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Author

Junhwa Chi

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Open access Jul 2026

Deep learning–based enhancement of feature tracking for sea ice drift estimation

This work proposes an SID estimation framework that replaces the conventional ORB with deep learning-based methods such as SuperGlue and Local Feature TRansformer (LoFTR), and demonstrates that deep learning-based methods can effectively replace the conventional ORB approach for SID estimation in the Arctic environment...

Ki-Yeong Mun, Junhwa Chi · 0 citations
Open access Jul 2026

Deep Learning Benchmarks for short-term Arctic Sea Ice Forecasting

This study benchmarks CNN backbone and Transformer backbone families for 10-day sea ice forecasting and indicates that CNN-based models, including PoolFormer, generally show more favorable performance in boundary preservation and prediction stability than Transformer-based models.

Bo-Ram Kim, Junhwa Chi · 0 citations
Open access Jul 2026

A Dynamically Weighted Framework for Adaptive Reference-Based Super-Resolution

Abstract. Satellite remote sensing is inherently constrained by a trade-off between spatial and temporal resolution. As a result, high-temporal-frequency sensors such as Geostationary Ocean Color Imager-II provide operationally valuable observations but at coarse spatial resolution. Reference-Based Super-Resolution (Re...

Chae-Eun Kim, Junhwa Chi · 0 citations
Open access Jul 2026

Stepwise Optimization and Ensemble Pipeline for Building Change Detection in High Resolution Satellite Imagery Using Mamba-Based Model

A systematic stepwise optimization pipeline for building change detection in dense urban environments using high-resolution CAS500-1 satellite imagery is proposed and establishes a practical foundation for future ensemble-based architectures.

DongHyuk Jin, Junhwa Chi · 0 citations

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