• Kushalatha. M. R
  • Dr. H. S. Prashantha
  • Beena. R. Shetty


Image classification has become a hot topic in the field of remote sensing. In general, the complex characteristics of multispectral data make the accurate classification of such data challenging for traditional machine learning methods. In addition, multispectral imaging often deals with an inherently nonlinear relation between the captured spectral information and the corresponding materials. In this paper, we propose a novel classification method for multispectral imagery, named as support vector machine (SVM). Pixel multispectral imagery can be represented by amplitude, phase and residual in frequency. Its applicability and effects are assessed by the experiment using data set, in which CNN, and KNN based feature extraction methods are adopted for comparison, aiming to evaluate the performance of the proposed method. Experimental results illustrate that the proposed model gains the highest classification accuracy. Comparison of various performance parameters showed that KNN works better than SVM.

Keywords: Multispectral l imaging, Support Vector Machines, K-Nearest Neighbour, Classification accuracy


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[1] Rahul Nigam1, Rojalin Tripathy, Sujay Dutta, Nita Bhagia, Rohit Nagori, K. Chandrasekar, Rajsi Kot, Bimal K. Bhattacharya and Susan Ustin (2019) “Crop type discrimination and health assessment using Hyperspectral imaging” Journal of Current Science, Vol. 116, 10.
[2] Qureshi, R., Uzair, M., Khurshid, K., & Yan, H. (2019), “Hyperspectral document image processing: Applications, challenges, and future prospects. Pattern Recognition”, 90, 12-22.
[3] Zhang, L., Zhang, L., Du, B., You, J., & Tao, D. (2019), “Hyperspectral Image Unsupervised Classification by Robust Manifold Matrix Factorization”, Information Sciences, 485, 154-169.
[4] Liu, Y., Wu, T., Yang, J., Tan, K., & Wang, S (2019), “Hyperspectral band selection for soybean classification based on information measure in FRS theory, Bio- systems Engineering, 2019 178, 219-232.
[5] Zhou, F., Hang, R., Liu, Q., & Yuan, X. (2019), “Hyperspectral image classification using spectral-spatial LSTMs”, Neurocomputing, 328, 39 - 47.
[6] Lan, R, Li, Z., Liu, Z., Gu, T., & Luo, X. (2019), “Hyperspectral image classification using k-sparse de- noising autoencoder and spectral–restricted spatial characteristics”, Applied Soft Computing, 2019, pp 74, 693-708.
[7] Carlan, I, Mihai, B. A., Nistor, C., & Grobe-Stoltenberg (2019), "Identifying urban vegetation stress factors based on open access remote sensing imagery and field observations”, Ecological Informatics 55, pp. 01032.
[8] Hasan, M, Ullah, S., Khan, M. J., & Khurshid, K (2019), “Comparative Analysis of SVM, ANN and CNN for Classifying Vegetation Species Using Hyperspectral Thermal Infrared Data”, International Archives of the Photogrammetric, Remote Sensing & Spatial Information Sciences.
[9] Sun, Z., Zhao, X., Wu, M. and Wang, C (2019), “Extracting Urban Impervious Surface from WorldView-2 and Airborne LiDAR Data Using 3D Convolutional Neural Networks”, Journal of the Indian Society of Remote Sensing, 47(3), pp.401-412.
[10] Brabant Ca, Alvarez-Vanhard E., Morin G., Thanh Nguyen K., Laribi A., Houet T (2018), “Evaluation of Dimensional Reduction Methods on Urban Vegetation Classification Performance using hyperspectral Data”, IEEE, pp.1636-1639.
[11] Cao Jinging, Wanchun Leng, Kai Liu, Lin Liu, Zhi He ID and Yuanhui Zhu (2018), “Object-Based Mangrove Species Classification Using Unmanned Aerial Vehicle Hyperspectral Images and Digital Surface Models”, Article published in Hyper Spectral Remote Sensing for Forest and Trees outside Forests.
[12] Peng, J, Jiang, X., Chen, N., & Fu, H (2018), “Local Adaptive Joint Sparse representation for Hyperspectral image classification. Neuro - computing”, 2019 334, 239-248.
[13] Zhao, G., Liu, G., Fang, L., Tu, B., & Ghamisi, P (2018), Multiple Convolutional Layers Fusion Framework for Hyperspectral Image Classification. Neurocomputing, 2019, pp 339, 149-160.
[14] Yinhua, X Gao, R Wei – Optik (2018), “Hyper spectral image classification based on adaptive segmentation”, Optik Volume 172.
[15] Annala, L., Eskelinen, M., Hämäläinen, J., Riihinen, A. and Pölönen, I (2018), “Practical approach for Hyperspectral image processing in python”, International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences (Vol. 42, No.3), International Society for Photogrammetry and Remote Sensing.
[16] Brabant, C., Alvarez-Vanhard, E., Morin, G., Laribi, A. and Houet, T (2018), “Evaluation of Dimensional Reduction Methods on Urban Vegetation Classification Performance Using Hyperspectral Data”, IGARSS 2018-2018 IEEE International Geosciences and Remote Sensing Symposium (pp. 1636-1639), IEEE.
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How to Cite
R, K. M., Prashantha, D. H. S., & Shetty, B. R. (2021). COMPARISON OF IMAGE CLASSIFICATION TECHNIQUES AND ITS APPLICATIONS. Asian Journal For Convergence In Technology (AJCT), 7(1), 58-62.