A novel convolutional neural network architecture of multispectral remote sensing images for automatic material classification
作者：
Highlights：
• A convolutional neural network (CNN) architecture to classify material in multispectral remote sensing images to simplify future models’ construction.
• The RUNet model of multiple convolutional neural network architectures for material classification.
• The RUNet model is based on an improved U-Net architecture combined with the shortcut connections approach.
• The encoding layer includes 10 convolution layers and 4 pooling layers.
• The decoding layer has 4 upsampling layers, 8 convolution layers, and one classified convolution layer.
摘要
•A convolutional neural network (CNN) architecture to classify material in multispectral remote sensing images to simplify future models’ construction.•The RUNet model of multiple convolutional neural network architectures for material classification.•The RUNet model is based on an improved U-Net architecture combined with the shortcut connections approach.•The encoding layer includes 10 convolution layers and 4 pooling layers.•The decoding layer has 4 upsampling layers, 8 convolution layers, and one classified convolution layer.
论文关键词：Terrain reconstruction,Remote sensing image,Multispectral image,Convolutional neural network,Material classification
论文评审过程：Received 20 September 2020, Revised 24 March 2021, Accepted 15 May 2021, Available online 25 May 2021, Version of Record 3 June 2021.
论文官网地址：https://doi.org/10.1016/j.image.2021.116329
