改进残差网络的遥感图像场景分类Remote sensing scene classification algorithm based on improved residual network
章晨,夏凯,杨垠晖,冯海林,杜晓晨
摘要(Abstract)：
针对因常规残差网络(ResNet)结构缺少跨维度特征整合而导致遥感图像场景分类准确率不高的问题,该文提出了一种改进残差网络的遥感图像场景分类方法。首先,利用残差结构降低深层网络的复杂度,减少参数量,解决网络退化的问题;然后,采用1×1的卷积结构降低特征维度,增加网络的宽度,整合遥感图像的空间信息和纹理特征。在NWPU-RESISC45数据集上进行实验,准确率达到了93.63%,较集成卷积神经网络方法的分类精度提高了1.1%,体现了改进的残差网络在遥感图像场景分类中的有效性和可行性。
关键词(KeyWords)： 遥感图像;残差网络;特征整合;场景分类
基金项目(Foundation): 浙江省自然科学基金委员会-青山湖科技城管委会联合基金项目(LQY18C160002);; 浙江省科技重点研发计划资助项目(2018C02013)
作者(Author): 章晨,夏凯,杨垠晖,冯海林,杜晓晨
DOI: 10.16251/j.cnki.1009-2307.2020.08.023
参考文献(References)：
- [1] BIAN X,CHEN C,TIAN L,et al.Fusing local and global features for high-resolution scene classification[J].IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing,2017,10(6):2889-2901.
- [2] LOWE D.Distinctive image features from scale-invariant keypoints[J].International Journal of Computer Vision,2004,60(2):91-110.
- [3] OUMA Y O,TATEISHI R.Urban-trees extraction from Quickbird imagery using multiscale spectex-filtering and non-parametric classification[J].ISPRS Journal of Photogrammetry and Remote Sensing,2008,63(3):333.
- [4] ZHAO L J,TANG P,HUO L Z.Land-use scene classification using a concentric circle-structured multiscale bag-of-visual-words model[J].IEEE Journal of Selected Topics in Applied Earth Observations & Remote Sensing,2014,7(12):4620-4631.
- [5] HINTON G E,SALAKHUTDINOV R R.Reducing the dimensionality of data with neural networks[J].Science,2006,313(5786):504-507.
- [6] LECUN Y,BENGIO Y,HINTON G E.Deep learning[J].Nature,2015,521(7553):436-444.
- [7] 张洪群,刘雪莹,杨森,等.深度学习的半监督遥感图像检索[J].遥感学报,2017,21(3):406-414.(ZHANG Hongqun,LIU Xueying,YANG Sen,et al.Retrieval of remote sensing images based on semisupervised deep learning[J].Journal of Remote Sensing,2017,21(3):406-414.)
- [8] 刘大伟,韩玲,韩晓勇.基于深度学习的高分辨率遥感影像分类研究[J].光学学报,2016,36(4):298-306.(LIU Dawei,HAN Ling,HAN Xiaoyong.High spatial resolution remote sensing image classification based on deep learning[J].Acta Optica Sinica,2016,36(4):298-306.)
- [9] KRIZHEVSKY A,SUTSKEVER I,HINTON G E.ImageNet classification with deep convolutional neural networks[C]//Advances in Neural Information Processing Systems(NIPS).[S.l.]:[s.n.],2012:1097-1105.
- [10] SIMONYAN K,ZISSERMAN A.Very deep convolutional networks for large-scale image recognition[C]//International Conference on Learning Repre-sentations(ICLR).[S.l.]:[s.n.],2015.
- [11] SZEGEDY C,LIU W,JIA Y Q,et al.Going deeper with convolutions[C]//IEEE Conference on Computer Vision and Pattern Recognition(CVPR).[S.l.]:[s.n.],2015:1-9.
- [12] HE K M,ZHANG X Y,REN S Q,et al.Deep residual learning for image recognition[C]//IEEE Conference on Computer Vision and Pattern Recognition(CVPR).[S.l.]:[s.n.],2016:770-778.
- [13] CHENG G,HAN J W,LU X Q.Remote sensing image scene classification:benchmark and state of the art[J].Proceedings of the IEEE,2017,105(10):1865-1883.
- [14] ZHAO B,HUANG B,ZHONG Y F.Transfer learning with fully pretrained deep convolution networks for land-use classification[J].IEEE Geoscience and Remote Sensing Letters,2017,14(9):1436-1440.
- [15] HAN X B,ZHONG Y F,CAO L Q,et al.Pre-trained alexnet architecture with pyramid pooling and supervision for high spatial resolution remote sensing image scene classification[J].Remote Sensing,2017,9(8):848.
- [16] 张晓男,钟兴,朱瑞飞,等.基于集成卷积神经网络的遥感影像场景分类[J].光学学报,2018,38(11):342-352.(ZHANG Xiaonan,ZHONG Xing,ZHU Ruifei,et al.Scene classification of remote sensing images based on integrated convolutional neural networks[J].Acta Optica Sinica,2018,38(11):342-352.)