Abstract
Hyperspectral image classification has so many applications in the area of remote sensing. In recent years, deep learning has been accepted as a powerful tool for feature extraction and ensuring better classification accuracies. In this paper, model for HSI classification is created by implementing open set domain adaptation and generative adversarial networks (GAN). Open set domain adaptation is a type of domain adaptation where target has more classes which are not present in the source distribution. Huge dimension of hyperspectral image needs to be reduced for an efficient classification. In this work, we analysed the effect of dimensionality reduction for open set domain adaptation for hyperspectral image classification by using dynamic mode decomposition (DMD) technique. Experimental results show that 20% of the total available bands of Salinas and 30% of the bands of PaviaU dataset are the highest achievable reduction in feature dimension that results in almost same classification accuracy.
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References
Lodha, S.P., Kamlapur, S.M.: Dimensionality reduction techniques for hyperspectral images. Int. J. Appl. Innov. Eng. Manag. (IJAIEM) 3(10), 92–99 (2014)
Busto, P.P., Gall, J.: Open set domain adaptation. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 754–763 (2017)
Gretton, A., Borgwardt, K., Rasch, M., Scholkopf, B., Smola, A.J.: A kernel method for the two-sample-problem. In: Advances in Neural Information Processing Systems, pp. 513–520 (2007)
Saito, K., Yamamoto, S., Ushiku, Y., Harada, T.: Open set domain adaptation by backpropagation. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 153–168 (2018)
Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., Bengio, Y.: Generative adversarial nets. In: Advances in Neural Information Processing Systems, pp. 2672–2680 (2014)
Koonsanit, K., Jaruskulchai, C., Eiumnoh, A.: Band selection for dimension reduction in hyper spectral image using integrated information gain and principal components analysis technique. Int. J. Mach. Learn. Comput. 2(3), 248 (2012)
Fukunaga, K.: Introduction to Statistical Pattern Recognition. Elsevier, San Diego (2013)
Fong, M.: Dimension reduction on hyperspectral images. Univ. California, Los Angeles, CA (2007)
Megha, P., Sowmya, V., Soman, K.P.: Effect of dynamic mode decomposition-based dimension reduction technique on hyperspectral image classification. In: Computational Signal Processing and Analysis, pp. 89–99. Springer, New York (2018)
Charmisha, K.S., Sowmya, V., Soman, K.P.: Dimensionality reduction by dynamic mode decomposition for hyperspectral image classification using deep learning and kernel methods. In: Thampi, S.M., Marques, O., Krishnan, S., Li, K.-C., Ciuonzo, D., Kolekar, M.H. (eds.) Advances in Signal Processing and Intelligent Recognition Systems, pp. 256–267. Springer, Singapore (2019)
Bendale, A., Boult, T.E.: Towards open set deep networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1563–1572 (2016)
Bharath Bhushan, D., Sowmya, V., Sabarimalai Manikandan, M., Soman, KP.: An effective pre-processing algorithm for detecting noisy spectral bands in hyperspectral imagery. In: 2011 International Symposium on Ocean Electronics, pp. 34–39. IEEE (2011)
U del Pais Vasco. http://www.ehu.es/ccwintco/index.php/hyperspectral-remotesensing-scenes. Accessed 25 Aug 2012
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Krishnendu C. S., Sowmya, V., Soman, K.P. (2021). Impact of Dimension Reduced Spectral Features on Open Set Domain Adaptation for Hyperspectral Image Classification. In: Bhateja, V., Peng, SL., Satapathy, S.C., Zhang, YD. (eds) Evolution in Computational Intelligence. Advances in Intelligent Systems and Computing, vol 1176. Springer, Singapore. https://doi.org/10.1007/978-981-15-5788-0_69
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