Hyperspectral image clustering with Albedo recovery Fuzzy C-Means.
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| Title: | Hyperspectral image clustering with Albedo recovery Fuzzy C-Means. |
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| Authors: | Azimpour, P.1 (AUTHOR), Shad, R.1 (AUTHOR) r.shad@um.ac.ir, Ghaemi, M.1 (AUTHOR), Etemadfard, H.1 (AUTHOR) |
| Source: | International Journal of Remote Sensing. Aug2020, Vol. 41 Issue 16, p6117-6134. 18p. 2 Color Photographs, 1 Diagram, 2 Charts. |
| Subjects: | Albedo, Remote sensing, Pixels, Hyperspectral imaging systems, Algorithms |
| Abstract: | Hyperspectral image clustering is usually used for unsupervised learning in different applications. However, the traditional clustering methods have not been considered the complex relationships among neighbouring pixels. The Albedo and Shading elements can define pixel values in the HyperSpectral Images (HSIs). In HSIs, features are different from each other because of their natural physical characteristics and the physical nature of different image features can be described by the Albedo element. Therefore, in this paper, we generate the natural Albedo feature of the HSIs by applying Albedo recovery step to exploit main information from HSIs. Then, we utilized the Fuzzy C-means clustering method to cluster the natural Albedo dataset. In this paper, we propose a novel accurate Albedo Recovery based Fuzzy C-Means (ARFCM) method to cluster HSIs. In the dataset, each feature vector is processed by the Albedo recovery step to create a new feature vector. This new feature vector can describe the dataset better than the original one. Comparing clustering methods as one of the powerful clustering algorithms are widely used in the remote sensing fields of studying. The experiments conducted on several benchmark datasets demonstrated that the proposed clustering method achieves higher performance than other methods and present the efficiency and effectiveness of the proposed method. The results of experiments over different HSI datasets indicated that the proposed method could produce reliable and suitable results compared to the other methods. This shows the robustness of the proposed ARFCM algorithm over the various HSI datasets. Other methods may provide a good response in a given dataset and do not perform well in the other data. Consequently, the ARFCM method, regardless of the study area characteristics and the sensor features, always renders remarkable clustering accuracy. [ABSTRACT FROM AUTHOR] |
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| Database: | Engineering Source |
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