Remote sensing image building extraction method and system based on depth learning, storage medium and electronic device

Abstract

The invention provides a remote sensing image building extraction method and system based on depth learning which comprises the steps of sample preparation, model training, precision evaluation, building prediction, merging and vectorization. The invention also relates to a remote sensing image building extraction system based on depth learning, a storage medium and an electronic device. Based onthe improved RCF boundary constraint model, the invention extracts the urban single building contour, the rural isolated building contour and the peripheral boundary of the rural building dense group,at the same time, a U-Net semantic segmentation model network structure is improved, and the improved U-Net is utilized to classify the images at pixel level. Finally, the two models are fused, and the depth learning model is trained by a large number of building sample label data, so that the network model by fusing the improved U-Net and the RCF is used to extract the buildings on the sub-meterGao Fen 2 remote sensing images, so that the automatic and effective building vector data extraction is realized, and the time cost and labor cost of manual rendering is greatly reduced.

Description

technical field [0001] The invention belongs to the technical field of remote sensing image processing, and is a method for extracting buildings from high spatial resolution remote sensing images based on a deep learning boundary constraint algorithm, which is mainly applied to the automatic extraction of buildings from submeter-level remote sensing images. Background technique [0002] Buildings are the most likely to be added and changed in the geographic database, and also the part that most needs to be updated. Due to the importance of buildings for urban construction, GIS system updates, digital cities, and military reconnaissance, rapid extraction of building information technology and building change detection have important applications in urban development planning, electronic informatization, and national defense. . The extraction of artificial building information in remote sensing images is a complicated process, which not only requires automatic recognition by ...

AI Technical Summary

Problems solved by technology

[0004] At present, the technical methods of building extraction based on remote sensing images can only extract relatively regular buildings with obvious features, and their versatility is poor. When the buildings are relatively dense, the extraction effect is general. key steps

[0005] At present, the building vector obtained by using deep learning for building extraction method has a large gap with the actual building plot represented by the image, and it is difficult to apply it to land use survey

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