Abstract
The difference between the convolutional neural network and the ordinary neural network is that the convolutional neural network contains a feature extractor composed of a convolutional layer and a subsampling layer. With the development of society and economy, the pace of urbanization is accelerating, and the number and types of urban buildings are also growing rapidly. Digital management has put forward higher requirements for 3D reconstruction of urban buildings. Aiming at explanation the question that sharpness form are proetrate to be blea or bewildered in CNN-supported structure birth from lofty-separation airy conception, an optimise construction birth algorithmic rule is converse to increase the construction brink of proud-resoluteness atmospheric semblance and the twist projection. Remote sensing image target recognition, as the main research content in the current remote sensing image application field, has important theoretical significance and extensive application value. In recent years, deep learning has become an emerging research direction in the field of machine learning, and convolutional neural network is a deep learning model that has been widely studied and applied. More specifically, the construction brink is better by realm vary recursive filter out, and the better appearance is fed into the U-Net nerval netting for making. Afterward, in custom to plentifully take advantage of the sumptuous detail shape of buildings on supercilious sake picture, we tempt to en plot impair from the manage copy and pigeonhole supported on the origin U-Net edifice to increase the school data. These beauty spot can remarkably fortify the procurement of edifice hie viterbilt characteristic in eager and invert intense lore. Finally, construction essence is instrument by mechanical advantage the quotation intense characteristic. The trial effect of edifice extract from the Panjin City have demonstrated that for the hoagie-optimum pattern data with division of shade areas, the everywhere assortment propriety of buildings recognized by U-Net is above 80%, and the zenith everywhere assortment truth of the amended regularity extension 83%. In this paper, through the research on the application of convolutional neural network in the field of image segmentation, the problems of low segmentation accuracy, long time and high cost in the task of aerial image building image segmentation are solved to a certain extent. The detection and segmentation method of buildings in aerial images based on CNN can automatically detect and segment buildings, and can segment a large number of buildings in aerial images in batches. In scenarios with high segmentation efficiency requirements.
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Deng, Y., Xie, X. & Xing, C. CNN-based architecture recognition and contour standardization based on aerial images. Neural Comput & Applic 35, 2119–2127 (2023). https://doi.org/10.1007/s00521-022-07288-0
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DOI: https://doi.org/10.1007/s00521-022-07288-0