Land cover classification with multi-sensor fusion of partly missing data
Date
2009-05Source Title
Photogrammetric Engineering and Remote Sensing
Print ISSN
0099-1112
Publisher
American Society for Photogrammetry and Remote Sensing
Volume
75
Issue
5
Pages
577 - 593
Language
English
Type
ArticleItem Usage Stats
146
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views
95
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downloads
Abstract
We describe a system that uses decision tree-based tools for seamless acquisition of knowledge for classification of remotely sensed imagery. We concentrate on three important problems in this process: information fusion, model understandability, and handling of missing data. Importance of multi-sensor information fusion and the use of decision tree classifiers for such problems have been well-studied in the literature. However, these studies have been limited to the cases where all data sources have a full coverage for the scene under consideration. Our contribution in this paper is to show how decision tree classifiers can be learned with alternative (surrogate) decision nodes and result in models that are capable of dealing with missing data during both training and classification to handle cases where one or more measurements do not exist for some locations. We present detailed performance evaluation regarding the effectiveness of these classifiers for information fusion and feature selection, and study three different methods for handling missing data in comparative experiments. The results show that surrogate decisions incorporated into decision tree classifiers provide powerful models for fusing information from different data layers while being robust to missing data. © 2009 American Society for Photogrammetry and Remote Sensing.
Keywords
Comparative experimentsData layer
Data source
Decision tree classifiers
Feature selection
Land cover classification
Missing data
Multi-sensor fusion
Multi-sensor information fusion
Performance evaluation
Remotely sensed imagery
Understandability
Classifiers
Data handling
Decision trees
Feature extraction
Information fusion
Learning systems
Remote sensing
Sensor data fusion
Comparative study
Image classification
Land cover
Modeling
Sensor
Permalink
http://hdl.handle.net/11693/22768Published Version (Please cite this version)
https://doi.org/10.14358/PERS.75.5.577Collections
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