Quantitative Analysis on Lossy Compression in Remote Sensing Image Classification

被引：2
|
作者：
Xia, Yatong [1 ]
Li, Zimeng [1 ]
Chen, Zhenzhong [1 ]
Yang, Daiqin [1 ]
机构：
[1] Wuhan Univ, Sch Remote Sensing & Informat Engn, Wuhan 430079, Hubei, Peoples R China
来源：
VISUAL INFORMATION PROCESSING AND COMMUNICATION VI | 2015年 / 9410卷
关键词：
Remote sensing image compression; Classification accuracy; LS-SVM; ALGORITHMS; SVM;
D O I：
10.1117/12.2083205
中图分类号：
TM [电工技术]; TN [电子技术、通信技术];
学科分类号：
0808 ; 0809 ;
摘要：
In this paper, we propose to use a quantitative approach based on LS-SVM to perform estimation of the impact of lossy compression on remote sensing image compression. Kernel function selection and the model parameters computation are studied for remote sensing image classification when LS-SVM analysis model is establish. The experiments show that our LS-SVM model achieves a good performance in remote sensing image compression analysis. Classification accuracy variation according to compression ratio scales are summarized based on our experiments.
引用
收藏
页数：8
相关论文
共 50 条
  • [21] Quantitative regularization in robust vision transformer for remote sensing image classification
    Song, Huaxiang
    Yuan, Yuxuan
    Ouyang, Zhiwei
    Yang, Yu
    Xiang, Hui
    PHOTOGRAMMETRIC RECORD, 2024, 39 (186): : 340 - 372
  • [22] Lossy Compression of Multichannel Remote Sensing Images with Quality Control
    Lukin, Vladimir
    Vasilyeva, Irina
    Krivenko, Sergey
    Li, Fangfang
    Abramov, Sergey
    Rubel, Oleksii
    Vozel, Benoit
    Chehdi, Kacem
    Egiazarian, Karen
    REMOTE SENSING, 2020, 12 (22) : 1 - 35
  • [23] QUANTITATIVE ANALYSIS OF PRIMARY ATTRIBUTION EXPLAINABLE ARTIFICIAL INTELLIGENCE METHODS FOR REMOTE SENSING IMAGE CLASSIFICATION
    Mohan, Akshatha
    Peeples, Joshua
    IGARSS 2023 - 2023 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM, 2023, : 950 - 953
  • [24] Preliminary filtering and lossy compression of noisy remote sensing images
    Zemliachenko, Alexander N.
    Abramov, Sergey K.
    Lukin, Vladimir V.
    Vozel, Benoit
    Chehdi, Kacem
    IMAGE AND SIGNAL PROCESSING FOR REMOTE SENSING XXIV, 2018, 10789
  • [25] Influence of lossy compression on hyperspectral image classification accuracy
    Minguillón, J
    Pujol, J
    Serra, J
    Ortuño, I
    DATA MINING II, 2000, 2 : 545 - 554
  • [26] Remote Sensing Image Compression: A Review
    Zhou, Shichao
    Deng, Chenwei
    Zhao, Baojun
    Xia, Yatong
    Li, Qisheng
    Chen, Zhenzhong
    2015 1ST IEEE INTERNATIONAL CONFERENCE ON MULTIMEDIA BIG DATA (BIGMM), 2015, : 406 - 410
  • [27] On the Impact of Lossy Compression on Hyperspectral Image Classification and Unmixing
    Garcia-Vilchez, Fernando
    Munoz-Mari, Jordi
    Zortea, Maciel
    Blanes, Ian
    Gonzalez-Ruiz, Vicente
    Camps-Valls, Gustavo
    Plaza, Antonio
    Serra-Sagrista, Joan
    IEEE GEOSCIENCE AND REMOTE SENSING LETTERS, 2011, 8 (02) : 253 - 257
  • [28] Analysis of the complexity of remote sensing image and its role on image classification
    Yang, XM
    Zhou, CH
    IGARSS 2000: IEEE 2000 INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM, VOL I - VI, PROCEEDINGS, 2000, : 2179 - 2181
  • [29] Analysis of Supervised Maximum Likelihood Classification for Remote Sensing Image
    Sisodia, Pushpendra Singh
    Tiwari, Vivekanand
    Kumar, Anil
    2014 RECENT ADVANCES AND INNOVATIONS IN ENGINEERING (ICRAIE), 2014,
  • [30] A Novel Spatial Analysis Method for Remote Sensing Image Classification
    Gao, Jianqiang
    Xu, Lizhong
    NEURAL PROCESSING LETTERS, 2016, 43 (03) : 805 - 821
12345