Learning by counterexamples in remote sensing image classification: a case study for road extraction

ABSTRACT

The classification of multi- and hyper-spectral imagery is essential for several remote sensing applications. However, several challenges still hinder the accuracy and efficiency of learning algorithms. In diverse remote sensing applications, the high spectral heterogeneity between and within classes is often an issue that adds uncertainties to the classification. Most classifiers do not consider the influence of non-target classes and variations in the spectral similarity level between classes during classification. This paper presents a novel learning strategy for remote sensing image classification, which exploits non-target classes to enhance the generation of classification masks and the performance of supervised methods for specific classes of interest. It produces multiple classification instances, based on the incorporation of counterexample information into the training data set, in order to refine the separation of classes of interest in the available feature space. The experimental results demonstrate the potential of the proposed counterexample assisted learning for road extraction, using high spatial-resolution imagery data.

Acknowledgments

This work was supported by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) under Grant [No. 001], and the Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) under Grant [No. #309135/2015-0].

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

The data that support the findings of this study are available from the corresponding author, upon reasonable request.

Additional information

Funding

The work was supported by the Conselho Nacional de Desenvolvimento Científico e Tecnológico [309135/2015-0]; Coordenação de Aperfeiçoamento de Pessoal de Nível Superior [001].