高分辨率遥感影像深度迁移可变形卷积的场景分类法

Scene classification of high-resolution remote sensing imagery based on deep transfer deformable convolutional neural networks

近年来基于深度卷积神经网络的高分辨率遥感影像场景分类成为广泛关注的焦点。由于现有深度卷积神经网络对遥感场景影像的几何形变不具有稳健性,本文提出了一种基于深度迁移可变形卷积神经网络(DTDCNN)的场景分类方法。该方法先利用大型自然场景数据集ImageNet上训练的深度模型提取遥感影像的深度特征,然后引入可变形卷积层,进一步学习对遥感场景的几何形变具有稳健性的深度特征。结果表明:增加可变形卷积后,DTDCNN在AID、UC-Merced和NWPU-RESISC45数据集上的精度分别提高了4.25%、1.9%和4.83%。该方法通过对场景中不同目标进行感受野自适应调整,增强了空间采样位置能力,有效提高了遥感场景分类的精度。

In recent years,scene classification of high-resolution remote sensing images based on deep convolutional neural networks has become the focus of attention.Because of the existing deep convolution neural network is not robust to the geometric deformation of remote sensing scene image,we proposed a novel scene classification method for high-resolution remote sensing image,based on the deep transfer deformable convolutional neural networks(DTDCNN).Specifically,the depth features of remote sensing image are extracted by using the trained depth model on the large-scale natural scene dataset(ImageNet),then,the deformable convolution layer is introduced to learn the depth features which are robust to the geometric deformation of remote sensing scene.The results show that:the accuracy of DTDCNN on AID,UC-Merced and NWPU-RESISC45 datasets is improved by 4.25%,1.9%and 4.83%after adding the deformable convolution,respectively.By the adaptive adjustment of the receptive field for different objects in the scene,DTDCNN enhances the ability of spatial sampling position,and,effectively improves the accuracy of remote sensing scene classification.