基于GAN的半监督遥感图像场景分类  

Semi-supervised Scene Classification of Remote Sensing Images Based on GAN

作　　者：夏英[1] 李骏垚 郭东恩 XIA Ying;LI Junyao;GUO Dongen(Chongqing University of Posts and Telecommunications,Chongqing Engineering Research Center of Spatial Big Data Intelligent Technology,Chongqing 400065,China;School of Computer and Software,Nanyang Institute of Technology,Nanyang,Henan 473000,China)

机构地区：[1]重庆邮电大学,空间大数据智能技术重庆市工程研究中心,重庆400065 [2]南阳理工学院计算机与软件学院,河南南阳473000

出　　处：《光子学报》2022年第3期28-39,共12页Acta Photonica Sinica

基　　金：国家自然科学基金(No.41871226);河南省科技攻关项目(No.212102210492);重庆市教委重点合作项目(No.HZ2021008)。

摘　　要：针对遥感图像背景复杂及有监督场景分类算法无法利用无标签数据的问题,提出一种基于生成对抗网络的半监督遥感图像场景分类方法。首先,引入谱归一化残差块代替传统生成对抗网络中的二维卷积,利用残差块的跳跃连接解决梯度消失问题;其次,引入特征融合思想,将浅层特征与深层特征进行融合,从而减少特征损失;最后,在生成对抗网络的判别器中加入结合门控的注意力模块,以增强特征判别能力。在EuroSAT和UC Merced数据集上的实验结果表明,该方法能够有效提取判别力更强的特征,提高半监督分类性能。Remote sensing image scene classification is an important and challenging problem of remote sensing image interpretation.With the generation of a large number of scene-rich high-resolution remote sensing images,scene classification of remote sensing images is widely used in many fields such as smart city construction,natural disaster monitoring and land resource utilization.Due to the advancement of deep learning techniques and the establishment of large-scale scene classification datasets,scene classification methods have been significantly improved.Although the classification methods based on deep learning have achieved high classification accuracy,the supervised methods require a large number of training samples,while the unsupervised classification methods are difficult to meet the practical needs and have low classification accuracy.Meanwhile,the annotation of remote sensing images requires rich engineering skills and expert knowledge,and in remote sensing applications,only a small amount of labeled remote sensing images exist for supervised training in most cases,and a large amount of unlabeled images cannot be fully utilized.Therefore,a semi-supervised learning method that extracts effective features from a large amount of unlabeled data by learning a small amount of labeled data becomes a potential way to solve such problems.To address the problems of complex background of remote sensing images and the inability of supervised scene classification algorithms to utilize unlabeled data,a semi-supervised remote sensing image scene classification method based on generative adversarial networks,namely,residual attention generative adversarial networks,is proposed.First,to enhance the stability of training,the residual blocks with jump structure are introduced in the deep neural network.At the same time,the spectral normalization constrains the spectral norm of the weight matrix in each convolutional layer of the residual block to ensure that the input and output of each batch of data satisfy the 1-Lipschitz con

关 键 词：遥感图像 场景分类 半监督 生成对抗网络 注意力机制 

分 类 号：TP391.4[自动化与计算机技术—计算机应用技术]