Xiaolin; Zhang, Chang
• Other Names: Zhang Yuwei Academic Editor.
• Is Part Of: International journal of antennas and propagation. Volume 2022(2022)
• Publication Date: 2022-09-07
• Subjects: Electronic apparatus and appliances -- Periodicals; Antennas (Electronics) -- Periodicals; Radio wave propagation -- Periodicals; Antennes (Électronique); Ondes radioélectriques -- Propagation; Antennas (Electronics); Electronic apparatus and appliances; Radio wave propagation; Electronic journals; Periodicals; 621.382405
• DOI: 10.1155/2022/1184198
• Languages: English
• ISSNs: 1687-5869

Abstract

Abstract : As a hot concept, the digital city has developed rapidly in recent years. The digital city uses information technology to realize all the contents of the past, present, and future of the city on the network, and can build a three-dimensional visual landscape. However, the traditional information fusion model has the problems of noise susceptibility and low efficiency in landscape design. In order to solve the above problems, this article proposes a wavelet transform-based 3D landscape design and optimization method for digital cities, which removes the noise influenced by wavelet change and builds an information fusion model based on neural network to complete the design optimization of the three-dimensional landscape of the digital city. Firstly, for the wavelet change denoising problem, an effective denoising algorithm for natural noise and abnormal noise is proposed by combining convolutional neural network and wavelet transform. The algorithm extracts mixed feature information of local long path and local short path based on the information retention module, and decomposes the information by combining wavelet transform, inputs the different components obtained from the decomposition into the network for training, and removes the noise by subsequent feature screening of the network structure. Then, aiming at the optimization of 3D landscape design, an information fusion model based on long-short time memory network and radial basis backpropagation network is proposed for fusing multiple sources of information in the digital city to evaluate the landscape. The method collects digital city feature information at the information layer and preprocesses the feature information by a rate detection algorithm. Then, LSTM and RBF-BP neural networks in deep learning are used in the feature layer for adaptive learning of multiple feature signals, and finally, fuzzy logic is used to control the system decision output to improve the efficiency of 3D landscape design. Finally, the simulation experimental results show that the proposed denoising method in this article can better retain the texture details in the images and the denoised images have better visual effects; the proposed information fusion model has higher accuracy compared with the traditional methods. Combining this method to design and optimize 3D landscapes in digital cities can improve the efficiency of landscape design.