Title: 3D-2D BCNN based Automated Feature Extraction & Classification for Hyperspectral Imaging
Abstract: The main challenge of processing Hyperspectral Image is its high dimensionality. Most of the machine learning classification algorithm's accuracy diminishes as the dimensionality of feature increases. As a result, for working with Hyperspectral image classification, complex feature reduction techniques are performed. The proposed architecture of CNN hierarchically constructs high-level features by seeking low dimensional representation of Hyperspectral interpretation in an automated way, instead of working with a full spectral band or complex handcrafted features. Principal Component Analysis (PCA) is used to remove highly correlated spectral bands before feeding into the network. A combination of 3D and 2D convNet layers are used to preserve both the spectral and spatial information of the Hyperspectral data. A logistic regression layer is responsible for the classification task. The overall classification accuracy of the demonstrated approach is 99.43% which is much better than other conventional machine learning and deep learning methods.
Publication Year: 2021
Publication Date: 2021-08-16
Language: en
Type: article
Indexed In: ['crossref']
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