Hyperspectral remote sensing image classification based on tighter random projection with minimal intra-class variance algorithm

Aiming at solving the problem of image size limiting in the traditional Random Projection (RP) algorithm, a novel Tighter Random Projection (TRP), which combines the scheme with Minimal Intra-class Variance (TRP-MIV) for hyperspectral remote sensing image classification is proposed. First, a new tighter dimensional boundary for expanding image size with the TRP-MIV matrix selected by multiple sampling for improving the class separability is defined to reduce dimension. Then the proposed algorithm is implemented, which integrates TRP-MIV for dimensionality reduction and Minimum Distance (MD) classifier for image classification. Finally, the image size and dimensionality reduction are evaluated by the number of spectral pixels under different theorems, and the spectral difference before and after dimensionality reduction, respectively. Classification performance is evaluated by kappa coefficient, Overall Accuracy (OA), Average Accuracy (AA), Average Precision Rate (APR) and running time. Classification results are obtained from the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) scanner and the Reflective Optics System Imaging Spectrometer (ROSIS) scanner, which indicate that the proposed algorithm is efficient and promising. (C) 2020 Elsevier Ltd. All rights reserved.

Automated summary

The novel Tighter Random Projection (TRP) algorithm combines Minimal Intra-class Variance (TRP-MIV) for hyperspectral remote sensing image classification by defining a tighter dimensional boundary for expanding image size and utilizing TRP-MIV for dimensionality reduction. The TRP-MIV matrix is selected by multiple sampling to improve class separability, and the algorithm integrates TRP-MIV for dimensionality reduction and Minimum Distance (MD) classifier for image classification.