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Article

Robust Classification Technique for Hyperspectral Images Based on 3D-Discrete Wavelet Transform

by 1,*, 2 and 2
1
Research Scholar, Department of Electronics and Communication Engineering, Amrita School of Engineering, Amrita Vishwa Vidyapeetham, Coimbatore 641112, India
2
Department of Electronics and Communication Engineering, Amrita School of Engineering, Amrita Vishwa Vidyapeetham, Coimbatore 641112, India
*
Author to whom correspondence should be addressed.
Academic Editor: Sathishkumar Samiappan
Received: 1 February 2021 / Revised: 13 March 2021 / Accepted: 16 March 2021 / Published: 25 March 2021
(This article belongs to the Special Issue Wavelet Transform for Remote Sensing Image Analysis)
Hyperspectral image classification is an emerging and interesting research area that has attracted several researchers to contribute to this field. Hyperspectral images have multiple narrow bands for a single image that enable the development of algorithms to extract diverse features. Three-dimensional discrete wavelet transform (3D-DWT) has the advantage of extracting the spatial and spectral information simultaneously. Decomposing an image into a set of spatial–spectral components is an important characteristic of 3D-DWT. It has motivated us to perform the proposed research work. The novelty of this work is to bring out the features of 3D-DWT applicable to hyperspectral images classification using Haar, Fejér-Korovkin and Coiflet filters. Three-dimensional-DWT is implemented with the help of three stages of 1D-DWT. The first two stages of 3D-DWT are extracting spatial resolution, and the third stage is extracting the spectral content. In this work, the 3D-DWT features are extracted and fed to the following classifiers (i) random forest (ii) K-nearest neighbor (KNN) and (iii) support vector machine (SVM). Exploiting both spectral and spatial features help the classifiers to provide a better classification accuracy. A comparison of results was performed with the same classifiers without DWT features. The experiments were performed using Salinas Scene and Indian Pines hyperspectral datasets. From the experiments, it has been observed that the SVM with 3D-DWT features performs better in terms of the performance metrics such as overall accuracy, average accuracy and kappa coefficient. It has shown significant improvement compared to the state of art techniques. The overall accuracy of 3D-DWT+SVM is 88.3%, which is 14.5% larger than that of traditional SVM (77.1%) for the Indian Pines dataset. The classification map of 3D-DWT + SVM is more closely related to the ground truth map. View Full-Text
Keywords: discrete wavelet transform; support vector machine; machine learning; K-nearest neighbor; random forest; classification; hyperspectral image discrete wavelet transform; support vector machine; machine learning; K-nearest neighbor; random forest; classification; hyperspectral image
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MDPI and ACS Style

Anand, R.; Veni, S.; Aravinth, J. Robust Classification Technique for Hyperspectral Images Based on 3D-Discrete Wavelet Transform. Remote Sens. 2021, 13, 1255. https://0-doi-org.brum.beds.ac.uk/10.3390/rs13071255

AMA Style

Anand R, Veni S, Aravinth J. Robust Classification Technique for Hyperspectral Images Based on 3D-Discrete Wavelet Transform. Remote Sensing. 2021; 13(7):1255. https://0-doi-org.brum.beds.ac.uk/10.3390/rs13071255

Chicago/Turabian Style

Anand, R, S Veni, and J Aravinth. 2021. "Robust Classification Technique for Hyperspectral Images Based on 3D-Discrete Wavelet Transform" Remote Sensing 13, no. 7: 1255. https://0-doi-org.brum.beds.ac.uk/10.3390/rs13071255

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