Spectral-Spatial Feature Reduction for Hyperspectral Image Classification with Optimized Technique Series

Abstract

Hyperspectral imaging (HSI) is increasingly being used in a wide range of practical applications. High correlations between and within classes, the curse of dimensionality, and overfitting have all piqued the interest of researchers. Because of the excellent feature extraction and good overall performance, deep learning-based algorithms with dimensionality reduction methods have been recommended as a viable alternative for hyperspectral image analysis. Principal component analysis (PCA) and Sparse PCA (SPCA) are commonly used in dimensionality reduction. Because each principal component is a linear mixture of the original variables, principal component analysis (PCA) is difficult to interpret, and SPCA cannot choose the most relevant features. To overcome the issue, a mutual information-based greedy feature selection approach called minimum redundancy maximum relevancy (mRMR) is combined with SPCA (SPCA-mRMR). Many deep learning combination techniques rely on the 2D and 3D Convolutional Neural Network (CNN) methodologies. A fused 3D-2D CNN model is less difficult than a 3D-CNN alone, yet it can also operate effectively in noisy settings with few training examples. Therefore, SPCA-mRMR and fused 3D-2D CNN are used to create a hybrid model called HMC-NET for HSI classification. Several optimization strategies are used to address overfitting and improve classification accuracy. To assess the efficacy of this proposed technique, performance results were compared to highly regarded models with fewer variables.