Image processing and machine learning‐based classification method for hyperspectral images. Issue 2 (28th January 2021)

• Record Type: Journal Article
• Authors: Yaman, Orhan; Yetiş, Hasan; Karaköse, Mehmet
• Is Part Of: Journal of engineering. Volume 2021:Issue 2(2021)
• Page Start: 85
• Page End: 96
• Publication Date: 2021-01-28
• Subjects: Probability theory, stochastic processes, and statistics -- Instrumentation and techniques for geophysical, hydrospheric and lower atmosphere research -- Other topics in statistics -- Optical, image and video signal processing -- Geophysical techniques and equipment -- Other topics in statistics -- Computer vision and image processing techniques -- Geophysics computing -- Machine learning (artificial intelligence) -- Support vector machines; Engineering -- Periodicals; Engineering; Electronic journals; Periodicals; 620.005
• DOI: 10.1049/tje2.12012
• Languages: English
• ISSNs: 2051-3305

Abstract

Abstract: Hyperspectral images are used to produce effective solutions in many areas due to the number of information they have. However, the number of information they provide may not bring an advantage always, in contrast, it may cause confusion sometimes. In this study, we propose a machine learning‐based classification method on the reduced bands using median and mean filters. The proposed method is tested on Indian Pines, Pavia University and Salinas datasets. After applying normalization, median filter and mathematical morphology for each band in an image, a feature matrix with only one band is achieved. Then, the most significant features are selected by using relief feature selection algorithm. The selected features are used for classification with support vector machine and K nearest neighbourhood. For all methods and datasets, a success rate above 99% in terms of accuracy is achieved. The proposed method has two significant contributions. First, when the proposed method is compared with the similar studies in the literature, it is clearly seen that the features selected by relief algorithm significantly increase the success rate of classification algorithms. Second significant contribution of using relief algorithm is obtaining faster methods by reducing the number of hyperspectral bands.