Classification Of Hyperspectral Images
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Randomized Principal Component Analysis for Hyperspectral Image Classification
The high-dimensional feature space of the hyperspectral imagery poses major challenges to the processing and analysis of the hyperspectral data sets.
Spatio-spectral classification of hyperspectral images for brain cancer detection during surgical operations
Surgery for brain cancer is a major problem in neurosurgery.
A comprehensive review of 3D convolutional neural network-based classification techniques of diseased and defective crops using non-UAV-based hyperspectral images
State-of-the-art methods based on 1D- and 2D-CNNs struggle to efficiently extract spectral and spatial information.
A new filter for dimensionality reduction and classification of hyperspectral images using GLCM features and mutual information
In this paper, we introduce a new methodology for dimensionality reduction and classification of HSI taking into account both spectral and spatial information based on mutual information.
Hybridization of filter and wrapper approaches for the dimensionality reduction and classification of hyperspectral images
The proposed approach is compared to an effective reproduced filters approach based on mutual information.
A Novel Filter Approach for Band Selection and Classification of Hyperspectral Remotely Sensed Images Using Normalized Mutual Information and Support Vector Machines
For this purpose, this paper introduces a new filter approach for dimension reduction and classification of hyperspectral images using information theoretic (normalized mutual information) and support vector machines SVM.
A novel filter based on three variables mutual information for dimensionality reduction and classification of hyperspectral images
Band selection filter based on "Mutual Information" is a common technique for dimensionality reduction.
A novel information gain-based approach for classification and dimensionality reduction of hyperspectral images
In this paper, we propose a new filter approach based on information gain for dimensionality reduction and classification of hyperspectral images.
A Novel Approach for Dimensionality Reduction and Classification of Hyperspectral Images based on Normalized Synergy
Keywords: Hyperspectral images; target detection; pixel classification; dimensionality reduction; band selection; information theory; mutual information; normalized synergy
New wrapper method based on normalized mutual information for dimension reduction and classification of hyperspectral images
In this context, we propose a new wrapper method based on normalized mutual information (NMI) and error probability (PE) using support vector machine (SVM) to reduce the dimensionality of the used hyperspectral images and increase the classification efficiency.
