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

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.

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