1 citations · 1 across the 8 of their papers we have counts for
8 papers
ELMZip: Onboard Satellite Image Compression via Extreme Learning Machines for Efficient Downlink
Woojin Cho, Junghwan Park, Sangcheol Sim +3
The acquisition of multispectral imagery via small satellites (e.g., CubeSats) presents significant data downlink challenges due to high data volumes and restricted communication w…
Few-Shot Open-Vocabulary Remote Sensing Segmentation via Textual Inversion
Junhyuk Heo, Junghwan Park
Open-vocabulary segmentation labels arbitrary categories from a text query without per-class training, yet on remote sensing imagery it underperforms on categories it handles relia…
FLAME: Physics-Guided Neural Operators for Onboard Satellite Methane Detection in Hyperspectral Imagery
Junhyuk Heo, Junghwan Park, Sangcheol Sim +2
Methane is a major driver of near-term climate change, and rapidly identifying its emission sources is a critical climate intervention. Spaceborne hyperspectral imagery is the prim…
Self-Supervised Score-Based Despeckling for SAR Imagery via Log-Domain Transformation
Junhyuk Heo
The speckle noise inherent in Synthetic Aperture Radar (SAR) imagery significantly degrades image quality and complicates subsequent analysis. Given that SAR speckle is multiplicat…
MANGO: A Global Single-Date Paired Dataset for Mangrove Segmentation
Junhyuk Heo, Beomkyu Choi, Hyunjin Shin +1
Mangroves are critical for climate-change mitigation, requiring reliable monitoring for effective conservation. While deep learning has emerged as a powerful tool for mangrove dete…
Basis-Oriented Low-rank Transfer for Few-Shot and Test-Time Adaptation
Junghwan Park, Woojin Cho, Junhyuk Heo +2
Adapting large pre-trained models to unseen tasks under tight data and compute budgets remains challenging. Meta-learning approaches explicitly learn good initializations, but they…
Fourier-Modulated Implicit Neural Representation for Multispectral Satellite Image Compression
Woojin Cho, Steve Andreas Immanuel, Junhyuk Heo +1
Multispectral satellite images play a vital role in agriculture, fisheries, and environmental monitoring. However, their high dimensionality, large data volumes, and diverse spatia…
Tackling Few-Shot Segmentation in Remote Sensing via Inpainting Diffusion Model
Steve Andreas Immanuel, Woojin Cho, Junhyuk Heo +1
Limited data is a common problem in remote sensing due to the high cost of obtaining annotated samples. In the few-shot segmentation task, models are typically trained on base clas…