Importance of CNN in the Classification of Remote Sensing Images

被引：0
|
作者：
Kurian, Vinija [1 ]
Jacob, Vinodkumar [1 ]
机构：
[1] APJ Abdul Kalam Technol Univ, Mar Athanasius Coll Engn, Dept ECE, Kothamangalam, Kerala, India
来源：
2023 ADVANCED COMPUTING AND COMMUNICATION TECHNOLOGIES FOR HIGH PERFORMANCE APPLICATIONS, ACCTHPA | 2023年
关键词：
CNN; classification of images; remote sensing dataset; measures for evaluation; CONVOLUTIONAL NEURAL-NETWORK;
D O I：
10.1109/ACCTHPA57160.2023.10083375
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
In this paper we can observe how CNN plays an important role in the classification of remote sensing images. Here we have discussed about different methods that are available for classification of images. The commonly used remote sensing dataset are mentioned here and the structure of convolutional neural network is described. The current state of the art of classification of remote sensing images also presented here.Various measures for evaluation of classification-Average Accuracy(AA),Overall Accuracy(OA) and Kappa coefficient of different deep neural networks that are available in literature are also compared here.
引用
收藏
页数：4
相关论文
共 50 条
  • [1] Scene Classification of Optical Remote Sensing Images Based on CNN Automatic Transfer
    Quan, Jicheng
    Wu, Chen
    Wang, Hongwei
    Wang, Zhiqiang
    [J]. PROCEEDINGS OF 2018 IEEE INTERNATIONAL CONFERENCE ON AUTOMATION, ELECTRONICS AND ELECTRICAL ENGINEERING (AUTEEE), 2018, : 110 - 114
  • [2] Scene classification for remote sensing images with self-attention augmented CNN
    Liu, Zongyin
    Dong, Anming
    Yu, Jiguo
    Han, Yubing
    Zhou, You
    Zhao, Kai
    [J]. IET IMAGE PROCESSING, 2022, 16 (11) : 3085 - 3096
  • [3] Multiscale Classification of Remote Sensing Images
    dos Santos, Jefersson Alex
    Gosselin, Philippe-Henri
    Philipp-Foliguet, Sylvie
    Torres, Ricardo da S.
    Falcao, Alexandre Xavier
    [J]. IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, 2012, 50 (10): : 3764 - 3775
  • [4] CNN with coefficient of variation-based dimensionality reduction for hyperspectral remote sensing images classification
    Zhang K.
    Hei B.
    Zhou Z.
    Li S.
    [J]. Hei, Baoqin (heibq@csu.ac.cn), 2018, Science Press (22): : 87 - 96
  • [5] A hybrid classification method with dual-channel CNN and KELM for hyperspectral remote sensing images
    Zhao, Huimin
    Wang, Chenchen
    Chen, Huayue
    Chen, Tao
    Deng, Wu
    [J]. INTERNATIONAL JOURNAL OF REMOTE SENSING, 2023, 44 (01) : 289 - 310
  • [6] Classification of Remote Sensing Images Using EfficientNet-B3 CNN Model With Attention
    Alhichri, Haikel
    Alswayed, Asma S.
    Bazi, Yakoub
    Ammour, Nassim
    Alajlan, Naif A.
    [J]. IEEE ACCESS, 2021, 9 : 14078 - 14094
  • [7] Hyperspectral Remote Sensing Images Terrain Classification Based on LDA and 2D-CNN
    Liu, Jing
    Li, Yang
    Wu, Meiyi
    Liu, Yi
    [J]. ADVANCES IN NATURAL COMPUTATION, FUZZY SYSTEMS AND KNOWLEDGE DISCOVERY, ICNC-FSKD 2022, 2023, 153 : 157 - 164
  • [8] SEMI-SUPERVISED SCENE CLASSIFICATION FOR REMOTE SENSING IMAGES BASED ON CNN AND ENSEMBLE LEARNING
    Dai, Xueyuan
    Wu, Xiaofeng
    Wang, Bin
    Zhang, Liming
    [J]. IGARSS 2018 - 2018 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM, 2018, : 4732 - 4735
  • [9] Searching for CNN Architectures for Remote Sensing Scene Classification
    Broni-Bediako, Clifford
    Murata, Yuki
    Mormille, Luiz H. B.
    Atsumi, Masayasu
    [J]. IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, 2022, 60
  • [10] CNN-GCN-based dual-stream network for scene classification of remote sensing images
    Deng P.
    Xu K.
    Huang H.
    [J]. National Remote Sensing Bulletin, 2021, 25 (11) : 2270 - 2282
12345