Hyperspectral Image Classification Bi-dimensional Empirical mode Decomposition and Deep Residual Networks

In this study a novel approach of hyperspectral image classification technique is realized using BEMD (Bi-Dimensional Empirical Mode Decomposition) and Deep Residual Networks. First Principal Component of the hyperspectral image dataset is computed using PCA(Principal Component Analysis) feature ext...

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Bibliographic Details
Main Authors: Jonnadula, Harikiran, Kumar, Ladi Sandeep, Panda, G. K., Dash, Ratnakar, Kumar, Ladi Pradeep
Format: Conference Proceeding
Language:English
Subjects:
Bi-dimensional Empirical Mode Decomposition
Classification
Deep Residual Networks
Feature extraction
Hyperspectral Image
Hyperspectral imaging
Image Processing
Kernel
Neural networks
Principal component analysis
RESNET
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Summary:In this study a novel approach of hyperspectral image classification technique is realized using BEMD (Bi-Dimensional Empirical Mode Decomposition) and Deep Residual Networks. First Principal Component of the hyperspectral image dataset is computed using PCA(Principal Component Analysis) feature extraction technique. The model also adapts BEMD algorithm to divide the principle component into three hierarchical components and obtain BIMFs (Bi-Dimensional Intrinsic Mode Functions) and residue-image. These BIMFs and residue image is further taken as input to the deep residual network for classification. The results of experiments on two popular datasets of hyperspectral remote sensing scenes represent that the proposed-model offers a competitive analytical-performance in comparison to some established methods.
ISSN:2640-5768
DOI:10.1109/AISP48273.2020.9073241