Linear and Non-Linear Models for Remotely-Sensed Hyperspectral Image Visualization

Radu-Mihai Coliban,Cosmin Hatfaludi,Mihai Ivanovici,Maria Marincaş

doi:10.3390/rs12152479

Radu-Mihai Coliban, Cosmin Hatfaludi + Show 2 more

Open Access

https://doi.org/10.3390/rs12152479

Copy DOI

Journal: Remote sensing	Publication Date: Aug 2, 2020
Citations: 8	License type: CC BY 4.0

Affiliation: Transylvania University of Brașov

Abstract

The visualization of hyperspectral images still constitutes an open question and may have an important impact on the consequent analysis tasks. The existing techniques fall mainly in the following categories: band selection, PCA-based approaches, linear approaches, approaches based on digital image processing techniques and machine/deep learning methods. In this article, we propose the usage of a linear model for color formation, to emulate the image acquisition process by a digital color camera. We show how the choice of spectral sensitivity curves has an impact on the visualization of hyperspectral images as RGB color images. In addition, we propose a non-linear model based on an artificial neural network. We objectively assess the impact and the intrinsic quality of the hyperspectral image visualization from the point of view of the amount of information and complexity: (i) in order to objectively quantify the amount of information present in the image, we use the color entropy as a metric; (ii) for the evaluation of the complexity of the scene we employ the color fractal dimension, as an indication of detail and texture characteristics of the image. For comparison, we use several state-of-the-art visualization techniques. We present experimental results on visualization using both the linear and non-linear color formation models, in comparison with four other methods and report on the superiority of the proposed non-linear model.

Highlights

Hyperspectral imaging captures high-resolution spectral information covering the visible and the infrared wavelength spectra, and can provide a high-level understanding of the land cover objects [1]
The non-linear color formation model that we propose is based on an Artificial Neural Network (ANN) [39], with the input feature vector consisting of a spectral reflectance curve and the output being the corresponding RGB value
Each figure is organized as follows: on the top row, the results obtained with the proposed linear approach using the Gaussian functions (Figure 5); on the middle row, the results obtained with the linear approach using camera spectral sensitivity functions (Figure 4); on the bottom row, the results obtained using the proposed ANN approach (Section 2.4), the approach based on the principal component analysis (PCA) to RGB mapping [15], the linear approach based on the stretched color matching functions (CMF) [18] and two recent approaches, constrained manifold learning (CML) [25] and decolorization-based hyperspectral visualization (DHV) [21]

Summary

Introduction

Hyperspectral imaging captures high-resolution spectral information covering the visible and the infrared wavelength spectra, and can provide a high-level understanding of the land cover objects [1]. It is used in a wide variety of applications, such as agriculture [2,3], forest management [4,5], geology [6,7] and military/defense applications [8,9]. In order to address this problem, a series of hyperspectral image visualization techniques have been developed, which can be included in the following broad categories: band selection, PCA-based approaches, linear approaches, approaches based on digital image processing techniques and machine/deep learning methods. More complex unsupervised band selection approaches have been developed, based on the one-bit transform (1BT) [12], normalized information (NI) [13], linear prediction (LP) or the minimum endmember abundance covariance (MEAC) [14]

Objectives

Methods

Results

Discussion

Conclusion