Sparsity-regularized feature selection for multi-class remote sensing image classification
- 16 Downloads
Remote sensing image classification plays an important role in a wide range of applications and has caused widely concerns. During the last few years, great efforts have been made to develop a number of scene classification methods for remote sensing images. However, the existing remote sensing image classification methods do not perform satisfactorily in dealing with multi-class classification problems and rely heavily on the quality of data sets. These disadvantages seriously restrict the application of remote sensing image, including industrial research, analysis and calculation of land use and land coverage. To this end, this paper proposes a remote sensing image classification algorithm based on the sparse regularized feature learning method. Specifically, after constructing bag of features by using speeded up robust features extraction algorithm, direct sparsity optimization-based feature selection method is applied for selecting discriminative features, which is used for constructing support vector machine classifier model. The proposed algorithm has been evaluated and compared with other advanced feature selection methods on four public remote sensing image data sets. The experimental results demonstrate the effectiveness of our proposed image classification algorithm, which has been successfully applied to remote sensing image classification tasks.
KeywordsFeature extraction Bag of features Sparsity-regularized feature selection Remote sensing image classification
The research was supported by the National Key R&D Program of China under Grant No. 2017YFC0820604, Anhui Provincial Natural Science Foundation under Grant No. 1808085QF188, and National Nature Science Foundation of China under grant Nos. 61702156, 61502138, 61772171 and 61876056.
Compliance with ethical standards
Conflict of interest
The authors declare that they have no conflict of interest.
- 1.Castelluccio M et al (2015) Land use classification in remote sensing images by convolutional neural networks. arXiv:1508.00092
- 2.Yang J et al (2007) Evaluating bag-of-visual-words representations in scene classification. In: Proceedings of the international workshop on multimedia information retrieval. ACM, 2007Google Scholar
- 7.Nowak E, Jurie F, Triggs B (2006) Sampling strategies for bag-of-features image classification. In: European conference on computer vision. Springer, Berlin, Heidelberg, 2006Google Scholar
- 8.Peng H, Fan Y (2017) A general framework for sparsity regularized feature selection via iteratively reweighted least square minimization. In: AAAI 2017, pp 2471–2477Google Scholar
- 10.Wallach HM (2006) Topic modeling: beyond bag-of-words. In: Proceedings of the 23rd international conference on machine learning. ACM, 2006Google Scholar
- 11.Peng H, Fan Y (2016) Direct sparsity optimization based feature selection for multi-class classification. In: IJCAI 2016, pp 1918–1924Google Scholar
- 12.Bay H, Tuytelaars T, Van Gool L (2006) Surf: speeded up robust features. In: European conference on computer vision. Springer, Berlin, HeidelbergGoogle Scholar
- 17.Fei-Fei L, Perona P (2005) A bayesian hierarchical model for learning natural scene categories. In: IEEE computer society conference on computer vision and pattern recognition, 2005. CVPR 2005. IEEE, vol 2Google Scholar
- 20.Zhang H et al (2013) Attribute-augmented semantic hierarchy: towards bridging semantic gap and intention gap in image retrieval. In: Proceedings of the 21st ACM international conference on multimedia. ACM, 2013Google Scholar
- 23.Zhang H et al (2017) Visual translation embedding network for visual relation detection. In: CVPR 2017, pp 3107–3115Google Scholar
- 24.Nie F et al (2010) Efficient and robust feature selection via joint ℓ2, 1-norms minimization. In: NIPS 2010, pp 1813–1821Google Scholar
- 26.Zhang M et al (2014) Feature selection at the discrete limit. In: AAAI 2014, pp 1355–1361Google Scholar
- 30.Li H et al (2017) RSI-CB: a large scale remote sensing image classification benchmark via crowdsource data. arXiv:1705.10450