基于U-Net的高分辨率遥感图像土地利用信息提取

随着现代遥感技术的迅速发展,遥感图像的质量和数量得到了显著的提升,新技术带来的高分辨率遥感图像所蕴含的信息也更加丰富,如何利用人工智能手段辅助挖掘这些丰富的信息也成为了遥感图像分析与理解的重要内容.与此同时,以深度卷积神经网络为代表的人工智能技术在图像处理领域大放异彩.得益于类人眼的分层卷积池化模型,深度卷积神经网络可...

Research on Land Use Information Extraction based on U-Net

With the rapid development of modern remote sensing technology， remote sensing image with high quality and quantity has been significantly promoted， new technology of high resolution remote sensing images contain more abundant information， how to make full use of the means of artificial intelligence auxiliary to mine these abundant information has become one of the important researches in remote sensing image analysis and understanding. At the same time， represented by deep convolutional neural networks based Artificial Intelligence （AI） technology is brilliant in the field of image processing. Thanks to the layer-wised convolutional and pooling structures which mimces human brain retinal systems， deep convolutional neural network can achieve excellent performance in image segmentation and classification. So this paper proposed a U-Net based model to extract features from high resolution remote sensing images with 2 m spatial resolution. Different from traditional methods based on hand craft image features， the proposed model can be automatically applied on massive amounts of high resolution remote sensing image feature extraction， it can also exert complicated nonlinear characteristics of high resolution remote sensing image with the help of the spectral features and texture features. The experimental results show that the time of using the U-Net model to calculate the land use classification of Xinchang County is 55.7s， and the accuracy is 90.95%， and the kappa coefficient is 0.86. U-Net model can quickly and accurately obtain the land cover features in high-resolution remote sensing images， and can get high-precision land use classification results， which shows that the deep learning into remote sensing image land use classification extraction has a broad prospect.