Assessment of Component Selection Strategies in Hyperspectral Imagery

Edurne Ibarrola-Ulzurrun,Javier Marcello,Consuelo Gonzalo-Martin

doi:10.3390/e19120666

Edurne Ibarrola-Ulzurrun, Javier Marcello + Show 1 more

Open Access

https://doi.org/10.3390/e19120666

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Journal: Entropy	Publication Date: Dec 5, 2017
Citations: 20	License type: CC BY 4.0

Affiliation: University of Las Palmas de Gran Canaria

Abstract

Hyperspectral imagery (HSI) integrates many continuous and narrow bands that cover different regions of the electromagnetic spectrum. However, the main challenge is the high dimensionality of HSI data due to the ’Hughes’ phenomenon. Thus, dimensionality reduction is necessary before applying classification algorithms to obtain accurate thematic maps. We focus the study on the following feature-extraction algorithms: Principal Component Analysis (PCA), Minimum Noise Fraction (MNF), and Independent Component Analysis (ICA). After a literature survey, we have observed a lack of a comparative study on these techniques as well as accurate strategies to determine the number of components. Hence, the first objective was to compare traditional dimensionality reduction techniques (PCA, MNF, and ICA) in HSI of the Compact Airborne Spectrographic Imager (CASI) sensor and to evaluate different strategies for selecting the most suitable number of components in the transformed space. The second objective was to determine a new dimensionality reduction approach by dividing the CASI HSI regarding the spectral regions covering the electromagnetic spectrum. The components selected from the transformed space of the different spectral regions were stacked. This stacked transformed space was evaluated to see if the proposed approach improves the final classification.

Highlights

Hyperspectral imagery (HSI) has significantly contributed to the progress of remote sensing studies
HSI increases the computational load due to the enhancement of spectral resolution, which gives a high level of data dimensionality that can demean the results of the classification process [5]
3.1.2 shows the results of shows the results obtained from the Support Vector Machine (SVM) classification, while Section 3.1.2 shows the results of the the different component component selection selectionstrategies

Summary

Introduction

Hyperspectral imagery (HSI) has significantly contributed to the progress of remote sensing studies. HSI contains multiple continuous and narrow spectral bands, which cover different regions of the electromagnetic spectrum [1,2,3,4]. The high number of spectral bands associated with HSI changes the ratio between the number of training samples and the number of bands, causing a decrease of a classifier’s accuracy [6] This fact produces the ’Hughes’ phenomenon [7], which specifies that the size of the training sample set or Regions Of Interest (ROIs) needed for a given classifier increases exponentially with the number of spectral bands [8]. Dimensionality reduction decreases the feature dimensionality by removing redundant information while keeping the important information in the feature vector [10]

Objectives

Methods

Results

Conclusion