One-class remote sensing classification: one-class vs. binary classifiers

Xueqing Deng

doi:10.1080/01431161.2017.1416697

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One-class remote sensing classification: one-class vs. binary classifiers

2018-01-03

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DOI：10.1080/01431161.2017.1416697

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Abstract

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Many applications of remote sensing only require the classification of a single land type. This is known as the one-class classification problem and it can be performed using either binary classifiers, by treating all other classes as the negative class, or one-class classifiers which only consider the class of interest. The key difference between these two approaches is in their training data and the amount of effort needed to produce it. Binary classifiers require an exhaustively labelled training data set while one-class classifiers are trained using samples of just the class of interest. Given ample and complete training data, binary classifiers generally outperform one-class classifiers. However, what is not clear is which approach is more accurate when given the same amount of labelled training data. That is, for a fixed labelling effort, is it better to use a binary or one-class classifier. This is the question we consider in this article. We compare several binary classifiers, including backpropagation neural networks, support vector machines, and maximum likelihood classifiers, with two one-class classifiers, one-class SVM, and presence and background learning (PBL), on the problem of one-class classification in high-resolution remote sensing imagery. We show that, given a fixed labelling budget, PBL consistently outperforms the other methods. This advantage stems from the fact that PBL is a positive-unlabelled method in which large amounts of readily available unlabelled data is incorporated into the training phase, allowing the classifier to model the negative class more effectively.

Keywords:

SUPERVISED IMAGE CLASSIFICATION

SUPPORT VECTOR MACHINES

NEURAL-NETWORKS

LAND

PROBABILITY

SET

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