Spectral Image Data Fusion for Multisource Data Augmentation
This addresses the data scarcity issue for researchers in fields like remote sensing and precision agriculture, but it is incremental as it builds on existing interpolation methods.
The study tackled the problem of limited usable spectral image data for machine learning by introducing a spectral image data fusion methodology using interpolation techniques, which allowed models to be trained on more sources and improved generalization, as evaluated through metrics like CMSE and NDVI and tested on semantic segmentation tasks.
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.