Multi-Sensor Fusion: A Simulation Approach to Pansharpening Aerial and Satellite Images

Abstract

The growing demand for high-quality imaging data and the current technological limitations of imaging sensors require the development of techniques that combine data from different platforms in order to obtain comprehensive products for detailed studies of the environment. To meet the needs of modern remote sensing, the authors present an innovative methodology of combining multispectral aerial and satellite imagery. The methodology is based on the simulation of a new spectral band with a high spatial resolution which, when used in the pansharpening process, yields an enhanced image with a higher spectral quality compared to the original panchromatic band. This is important because spectral quality determines the further processing of the image, including segmentation and classification. The article presents a methodology of simulating new high-spatial-resolution images taking into account the spectral characteristics of the photographed types of land cover. The article focuses on natural objects such as forests, meadows, or bare soils. Aerial panchromatic and multispectral images acquired with a digital mapping camera (DMC) II 230 and satellite multispectral images acquired with the S2A sensor of the Sentinel-2 satellite were used in the study. Cloudless data with a minimal time shift were obtained. Spectral quality analysis of the generated enhanced images was performed using a method known as “consistency” or “Wald’s protocol first property”. The resulting spectral quality values clearly indicate less spectral distortion of the images enhanced by the new methodology compared to using a traditional approach to the pansharpening process.

Accelerating Crop Yield: Multisensor Data Fusion and Machine Learning for Agriculture Text Classification

“…Although the outcome was positive for assessing and monitoring rice production, it was only on a moderate scale, and more geographic region-based forecasting of production remains a challenge for food security solutions. Due to sensor imaging limitations, Siok [13] highlighted environmental studies and the need for techniques that combine data from different platforms. The authors combined multispectral aerial and satellite imagery to create a more spectrally accurate image.…”

Enhancing Uav Coastal Mapping Using Infrared Pansharpening

“…Pansharpening methods allow to add spectral bands to images that are devoid of them and thus, increase the invisible spectrum. Often used for satellite images, pansharpening is increasingly used for the fusion of manned aerial and satellite images (Siok et al, 2020). Fusion at centimetric scale can also be applied between satellite or manned aerial and UAV images (Jenerowicz et al, 2017).…”

Fusion of different multispectral band combinations of Sentinel-2A with UAV imagery for crop classification

“…16,17 Limited research is being done in transforming multispectral information from satellite and aerial imagery onto high spatial panchromatic images for crop classification. 5,18,19 Various algorithms have been developed over the years for image fusion, such as principal component analysis (PCA), Brovey transformation (BT), intensity-hue-saturation (IHS), and Gram-Schmidt (GS) transformations. [19][20][21][22][23][24] The PCA method preserves the spectral information by enhancing the image quality and also reduces the redundancy.…”