Abstract
Hyperspectral images are becoming a valuable tool much used in agriculture, mineralogy, and so on. The challenge is to successfully classify the materials founded in the field relevant for different applications. Due to a large amount of data corresponding to a big number of spectral bands, the classification programs require a long time to analyze and classify the data. The purpose is to find a better method for reducing the classification time. We exploit various algorithms on real hyperspectral data sets to find out which algorithm is more effective. This paper presents a comparison of unsupervised hyperspectral image classification such as K-means, Hierarchical clustering, and Parafac decomposition, which allows the performance of the model reduction and feature extraction. The results showed that the method useful for big data is the classification of data after Parafac Decomposition.
Highlights
Hyperspectral images are a challenging area due to unfavorable resolution of images, high dimensionality, and insufficient ground truth data during training
We explore the efficacy of the tensor decomposition, k-means, hierarchical clustering and tensor decomposition classification
This study has addressed the problem of the classification of hyperspectral images using K-means, Parallel Factor Analysis (Parafac) Decomposition, and Hierarchical clustering
Summary
Hyperspectral images are a challenging area due to unfavorable resolution of images, high dimensionality, and insufficient ground truth data during training. The unsupervised classification discovers the information on its own and leaves the number of classes to be chosen by the user. Hyperspectral image classification is very complex and difficult to obtain because of image noises, complex background, various characters [2] or high dimensionality [3]. Existing classifiers do not always offer a better classification for all types of data, which is why studying the classification of hyperspectral images can provide us general information about classification accuracy. Hyperspectral images are an important tool for identifying materials spectrally unique, as it provides sufficient information for classifying data from different areas. Hyperspectral images could be used to caption the different phases of the eruptive activity of volcanoes, to obtain a detailed chronology of the lava flow emplacement and radiative power estimation [4]
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