图像级高光谱影像高分辨率特征网络分类方法  

A high-resolution feature network image-level classification method for hyperspectral image

作　　者：孙一帆 刘冰[1] 余旭初[1] 谭熊[1] 余岸竹[1] SUN Yifan;LIU Bing;YU Xuchu;TAN Xiong;YU Anzhu(Information Engineering University,Zhengzhou 450001,China)

机构地区：[1]信息工程大学,河南郑州450001

出　　处：《测绘学报》2024年第1期50-64,共15页Acta Geodaetica et Cartographica Sinica

基　　金：国家自然科学基金(41801388);河南省自然科学基金(222300420387)。

摘　　要：基于深度学习的高光谱影像分类方法通常将高光谱影像切分为局部方块作为模型的输入,这不但限制了长距离空-谱信息关联的获取,还带来了大量额外的计算开销。以全局图像作为输入的图像级分类方法能够有效避免这些缺陷,然而,现有的基于全卷积神经网络特征串行流动模式的图像级分类方法在信息恢复时的细节损失会导致分类精度低、分类图视觉效果差等问题。因此,本文提出一种基于HRNet的图像级高光谱影像快速分类方法,在全程保持高分辨率特征的基础上对影像的多重分辨率特征进行并行计算与交叉融合,从而缓解了传统特征串行流动模式造成的信息损失问题。同时,提出多分辨率特征联合监督和投票分类策略,进一步提升了模型分类性能。利用4组开源高光谱影像数据集对本文方法进行验证,试验结果表明,与现有的先进分类方法相比,本文方法能够取得具有竞争性的分类结果,同时显著减少训练和分类时长,在实际应用时更具时效性。为了保证方法的复现性,笔者将代码开源于https://github.com/sssssyf/fast-image-level-vote。Hyperspectral image(HSI)classification methods based on deep learning usually slice hyperspectral images into local-patches as the input of the model,which not only limits the acquisition of long-distance space-spectral information association,but also brings a lot of extra computational overhead.The image-level classification method with global image as input can effectively avoid these defects.However,the detail loss during information recovery of the existing image-level classification methods based on feature serial flow pattern of fully convolutional network(FCN)will lead to problems such as low classification accuracy and poor visual effect of the classification map.Therefore,this paper proposes a high-resolution feature network(HRNet)image-level classification method for hyperspectral image,which performs parallel computation and cross fusion of multi-resolution features of images while maintaining high-resolution features throughout the whole process,thus alleviating the information loss caused by the traditional serial flow pattern of features.Simultaneously,we propose a jointly-super vised training strategy of multi-resolution feature and a vote classification strategy,so as to further improve the classification performance of the model.Four public hyperspectral image datasets are used to verify the proposed method.Experimental results show that compared with the existing advanced classification methods,the proposed method can obtain competitive classification results,significantly reduce the training and classification time at the same time,and is more time-sensitive in practical application.In order to assure the reproducibility of method,we will open the code at https:∥github.com/sssssyf/fast-image-level-vote.

关 键 词：高光谱影像分类 图像级 全卷积神经网络 HRNet 

分 类 号：P237[天文地球—摄影测量与遥感]