Abstract
Recently, machine learning has produced appreciable performance results on various visual computing related studies, including the classification of common hyperspectral images. This study aims to compare the results of different machine learning models for the classification of a hyperspectral image dataset. The hyperspectral data captured from AVIRIS sensor covering scene over the Indian Pines test site in North-western Indiana and consists 224 spectral reflectance bands. The ground truth has sixteen classes including vegetation crops, built structures, etc. Accuracy assessments and confusion matrices were used to evaluate classification performance. The study includes classification results of mainly three learning models including dimensionally reduced data via PCA for SVM classification, CNN and k-NN. The overall accuracy in PCA-SVM results was 72.38%, CNN was 85% and k-NN was 66.21% concluding the better efficiency of CNN classification for the hyperspectral dataset. Hence CNN classification technique succeeded in the hyperspectral image classification.
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References
Chang, C.I.: Hyperspectral Imaging: Techniques for Spectral Detection and Classification. Kluwer Academic/Plenum Publishers, New York (2003)
Wan, Y.Q., Fan, Y.H., Jin, M.S.: Application of hyperspectral remote sensing for supplementary investigation of polymetallic deposits in Huaniushan ore region, Northwestern China. Sci. Rep. 11(1), 440 (2021). https://doi.org/10.1038/s41598-020-79864-0
Lu, B., Dao, P.D., Liu, J., He, Y., Shang, J.: Recent advances of hyperspectral imaging technology and applications in agriculture. Remote Sens. 12(16), 2659 (2020). https://doi.org/10.3390/rs12162659
Lua, G., Fei, B.: Medical hyperspectral imaging: a review. J. Biomed. Optics 19, 010901 (2014). https://doi.org/10.1117/1.JBO.19.1.010901
Briottet, X., Boucher, Y., Dimmeler, A.: Military applications of hyperspectral imagery. In: Proceedings of Targets and Backgrounds XII: Characterization and Representation, vol. 6239, p. 62390B (2006). https://doi.org/10.1117/12.672030
Li, S., Song, W., Fang, L., Chen, Y., Ghamisi, P., Benediktsson, J.: Deep learning for hyperspectral image classification: an overview. IEEE Trans. Geosci. Remote Sens. 57(9), 6690–6709 (2019). https://doi.org/10.1109/TGRS.2019.2907932
Bala, R., Kumar, D.: Classification using ANN: a review. Int. J. Comput. Intell. Res. 13(7), 1811–1820 (2017). ISSN 0973–1873
Gibbs-Bravo, A., Pennacchia, D.: Evaluating the performance of multilayer perceptrons and support vector machines on an image classification task (2019)
Chen, Y., Jiang, H., Li, C., Jia, X., Ghamisi, P.: Deep feature extraction and classification of hyperspectral images based on convolutional neural networks. IEEE Trans. Geosci. Remote Sens. 54(10), 6232–6251 (2016)
Chen, Y., Nasrabadi, N.M., Tran, T.D.: Hyperspectral image classification using dictionary-based sparse representation. IEEE Trans. Geosci. Remote Sens. 49(10), 3973–3985 (2011)
Haut, J.M., Paoletti, M., Plaza, J., Plaza, A.: Cloud implementation of the K-means algorithm for hyperspectral image analysis. J. Supercomput. 73(1), 514–529 (2016). https://doi.org/10.1007/s11227-016-1896-3
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Appendix
Appendix
Visualization of bands of the Indian Pines Hyperspectral Dataset shown below.
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Gautam, S., Tiwari, K.C. (2022). Effective Hyperspectral Image Classification Using Learning Models. In: Dev, A., Agrawal, S.S., Sharma, A. (eds) Artificial Intelligence and Speech Technology. AIST 2021. Communications in Computer and Information Science, vol 1546. Springer, Cham. https://doi.org/10.1007/978-3-030-95711-7_51
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