Multilabel Remote Sensing Image Classification with Capsule Networks

被引：2
|
作者：
Topcu, Mucahit [1 ]
Dede, Abdulkadir [1 ]
Eken, Suleyman [2 ]
Sayar, Ahmet [1 ]
机构：
[1] Kocaeli Univ, Bilgisayar Muhendisligi, Kocaeli, Turkey
[2] Kocaeli Univ, Bilisim Sistemleri Muhendisligi, Kocaeli, Turkey
来源：
2ND INTERNATIONAL CONGRESS ON HUMAN-COMPUTER INTERACTION, OPTIMIZATION AND ROBOTIC APPLICATIONS (HORA 2020) | 2020年
关键词：
capsule networks; multilabel classification; remote sensing; deep learning;
D O I：
10.1109/hora49412.2020.9152917
中图分类号：
TP301 [理论、方法];
学科分类号：
081202 ;
摘要：
As a result of the developments in remote sensing technologies, the classification of remote sensing images according to user needs has gained great attention in recent years. Deep learning techniques are also known to increase the classification performance of remote sensing. In this study, image classification is made with capsule networks, which are deep artificial neural network model, on the multilabel datasets -Ankara Hyperspectral Image Archive and UC Merced Land Use. The performance of the classification is measured with various performance metrics.
引用
收藏
页码：316 / 318
页数：3
相关论文
共 50 条
  • [1] Exploring Transformer and Multilabel Classification for Remote Sensing Image Captioning
    Kandala, Hitesh
    Saha, Sudipan
    Banerjee, Biplab
    Zhu, Xiao Xiang
    [J]. IEEE GEOSCIENCE AND REMOTE SENSING LETTERS, 2022, 19
  • [2] Semantic interleaving global channel attention for multilabel remote sensing image classification
    Liu, Yongkun
    Ni, Kesong
    Zhang, Yuhan
    Zhou, Lijian
    Zhao, Kun
    [J]. INTERNATIONAL JOURNAL OF REMOTE SENSING, 2024, 45 (02) : 393 - 419
  • [3] Global Context-Based Multilevel Feature Fusion Networks for Multilabel Remote Sensing Image Scene Classification
    Wang, Xin
    Duan, Lin
    Ning, Chen
    [J]. IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING, 2021, 14 : 11179 - 11196
  • [4] Toward Multilabel Image Retrieval for Remote Sensing
    Imbriaco, Raffaele
    Sebastian, Clint
    Bondarev, Egor
    de With, Peter H. N.
    [J]. IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, 2022, 60
  • [5] Using Siamese capsule networks for remote sensing scene classification
    Zhou, Song
    Zhou, Yong
    Liu, Bing
    [J]. REMOTE SENSING LETTERS, 2020, 11 (08) : 757 - 766
  • [6] Multitask Fine-Grained Feature Mining for Multilabel Remote Sensing Image Classification
    Guo, Jie
    Sun, Hao
    Han, Jinheng
    Song, Bin
    Chi, Yuhao
    Song, Bingxi
    [J]. IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, 2024, 62
  • [7] Label-Driven Graph Convolutional Network for Multilabel Remote Sensing Image Classification
    Ma, Boyi
    Wu, Falin
    Hu, Tianyang
    Fathollahi, Loghman
    Sui, Xiaohong
    Liu, Yushuang
    Gantumur, Byambakhuu
    [J]. IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING, 2024, 17 : 2245 - 2255
  • [8] Recurrent neural networks for remote sensing image classification
    Lakhal, Mohamed Ilyes
    Cevikalp, Hakan
    Escalera, Sergio
    Ofli, Ferda
    [J]. IET COMPUTER VISION, 2018, 12 (07) : 1040 - 1045
  • [9] Deep Neural Networks for Remote Sensing Image Classification
    Miniello, Giorgia
    La Salandra, Marco
    Vino, Gioacchino
    [J]. INTELLIGENT COMPUTING, VOL 2, 2022, 507 : 117 - 128
  • [10] A novel multilabel classification of remote sensing images using XGBoost
    Bhagwat, Rohit Uttam
    Shankar, B. Uma
    [J]. 2019 IEEE 5TH INTERNATIONAL CONFERENCE FOR CONVERGENCE IN TECHNOLOGY (I2CT), 2019,
12345