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Methodological approach for fast high-resolution image selection: FAHRIS algorithm.

Bjørnar Åsebø¹, Stefano Cavazzani^1,2,3, Chiara Bertolin¹

¹Department of Mechanical and Industrial Engineering, Norwegian University of Science and Technology, Trondheim, Norway.

|December 24, 2024

Summary

This study introduces a new system for stabilizing aerial images and a fast algorithm (FAHRIS) for processing them. These advancements aim to improve the collection of Artificial Light at Night (ALAN) data from stratospheric balloons and drones.

Keywords:

Atmospheric effects Fast Algorithm for High-Resolution Image Selection - FAHRIS Image analysis Image classification Image grouping Image stitching Light pollution Radiosondes Site testing Unmanned aerial vehicles

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Area of Science:

Earth and Space Sciences
Computer Science
Engineering

Background:

Collecting Artificial Light at Night (ALAN) data via aerial platforms necessitates robust image stabilization.
Existing methods for processing ALAN imagery lack efficiency and speed, particularly for high-resolution datasets.

Purpose of the Study:

To propose a design for a high-resolution image acquisition and stabilization system for aerial instruments.
To develop and validate a novel Fast Algorithm for High-Resolution Image Selection (FAHRIS) for efficient image processing.
To address limitations in current ALAN data collection and processing.

Main Methods:

A design concept for an aerial image stabilization system was developed.
A novel algorithm, FAHRIS, was created for rapid image selection, grouping, and stitching.
The FAHRIS algorithm was validated using data from stratospheric balloon and drone flights over Italy.

Main Results:

The proposed stabilization system design addresses the need for enhanced in-flight stability.
FAHRIS demonstrated effectiveness in rapid image selection and grouping.
Image stitching results revealed potential distortions, particularly with significant relative angles between images.

Conclusions:

The developed FAHRIS algorithm offers a significant advancement in processing aerial imagery for ALAN studies.
The proposed stabilization system design provides a foundation for future aerial data collection platforms.
Further research is needed to mitigate stitching distortions and optimize computational efficiency.