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Adaptive Remote Sensing Image Enhancement for KOMPSAT Imagery.

Giwoong Lee¹, Jingi Ju¹, Minwoo Kim¹

¹IOPS Co., Ltd., Daejeon 35223, Republic of Korea.

Sensors (Basel, Switzerland)

|March 14, 2026

Summary

This summary is machine-generated.

Adaptive Remote Sensing Image Enhancement (ARSIE) uses reinforcement learning to automatically improve satellite image quality for better segmentation. This automated framework enhances KOMPSAT imagery, overcoming common degradation issues.

Keywords:

image denoising image segmentation reinforcement learning satellite imagery

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Area of Science:

Remote Sensing
Computer Vision
Artificial Intelligence

Background:

Satellite imagery, such as KOMPSAT, faces quality degradation from atmospheric effects, low illumination, and viewing angles.
Manual image enhancement is labor-intensive and inconsistent, hindering accurate deep learning-based segmentation.
Degraded imagery significantly reduces the performance of deep learning models for image segmentation tasks.

Purpose of the Study:

To develop an automated framework for enhancing degraded remote sensing imagery.
To improve the segmentation performance of deep learning models on challenging satellite data.
To introduce a reinforcement learning-based approach for adaptive image enhancement.

Main Methods:

Proposed Adaptive Remote Sensing Image Enhancement (ARSIE), a reinforcement learning-based framework.
ARSIE learns image-specific enhancement sequences from a filter pool using a policy network.
The policy network leverages intermediate feature maps from a segmentation model to guide enhancement decisions.

Main Results:

ARSIE automatically discovers effective, image-specific enhancement combinations.
Consistent improvements in segmentation accuracy were observed on degraded KOMPSAT imagery.
The framework demonstrated the ability to enhance image quality for downstream tasks.

Conclusions:

ARSIE offers an automated and effective solution for enhancing degraded remote sensing images.
The reinforcement learning approach directly optimizes for improved segmentation performance.
ARSIE shows potential for broader application in improving various types of satellite imagery quality.