摘要
基于遥感图像的地物要素分类与提取是实现数字化战场建设、智能化战场感知的关键支撑技术之一。实际应用平台运算资源有限、样本匮乏导致训练不充分等制约深度神经网络的遥感图像地物分类效果。基于张量链式分解和权重共享,设计空谱卷积长短时记忆单元的两种张量扩展结构,提出轻量级张量空谱卷积长短时记忆网络用于遥感图像分类。在两个公开高光谱遥感图像数据集进行实验,该算法仅需0.34MB存储空间,较同类方法实现更优分类性能。
Classification and extraction of geographical elements based on remote sensing images(RSIs)is one of the key technologies for digital battlefield construction and intelligent battlefield perception.Actual application platforms face the problems of limited computing resources and insufficient training under small-samples,limiting the classification performance of geographical elements in deep neural networks.With tensor-train(TT)decomposition and weight sharing,the spatial-spectral convolutional long short-term memory(ConvLSTM3D)unit is extended to two tensorized structures(TTConvLSTM3D)and a lightweight Tensorized Spatial-spectral TTConvLSTM3D Neural Network(TST3DNN)is proposed for the classification of remote sensing images.The results on two hyperspectral HSI datasets show that TST3DNN only requires 0.34 MB of storage space,achieving better classification performance than that of the similar methods.
作者
胡文帅
李伟
李恒超
张蒙蒙
陶然
HU Wenshuai;LI Wei;LI Hengchao;ZHANG Mengmeng;TAO Ran(School of Information and Electronics,Beijing Institute of Technology,Beijing 100081,China;School of Information Science and Technology,Southwest Jiaotong University,Chengdu 611756,China)
出处
《指挥与控制学报》
CSCD
北大核心
2024年第4期458-468,共11页
Journal of Command and Control
基金
多谱信息智能处理技术全国重点实验室基金(6142113220205)
国家自然科学基金(62271418)
国家资助博士后研究人员计划(GZC20242184)
中国博士后科学基金(2024M75088)
福建省空间信息感知与智能处理重点实验室(阳光学院)开放基金(FKLSIPIP1016)
河南省通用航空技术重点实验室开放基金(ZHKF-230211)资助。
关键词
遥感图像
网络轻量化
卷积长短时记忆网络
张量分解
精细分类
remote sensing images
lightweight network
convolutional long short-term memory
tensor decomposition
fine classification
