Enhanced algorithm performance for land cover classification from remotely sensed data using bagging and boosting

被引：0
|
作者：
Chan, JCW [1 ]
Huang, CQ
DeFries, R
机构：
[1] Univ Maryland, Dept Geog, Lab Global Remote Sensing Studies, College Pk, MD 20742 USA
[2] Raytheon ITSS, EROS Data Ctr, USGS, Sioux Falls, SD 57101 USA
[3] Univ Maryland, Dept Geog, Earth Syst Sci Interdisciplinary Ctr, College Pk, MD 20742 USA
来源：
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING | 2001年 / 39卷 / 03期
关键词：
algorithm performance; bagging; boosting; voting classifications;
D O I：
暂无
中图分类号：
P3 [地球物理学]; P59 [地球化学];
学科分类号：
0708 ; 070902 ;
摘要：
Two ensemble methods, bagging and boosting, were investigated for improving algorithm performance. Our results confirmed the theoretical explanation [1] that bagging improves unstable, but not stable, learning algorithms, While boosting enhanced accuracy of a weak learner, its behavior is subject to the characteristics of each learning algorithm.
引用
收藏
页码：693 / 695
页数：3
相关论文
共 50 条
  • [1] Decision tree classification of land cover from remotely sensed data
    Friedl, MA
    Brodley, CE
    [J]. REMOTE SENSING OF ENVIRONMENT, 1997, 61 (03) : 399 - 409
  • [2] Land Cover Classification of Subarctic Wetlands Using Multisource Remotely Sensed Data
    Hu, Baoxin
    Brown, Glen
    Stirling, Callie
    Wang, Jianguo
    [J]. IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING, 2024, 17 : 5073 - 5092
  • [3] Estimation of land coverage from a land cover classification derived from remotely sensed data
    [J]. GeoJournal, 1995, 36 (04):
  • [4] Automatic land cover analysis for Tenerife by supervised classification using remotely sensed data
    Keuchel, J
    Naumann, S
    Heiler, M
    Siegmund, A
    [J]. REMOTE SENSING OF ENVIRONMENT, 2003, 86 (04) : 530 - 541
  • [5] Assessment of Machine Learning Algorithms for Land Cover Classification Using Remotely Sensed Data
    Park, Jeongmook
    Lee, Yongkyu
    Lee, Jungsoo
    [J]. SENSORS AND MATERIALS, 2021, 33 (11) : 3885 - 3902
  • [6] CLASSIFICATION METHODS FOR REMOTELY SENSED DATA: LAND USE AND LAND COVER CLASSIFICATION USING VARIOUS COMBINATIONS OF BANDS
    Mahmon, Nur Anis
    Ya'acob, Norsuzila
    Yusof, Azita Laily
    Jaafar, Jasmee
    [J]. JURNAL TEKNOLOGI, 2015, 74 (10): : 89 - 96
  • [7] Performance Evaluation of Land cover change detection Algorithms using remotely sensed Data
    Jude, L. Antony
    Suruliandi, A.
    [J]. 2014 IEEE INTERNATIONAL CONFERENCE ON CIRCUIT, POWER AND COMPUTING TECHNOLOGIES (ICCPCT-2014), 2014, : 1409 - 1415
  • [8] INVESTIGATING THE EFFECTS OF ENSEMBLE CLASSIFICATION OF REMOTELY SENSED DATA FOR LAND COVER MAPPING
    Abe, Bolanle
    Gidudu, Anthony
    Marwala, Tshilidzi
    [J]. 2010 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM, 2010, : 2832 - 2835
  • [9] Optical remotely sensed time series data for land cover classification: A review
    Gomez, Cristina
    White, Joanne C.
    Wulder, Michael A.
    [J]. ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING, 2016, 116 : 55 - 72
  • [10] Mapping Land Cover from Remotely Sensed Data with a Softened Feedforward Neural Network Classification
    Giles M. Foody
    [J]. Journal of Intelligent and Robotic Systems, 2000, 29 : 433 - 449
12345