Abstract
In this paper, a hyperspectral image classification technique is proposed using semi-supervised random forest (SSRF). Robust node splitting in the random forest requires enormous training data, which is scarce in remote sensing applications. In order to overcome this drawback, we propose utilizing unlabeled data in conjunction with labeled data to assist the splitting process. Moreover, in order to tackle the curse of dimensionality associated with a hyperspectral image, we explore nonnegative matrix factorization (NMF) to remove redundant information. Experimental results confirm the efficacy of the proposed method.
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Mather PM, Koch M (2010) Computer processing of remotely-sensed images, 4th edn. Wiley, New York
Baraldi A, Bruzzone L, Blonda P (2005) Quality assessment of classification and cluster maps without ground truth knowledge. IEEE Trans Geosci Remote Sens 43:857–873
Hughes GF (1968) On the mean accuracy of statistical pattern recognition. IEEE Trans Inf Theory 14:55–63
Bruzzone L, Chi M, Marconcini M (2006) A novel transductive SVM for semisupervised classification of remote-sensing images. IEEE Trans Geosci Remote Sens 44:3363–3373
Gomez-Chova L, Camps-Valls G, Munnoz-Mari J, Calpe J (2008) Semisupervised image classification with Laplacian support vector machines. IEEE Geosci Remote Sens Lett 5:336–340
Camps-Valls G, Bandos TV, Zhou D (2007) Semi-supervised graph-based hyperspectral image classification. IEEE Geosci Remote Sens Lett 45:3044–3054
Ratle F, Camps-Valls G, Weston J (2010) Semisupervised neural networks for efficient hyperspectral image classification. IEEE Trans Geosci Remote Sens 48(5):2271–2282
Zhang T, Oles F (2000) A probability analysis on the value of unlabeled data for classification problems. In: 17th International conference on machine learning. ACM Press, California, pp 1191–1198
Lee DD, Seung S (2000) Algorithms for non-negative matrix factorization. In: Neural information processing systems. MIT Press, Denver, pp 556–562
Breiman L (1996) Bagging predictors. Mach Learn 26:123–140
Liu X, Song M, Tao D, Liu Z, Zhang L, Chen C, Bu J (2013) Semi-supervised node splitting for random forest construction. In: Conference on computer vision and pattern recognition. IEEE Press, Oregon, pp 492–499
Silverman B (1986) Density estimation for statistics and data analysis. Chapman and Hall/CRC, London
Criminisi A, Shotton J, Konukoglu E (2011) Decision forests for classification, regression, density estimation, mainfold learning and semi-supervised learning. Technical report, Microsoft Research
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The authors thank Prof. Gamba for providing the University of Pavia dataset.
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Adhikary, S.K., Dhekane, S.G. (2019). Hyperspectral Image Classification Using Semi-supervised Random Forest. In: Pandian, D., Fernando, X., Baig, Z., Shi, F. (eds) Proceedings of the International Conference on ISMAC in Computational Vision and Bio-Engineering 2018 (ISMAC-CVB). ISMAC 2018. Lecture Notes in Computational Vision and Biomechanics, vol 30. Springer, Cham. https://doi.org/10.1007/978-3-030-00665-5_102
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DOI: https://doi.org/10.1007/978-3-030-00665-5_102
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