Spectral Image Data Fusion for Multisource Data Augmentation

TL;DR

This paper introduces a spectral image data fusion methodology using interpolation techniques to enhance data compatibility across sources, thereby improving machine learning model training and generalization in spectral imaging applications.

Contribution

The study presents novel interpolation methods for spectral data fusion, enabling the use of diverse spectral sources with fixed spectral signatures for machine learning tasks.

Findings

Interpolation improves spectral data compatibility

Enhanced model training with fused spectral data

Better generalization in spectral imaging models

Abstract

Multispectral and hyperspectral images are increasingly popular in different research fields, such as remote sensing, astronomical imaging, or precision agriculture. However, the amount of free data available to perform machine learning tasks is relatively small. Moreover, artificial intelligence models developed in the area of spectral imaging require input images with a fixed spectral signature, expecting the data to have the same number of spectral bands or the same spectral resolution. This requirement significantly reduces the number of usable sources that can be used for a given model. The scope of this study is to introduce a methodology for spectral image data fusion, in order to allow machine learning models to be trained and/or used on data from a larger number of sources, thus providing better generalization. For this purpose, we propose different interpolation techniques, in order to make multisource spectral data compatible with each other. The interpolation outcomes are evaluated through various approaches. This includes direct assessments using surface plots and metrics such as a Custom Mean Squared Error (CMSE) and the Normalized Difference Vegetation Index (NDVI). Additionally, indirect evaluation is done by estimating their impact on machine learning model training, particularly for semantic segmentation.