宇航学报 ›› 2022, Vol. 43 ›› Issue (6): 811-819.doi: 10.3873/j.issn.1000-1328.2022.06.012

• 制导、导航、控制与电子 • 上一篇    下一篇

遥感场景分类的批处理协方差神经网络方法

郑天佑,王强   

  1. 哈尔滨工业大学航天学院,哈尔滨 150001
  • 收稿日期:2021-08-17 修回日期:2021-09-04 出版日期:2022-06-15 发布日期:2022-06-15
  • 基金资助:
    国家自然科学基金(11973021);国家自然科学基金(61773145);国家自然科学基金(61876054);国家自然科学基金(61973098)

A Batch Covariance Neural Network for Remote Sensing Scene Classification

ZHENG Tianyou, WANG Qiang   

  1. School of Astronautics, Harbin Institute of Technology, Harbin 150001, China
  • Received:2021-08-17 Revised:2021-09-04 Online:2022-06-15 Published:2022-06-15

摘要: 针对卫星遥感图像场景分类数据集中存在的局部区域特征异常问题,提出一种采用批处理协方差层的神经网络(CovNN)模型进行遥感场景分类的方法。该方法通过计算全输入通道的局部区域均值实现一种3D批处理协方差算法,能够有效消除局部区域均值的影响,从而更好地处理局部光照过强和局部区域存在无关特征的问题。将其应用于存在局部光照异常和局部无关特征问题的卫星采集AID数据集和NWPU RESISC45数据集中,实验表明CovNN在两个数据集上均取得了超过现有卷积神经网络(CNN)的召回率,可有效降低图像局部区域特征异常的不利影响。

关键词: 遥感卫星, 人工智能, 深度神经网络, 批处理协方差, 迁移学习

Abstract: A method using a batch covariance neural network (CovNN) for remote sensing scene classification is proposed in this paper to reduce the influence of local abnormal feature in remote sensing scene datasets. The batch covariance layer in CovNN can effectively minus the influence caused by the mean of the whole channel for the input feature maps, which acts as a 3D covariance layer. The CovNN can solve the problems of local variations in light intensity and irrelevant features in the local region. Experiments on the AID and NWPU RESISC45 datasets are conducted to test the recall rates of CovNN compared with other state of the art (SOTA) convolutional neural network (CNN) models. Both datasets have abnormal features in the local region of the images, and the proposed CovNN improves the recall rates compared with other CNN models, which are demonstrated by experiments.

Key words: Remote sensing satellite, Artificial intelligence, Deep neural network, Batch covariance, Transfer learning

中图分类号: