A Dashboard to Analysis and Synthesis of Dimensionality Reduction Methods in Remote Sensing
This work provides a tool for remote sensing researchers to improve classification accuracy, but it is incremental as it synthesizes existing methods without introducing new algorithms.
The paper addresses the challenge of poor classification accuracy in hyperspectral images due to high dimensionality and redundancy by surveying existing dimensionality reduction methods and synthesizing a dashboard to help users analyze and hypothesize feature selection and extraction software.
Hyperspectral images (HSI) classification is a high technical remote sensing software. The purpose is to reproduce a thematic map . The HSI contains more than a hundred hyperspectral measures, as bands (or simply images), of the concerned region. They are taken at neighbors frequencies. Unfortunately, some bands are redundant features, others are noisily measured, and the high dimensionality of features made classification accuracy poor. The problematic is how to find the good bands to classify the regions items. Some methods use Mutual Information (MI) and thresholding, to select relevant images, without processing redundancy. Others control and avoid redundancy. But they process the dimensionality reduction, some times as selection, other times as wrapper methods without any relationship . Here , we introduce a survey on all scheme used, and after critics and improvement, we synthesize a dashboard, that helps user to analyze an hypothesize features selection and extraction softwares.