Target Identification from High Resolution Remote Sensing Image by Combining Multiple Classifiers

Authors:
Peijun Du;Hao Sun;Wei Zhang
Affiliations:
Department of Remote Sensing and Geographical Information Science, China University of Mining and Technology, Xuzhou City, P.R. China 221116;Department of Remote Sensing and Geographical Information Science, China University of Mining and Technology, Xuzhou City, P.R. China 221116;Department of Remote Sensing and Geographical Information Science, China University of Mining and Technology, Xuzhou City, P.R. China 221116
Venue:
MCS '09 Proceedings of the 8th International Workshop on Multiple Classifier Systems
Year:
2009

Citing 7
Cited 0

Data mining: practical machine learning tools and techniques with Java implementations

Data mining: practical machine learning tools and techniques with Java implementations
Three learning phases for radial-basis-function networks

Neural Networks
A Hierarchical Multiclassifier System for Hyperspectral Data Analysis

MCS '00 Proceedings of the First International Workshop on Multiple Classifier Systems
Inference for the Generalization Error

Machine Learning
Combining Pattern Classifiers: Methods and Algorithms

Combining Pattern Classifiers: Methods and Algorithms
Solving multiclass learning problems via error-correcting output codes

Journal of Artificial Intelligence Research
Hierarchical neural networks utilising dempster-shafer evidence theory

ANNPR'06 Proceedings of the Second international conference on Artificial Neural Networks in Pattern Recognition

Quantified Score

Hi-index	0.00

Visualization

Abstract

Target identification from high resolution remote sensing image is a common task for many applications. In order to improve the performance of target identification, multiple classifier combination is used to a QuickBird high resolution image, and some key techniques including selection and design of member classifier, classifier combination algorithm and target identification methods are investigated. After constructing a classifier ensemble composed of five members: maximum likelihood classifier (MLC), minimum distance classifier (MDC), Mahalanobis distance classifier (MHA), decision tree classifier (DTC) and support vector machine (SVM), double fault measure is used to select three classifiers for further combination. MLC, DTC and MHA are selected, and their independence and diversity are evaluated. Different classifier combination strategies are experimented to extract sports field and buildings from QuickBird image. The results show that multiple classifier combination can improve the performance of image classification and target identification, and the accuracy is affected by many factors.