Enhancement of Satellite Images by Smoothing and Sharpening Filters

Abstract

Satellite imagery plays a crucial role in a wide range of applications, including environmental monitoring, urban planning, and disaster management. However, raw satellite images often suffer from various distortions such as noise, low contrast, and blurring, which can reduce their interpretability and effectiveness. This dissertation explores the enhancement of satellite images using both smoothing and sharpening filters to improve their visual quality and feature clarity. In the first phase, smoothing filters such as mean, Gaussian, median, and bilateral filters were applied to reduce noise and improve image uniformity. Each filter\'s performance was evaluated using metrics including entropy, Peak Signal-to-Noise Ratio (PSNR), and Edge Preservation Index (EPI). The bilateral filter demonstrated superior noise reduction while preserving important image edges. In the second phase, sharpening filters such as Laplacian, unsharp masking, and high-pass filtering were implemented to enhance fine details and edges in the images. Performance metrics were again used to assess the impact of each method. Visual results and quantitative analysis revealed that sharpening significantly improved feature visibility after smoothing. The experimental results confirm that a hybrid approach—combining appropriate smoothing followed by sharpening—yields enhanced satellite images with better clarity, noise suppression, and edge definition. This methodology contributes to more accurate analysis and interpretation of satellite data in practical applications.

Introduction

Satellite Imagery involves capturing images of Earth or other planets using satellites equipped with sensors that detect multiple spectral bands such as visible, infrared, and radar. These images are transmitted, processed, and used for a variety of analytical and operational purposes.

Importance & Advantages:

• Provides wide-area, repetitive, near real-time global observations.

• Covers remote or inaccessible regions.

• Offers multispectral data beyond human vision.

• Cost-effective compared to ground surveys.

Applications include:

• Environmental monitoring (deforestation, pollution, glaciers).

• Urban planning and infrastructure development.

• Agriculture (crop health, yield estimation).

• Disaster management (floods, wildfires, hurricanes).

• Climate and weather forecasting.

• Military surveillance.

• Geological and mining exploration.

Image Enhancement Techniques:

• Spatial Domain: Manipulate pixels directly.

• Frequency Domain: Manipulate frequency components (e.g., via Fourier Transform).

Types of Filters:

• Smoothing Filters: Reduce noise and blur images (e.g., Mean, Gaussian, Median, Bilateral).

• Sharpening Filters: Enhance edges and details (e.g., Laplacian, Unsharp Mask, High-Boost).

Role of Image Processing:
Enhances satellite image clarity, reduces noise, detects edges/features, aids preprocessing, and improves data quality for analysis.

Literature Review Highlights:

• Mean filter blurs edges, Gaussian preserves edges better, Median is effective for salt-and-pepper noise.

• Bilateral filtering excels in noise reduction while preserving edges.

• Sharpening filters improve contrast but can amplify noise; Unsharp Mask and High-Boost are effective, Laplacian less so.

• Hybrid approaches combining smoothing and sharpening (e.g., Bilateral + Unsharp Mask) yield superior image quality.

Performance Metrics:

• Edge Preservation Index (EPI): Measures how well edges are retained.

• Entropy: Reflects image information content.

• Mean Squared Error (MSE): Measures error relative to original.

• Peak Signal-to-Noise Ratio (PSNR): Quantifies image quality.

Key Results:

• Bilateral filter is best among smoothing filters (high EPI and PSNR).

• High-Boost filter outperforms other sharpening filters.

• Laplacian filter performs poorly with distortion.

• Combined Bilateral + Unsharp Mask hybrid filter balances noise reduction and edge enhancement, achieving the best overall performance across all metrics.

Conclusion

The enhancement of satellite images is a crucial step in improving the interpretability, accuracy, and usability of remote sensing data for applications such as urban planning, environmental monitoring, disaster assessment, and land cover classification. This study focused on enhancing satellite imagery through the implementation and evaluation of various smoothing and sharpening filters, with the aim of improving image clarity while preserving critical structural and textural information.

References

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Copyright

Copyright © 2025 Osho Chauhan, Dr. Jawahar Thakur. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.