Classification accuracy for stratification with remotely sensed data

被引：0
|
作者：
Czaplewski, RL [1 ]
Patterson, PL [1 ]
机构：
[1] US Forest Serv, USDA, Rocky Mt Res Stn, Ft Collins, CO 80526 USA
来源：
FOREST SCIENCE | 2003年 / 49卷 / 03期
关键词：
forest inventory and monitoring; forest statistics;
D O I：
暂无
中图分类号：
S7 [林业];
学科分类号：
0829 ; 0907 ;
摘要：
Tools are developed that help specify the classification accuracy required from remotely sensed data. These tools are applied during the planning stage of a sample survey that will use poststratification, prestratification with proportional allocation, or double sampling for stratification. Accuracy standards are developed in terms of an "error matrix," which is familiar-to remote sensing specialists. In addition, guidance is provided to determine when new remotely sensed classifications are needed to maintain acceptable levels of statistical precision with stratification.
引用
收藏
页码：402 / 408
页数：7
相关论文
共 50 条
  • [41] Beach hydromorphological classification through image classification techniques applied to remotely sensed data
    Teodoro, A. C.
    Pais-Barbosa, J.
    Veloso-Gomes, F.
    Taveira-Pinto, F.
    REMOTE SENSING FOR ENVIRONMENTAL MONITORING, GIS APPLICATIONS, AND GEOLOGY IX, 2009, 7478
  • [42] Non-linear methods in remotely sensed multispectral data classification
    Nikolov, Hristo S.
    Petkov, Doyno I.
    Jeliazkova, Nina
    Ruseva, Stela
    Boyanov, Kiril
    ADVANCES IN SPACE RESEARCH, 2009, 43 (05) : 859 - 868
  • [43] Review of studies on tree species classification from remotely sensed data
    Fassnacht, Fabian Ewald
    Latifi, Hooman
    Sterenczak, Krzysztof
    Modzelewska, Aneta
    Lefsky, Michael
    Waser, Lars T.
    Straub, Christoph
    Ghosh, Aniruddha
    REMOTE SENSING OF ENVIRONMENT, 2016, 186 : 64 - 87
  • [44] Optimizing Classification Accuracy of Remotely Sensed Imagery with DT-CWT Fused Images
    Renza, Diego
    Martinez, Estibaliz
    Arquero, Agueda
    PROGRESS IN PATTERN RECOGNITION, IMAGE ANALYSIS, COMPUTER VISION, AND APPLICATIONS, PROCEEDINGS, 2009, 5856 : 1031 - +
  • [45] Crisp and fuzzy classification accuracy measures for remotely sensed derived land cover maps
    Arora, MK
    Shalan, MA
    Elgy, J
    GEODYNAMICS-BOOK, 2005, : 11 - 27
  • [46] The importance of spatial accuracy in characterizing stand types using remotely sensed data
    Sivrikaya, Fatih
    AFRICAN JOURNAL OF BIOTECHNOLOGY, 2011, 10 (66): : 14891 - 14906
  • [47] Classification Performances Of Data Mining Clustering Algorithms For Remotely Sensed Multispectral Image Data
    Erol, Hamza
    Tyoden, Bala Mikat
    Erol, Recep
    2018 INNOVATIONS IN INTELLIGENT SYSTEMS AND APPLICATIONS (INISTA), 2018,
  • [48] Effects of Compression on Classification Performance and Discriminant Information Preservation in Remotely Sensed Data
    Lee, Chulhee
    Youn, Sungwook
    Baek, Jeoung Yeol
    Serra Sagrista, Joan
    SATELLITE DATA COMPRESSION, COMMUNICATIONS, AND PROCESSING XI, 2015, 9501
  • [49] INVESTIGATING THE EFFECTS OF ENSEMBLE CLASSIFICATION OF REMOTELY SENSED DATA FOR LAND COVER MAPPING
    Abe, Bolanle
    Gidudu, Anthony
    Marwala, Tshilidzi
    2010 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM, 2010, : 2832 - 2835
  • [50] An improved SVM method P-SVM for classification of remotely sensed data
    Zhang, R.
    Ma, J.
    INTERNATIONAL JOURNAL OF REMOTE SENSING, 2008, 29 (20) : 6029 - 6036
12345