Abstract
With the resolution of remote sensing images is getting higher and higher, high-resolution remote sensing images are widely used in many areas. Among them, image information extraction is one of the basic applications of remote sensing images. In the face of massive high-resolution remote sensing image data, the traditional method of target recognition is difficult to cope with. Therefore, this paper proposes a remote sensing image extraction based on U-net network. Firstly, the U-net semantic segmentation network is used to train the training set, and the validation set is used to verify the training set at the same time, and finally the test set is used for testing. The experimental results show that U-net can be applied to the extraction of buildings.
Highlights
In recent years, the word "artificial intelligence" has become more and more hot
Conducts feasibility study on whether it can be applied to large-scale and high spatial resolution remote sensing image information extraction
U-net is a semantic segmentation network based on a full convolutional network
Summary
The word "artificial intelligence" has become more and more hot. From AlphaGo to machine translation, the application of deep learning methods has become more and more widespread. Most of the traditional remote sensing information extraction methods rely on the combination of manual interpretation and computer processing. This requires the interpreter has rich geoscience knowledge, and requires a lot of repetitive labor, and the method has low mobility [1,2]. The deep learning method allows the computer to automatically extract features without the need to manually design features, with strong generalization capabilities and good application prospects. Conducts feasibility study on whether it can be applied to large-scale and high spatial resolution remote sensing image information extraction. The extraction method is compared with the artificial intelligence end-to-end training method extraction efficiency, makeing the computer to automatically extract image features
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