An End-to-End Local-Global-Fusion Feature Extraction Network for Remote Sensing Image Scene Classification

被引:41
|
作者
Lv, Yafei [1 ]
Zhang, Xiaohan [1 ]
Xiong, Wei [1 ]
Cui, Yaqi [1 ]
Cai, Mi [1 ]
机构
[1] Naval Aviat Univ, Res Inst Informat Fus, Yantai 264001, Peoples R China
基金
中国国家自然科学基金;
关键词
Remote sensing image scene classification (RSISC); Gated Recurrent Units (GRUs); discriminative feature representation; local feature; OBJECT DETECTION; SCALE;
D O I
10.3390/rs11243006
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
Remote sensing image scene classification (RSISC) is an active task in the remote sensing community and has attracted great attention due to its wide applications. Recently, the deep convolutional neural networks (CNNs)-based methods have witnessed a remarkable breakthrough in performance of remote sensing image scene classification. However, the problem that the feature representation is not discriminative enough still exists, which is mainly caused by the characteristic of inter-class similarity and intra-class diversity. In this paper, we propose an efficient end-to-end local-global-fusion feature extraction (LGFFE) network for a more discriminative feature representation. Specifically, global and local features are extracted from channel and spatial dimensions respectively, based on a high-level feature map from deep CNNs. For the local features, a novel recurrent neural network (RNN)-based attention module is first proposed to capture the spatial layout information and context information across different regions. Gated recurrent units (GRUs) is then exploited to generate the important weight of each region by taking a sequence of features from image patches as input. A reweighed regional feature representation can be obtained by focusing on the key region. Then, the final feature representation can be acquired by fusing the local and global features. The whole process of feature extraction and feature fusion can be trained in an end-to-end manner. Finally, extensive experiments have been conducted on four public and widely used datasets and experimental results show that our method LGFFE outperforms baseline methods and achieves state-of-the-art results.
引用
收藏
页数:20
相关论文
共 50 条
  • [41] Bidirectional adaptive feature fusion for remote sensing scene classification
    Lu, Xiaoqiang
    Ji, Weijun
    Li, Xuelong
    Zheng, Xiangtao
    NEUROCOMPUTING, 2019, 328 : 135 - 146
  • [42] MULTI-GRAINED GLOBAL-LOCAL SEMANTIC FEATURE FUSION FOR FEW SHOT REMOTE SENSING SCENE CLASSIFICATION
    Liu, Yuqing
    Zhang, Tong
    Zhuang, Yin
    Wang, Guanqun
    Chen, He
    IGARSS 2023 - 2023 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM, 2023, : 6235 - 6238
  • [43] Local-global feature fusion network for hyperspectral image classification
    Gan, Yuquan
    Zhang, Hao
    Liu, Weihua
    Ma, Jieming
    Luo, Yiming
    Pan, Yushan
    INTERNATIONAL JOURNAL OF REMOTE SENSING, 2024, 45 (22) : 8548 - 8575
  • [44] Remote sensing image scene classification based on multilayer feature context encoding network
    Li Ruo-Yao
    Zhang Bo
    Wang Bin
    JOURNAL OF INFRARED AND MILLIMETER WAVES, 2021, 40 (04) : 530 - 538
  • [45] Remote Sensing Scene Classification Using Heterogeneous Feature Extraction and Multi-Level Fusion
    Wang, Xin
    Xu, Mingjun
    Xiong, Xingnan
    Ning, Chen
    IEEE ACCESS, 2020, 8 : 217628 - 217641
  • [46] End-to-end Image Dehazing Based on Ladder Network and Cross Fusion
    Yang Yan
    Zhang Jinlong
    Liang Xiaozhen
    ACTA PHOTONICA SINICA, 2022, 51 (02)
  • [47] An End-to-End Sequence Learning Approach for Text Extraction and Recognition from Scene Image
    Lalitha, G.
    Lavanya, B.
    INTERNATIONAL JOURNAL OF COMPUTER SCIENCE AND NETWORK SECURITY, 2022, 22 (07): : 220 - 228
  • [48] ADC-CPANet:A remote sensing image classification method based on local-global feature fusion
    Wang, Wei
    Li, Xijie
    Wang, Xin
    National Remote Sensing Bulletin, 2024, 28 (10) : 2661 - 2672
  • [49] IRU-Net: An Efficient End-to-End Network for Automatic Building Extraction From Remote Sensing Images
    Sheikh, Md Abdul Alim
    Maity, Tanmoy
    Kole, Alok
    IEEE ACCESS, 2022, 10 : 37811 - 37828
  • [50] Image Feature Fusion Based Remote Sensing Scene Zero-Shot Classification Algorithm
    Wu Chen
    Wang Hongwei
    Yuan Yuwei
    Wang Zhiqiang
    Liu Yu
    Cheng Hong
    Quan Jicheng
    ACTA OPTICA SINICA, 2019, 39 (06)