A Whitening Transformation Based Approach to One-class Classification of Remote Sensing Imagery

doi:10.11947/j.AGCS.2015.20130439

Abstract

Abstract: In this study, a whitening transformation based approach to one-class classification of remote sensing imagery is investigated. Only positive data are required to train the one-class classifier. Firstly, the image data is mapped to a new feature space using the whitening processing with all directions of the class of interest. Then a threshold is selected to make a binary prediction. A heuristic method of threshold selection is performed in the experiment of one-class classification. A series of values are set to the threshold based on standard deviation, and perform the one-class classification with each threshold value. The experiment shows that high accuracy is achieved with the threshold range from 3 to 4 standard deviations of the mean. Finally, the results of one-class classification with the threshold of 3 standard deviations are compared to that of one-class support vector machine. The results indicate that the proposed method provides nearly the same accuracy of one-class classification as one-class support vector machine. The advantage of the proposed method is that it can use a constant threshold to extract various land types.

Key words: whitening transformation, one-classification, class of interest, threshold

CLC Number:

P237

BO Shukui, LI Xiang, LI Lingling. A Whitening Transformation Based Approach to One-class Classification of Remote Sensing Imagery[J]. Acta Geodaetica et Cartographica Sinica, 2015, 44(2): 190-197.

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