Dimensionality Reduction using Bi-Dimensional Empirical Mode Decomposition Method for Hyperspectral Image Segmentation
B.Raviteja1, K.Venkata Rao2
1Mr.B.Ravi Teja, Asst. Professor, Dept of CSE, GITAM University. Visakhapatnam.
2Dr.K.Venkat Rao, Professor, Dean, Academics in the Dept. of Computer Science and Systems Engineering, Andhra University, Visakhapatnam.
Manuscript received on November 11, 2019. | Revised Manuscript received on November 20 2019. | Manuscript published on 30 November, 2019. | PP: 11300-11304 | Volume-8 Issue-4, November 2019. | Retrieval Number: D9566118419/2019©BEIESP | DOI: 10.35940/ijrte.D9566.118419
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: This paper presents a dimensionality reduction of hyperspectral dataset using bi-dimensional empirical mode decomposition (BEMD). This reduction method is used in a process for segmentation of hyperspectral data. Hyperspectral data contains multiple narrow bands conveying both spectral and spatial information of a scene. Analysis of this kind of data is done in three sequential stages, dimensionality reduction, fusion and segmentation. The method presented in this paper mainly focus on the dimensionality reduction step using BEMD, fusion is carried out using hierarchical fusion method and the segmentation is carried out using Clustering algorithms. This dimensionality reduction removes less informative bands in the data set, decreasing the storage and processing load in further steps in analysis of data. The qualitative and quantitative analysis shows that best informative bands are selected using proposed method which gets high quality segmented image using FCM.
Keywords: Bidimensional Empirical Mode Decomposition, Fuzzy C-Means, Hyperspectral Imaging.
Scope of the Article: Fuzzy Logics.