Work

Validation of Map Production through Deep Learning-based Satellite Image Super-Resolution

Year: 2025

Type: article

Abstract: In recent years, significant efforts have been made to address the cost and time challenges associated with satellite-based remote sensing in both civilian and defense sectors by using deep learning-based super-resolution models to enhance satellite imagery. This paper designs and trains a deep learning-based super-resolution model for satellite images. Utilizing high-resolution satellite images generated from the super-resolution model, we produced maps for RPC error correction, point cloud generation, and DSM creation. We validated the effectiveness of these maps by comparing them with maps produced from original satellite images for both outcome and accuracy. Significant results were achieved in RPC error correction, point cloud generation, and DSM creation. Despite increasing the resolution with the super-resolution model, accuracy was either improved or maintained, confirming its validity for map production.

Source: Journal of the Korea Institute of Military Science and Technology

DOI: 10.9766/kimst.2025.28.2.155

Authors: Jaeil Park, Sangheon Shin, Songyun Kwak, Youngchan Jang, Jinhyeok Park

Language: English

Topic: Satellite Image Processing and Photogrammetry

Subfield: Ocean Engineering

Field: Engineering

Domain: Physical Sciences

Open Access status: diamond

Funders: Korea Research Institute for Defense Technology Planning and Advancement, Defense Acquisition Program Administration

Award: G943278048

OpenAlex ID: W4409202654

FWCI:

Cites: 16

Cited by:

Related to: 10