Accuracy assessment of remote sensing classification techniques

被引:0
|
作者
Elghazali, S. [1 ]
Wishahy, Z. [1 ]
Ismail, M. [1 ]
机构
[1] Faculty of Engineering, Cairo University, Cairo, Egypt
来源
| 2001年 / Cairo University卷 / 48期
关键词
Edge detection - Feature extraction - Mathematical models - Maximum likelihood estimation - Satellites;
D O I
暂无
中图分类号
学科分类号
摘要
Remote sensing data are expressed as digital numbers per pixel which means that they are not object oriented, thus leading to some limitations for the automatic interpretation of the satellite images. The main objective of a classification process is to solve the problem of feature selection in order to obtain a thematic map from multi-spectral image. This research analyses the classified images to assess the accuracy of various classification processes, present their mathematical models and compare their performance. Many experiments have been performed using Landsat TM images with multi-bands observations. The best classifier deduced from the previous analysis was subsequently applied to Spot XS data. The results are verified by ground truth data and the accuracy of each classification technique is evaluated according to statistical accuracy's assessment rules.
引用
收藏
相关论文
共 50 条
  • [1] A comparison of resampling methods for remote sensing classification and accuracy assessment
    Lyons, Mitchell B.
    Keith, David A.
    Phinn, Stuart R.
    Mason, Tanya J.
    Elith, Jane
    REMOTE SENSING OF ENVIRONMENT, 2018, 208 : 145 - 153
  • [2] Assessment of Radiometric Resolution Impact on Remote Sensing Data Classification Accuracy
    Verde, Natalia
    Mallinis, Giorgos
    Tsakiri-Strati, Maria
    Georgiadis, Charalampos
    Patias, Petros
    REMOTE SENSING, 2018, 10 (08):
  • [3] Roughness classification utilizing remote sensing techniques for wind resource assessment
    Nayyar, Zeeshan Alam
    Ali, Ahmed
    RENEWABLE ENERGY, 2020, 149 : 66 - 79
  • [4] Spatial Stratification Mode and Differentiation Evaluation for Accuracy Assessment of Remote Sensing Classification
    Wu Y.
    Dong S.
    Xiao C.
    Li X.
    Pan Y.
    Niu C.
    Nongye Jixie Xuebao/Transactions of the Chinese Society for Agricultural Machinery, 2021, 52 (08): : 147 - 153
  • [5] A New Accuracy Assessment Method for One-Class Remote Sensing Classification
    Li, Wenkai
    Guo, Qinghua
    IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, 2014, 52 (08): : 4621 - 4632
  • [6] Supervised Classification Accuracy Assessment Using Remote Sensing and Geographic Information System
    Al-Aarajy, Khalid H. Abbas
    Zaeen, Ahmed A.
    Abood, Khaleel I.
    TEM JOURNAL-TECHNOLOGY EDUCATION MANAGEMENT INFORMATICS, 2024, 13 (01): : 396 - 403
  • [7] TAU-COEFFICIENTS FOR ACCURACY ASSESSMENT OF CLASSIFICATION OF REMOTE-SENSING DATA
    MA, ZK
    REDMOND, RL
    PHOTOGRAMMETRIC ENGINEERING AND REMOTE SENSING, 1995, 61 (04): : 435 - 439
  • [8] Accuracy assessment model for classification result of remote sensing image based on spatial sampling
    Huang, Dongmei
    Xu, Shoujue
    Sun, Jingqi
    Liang, Suling
    Song, Wei
    Wang, Zhenhua
    JOURNAL OF APPLIED REMOTE SENSING, 2017, 11
  • [10] Spatial analysis of remote sensing image classification accuracy
    Comber, Alexis
    Fisher, Peter
    Brunsdon, Chris
    Khmag, Abdulhakim
    REMOTE SENSING OF ENVIRONMENT, 2012, 127 : 237 - 246