基于CNN的高分遥感影像深度语义特征提取研究综述
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摘要
近年来,深度学习作为计算机视觉的研究热点,在诸多方面得以发展与应用。特征提取是理解和分析高分遥感影像的关键基础。为促进高分遥感影像特征提取技术的发展,总结了深度学习模型在高分遥感影像特征提取技术的研究与发展,如:AlexNet,VGG-网和GoogleNet等卷积网络模型在深度语义特征提取中的应用。此外,重点分析和讨论了以卷积神经网络模型为基础的各类深度学习模型在高分遥感影像特征提取方面的应用与创新,如:迁移学习的应用;卷积神经网络(Convolutional Neural Network,CNN)模型结构的改变;CNN模型与其他模型结构的结合等方式,均提升了深度语义特征提取能力。最后,对卷积神经网络模型在高分遥感影像深度语义特征提取方面存在的问题以及后续可能的研究趋势进行了分析。
In recent years,deep learning has been developed and applied in many aspects as a research hotspot of computer vision.Feature extraction is the key basis for understanding and analyzing high-resolution remote sensing images.In order to promote the development of high-resolution remote sensing image feature extraction technology,the research and development of deep learning model in high-resolution remote sensing image feature extraction technology,such as:AlexNet,VGG-net,and GoogleNet convolutional network models,have been summarized in depth semantic features.In addition,the application of extraction is also focused on the application and innovation of various deep learning models based on convolutional neural network models in high-resolution remote sensing image feature extraction,such as:application of migration learning;The combination of the CNN model and other model structures enhances the ability to extract deep semantic features.Finally,the problems of the convolutional neural network model in the extraction of deep semantic features of high-resolution remote sensing images and the possible research trends are analyzed.
In recent years,deep learning has been developed and applied in many aspects as a research hotspot of computer vision.Feature extraction is the key basis for understanding and analyzing high-resolution remote sensing images.In order to promote the development of high-resolution remote sensing image feature extraction technology,the research and development of deep learning model in high-resolution remote sensing image feature extraction technology,such as:AlexNet,VGG-net,and GoogleNet convolutional network models,have been summarized in depth semantic features.In addition,the application of extraction is also focused on the application and innovation of various deep learning models based on convolutional neural network models in high-resolution remote sensing image feature extraction,such as:application of migration learning;The combination of the CNN model and other model structures enhances the ability to extract deep semantic features.Finally,the problems of the convolutional neural network model in the extraction of deep semantic features of high-resolution remote sensing images and the possible research trends are analyzed.
引文
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[37] Deng Z,Lei L,Sun H,et al.An Enhanced Deep Convolutional Neural Network for Densely Packed Objects Detection in Remote Sensing Images[C]//International Workshop on Remote Sensing with Intelligent Processing.IEEE,2017:1-4.
[38] Song X,Dai Y,Qin X.Deep Depth Super-Resolution:Learning Depth Super-Resolution Using Deep Convolutional Neural Network[J].Computer Science Computer Vision and Pattern Recognition,2016,10114:360-376.
[39] Liu F,Shen C,Lin G.Deep Convolutional Neural Fields for Depth Estimation from a Single Image[C]// Computer Vision and Pattern Recognition.IEEE,2015:5162-5170.
[40] Deng J,Dong W,Socher R,et al.ImageNet:A Large-scale Hierarchical Image Database[C]//IEEE Conference on Computer Vision and Pattern Recognition,2009:248-255.
[41] Cheng G,Zhou P,Han J.Learning Rotation-Invariant Convolutional Neural Networks for Object Detection in VHR Optical Remote Sensing Images[J].IEEE Transactions on Geoscience and Remote Sensing,2016,54(12):7405-7415.
[42] Liu Y,Zhong Y,Fei F,et al.Scene Semantic Classification based on Random-scale Stretched Convolutional Neural Network for High-spatial Resolution Remote Sensing Imagery[C]//IEEE Geoscience and Remote Sensing Symposium,2016:763-766.
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