PERFORMANCE COMPARISON AND ANALYSIS OF NOISE REDUCTION METHODS ON GÖKTÜRK-2 SATELLITE IMAGES

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Tezin Türü: Yüksek Lisans

Tezin Yürütüldüğü Kurum: Hacettepe Üniversitesi, Mühendislik Fakültesi, Geomatik Mühendisliği Bölümü, Türkiye

Tezin Dili: İngilizce

Öğrenci: ŞULE TUNAHAN

Danışman: Mustafa Türker

Özet:

Optical satellite imagery represents a critical data source acquired from space, serving a multitude of application domains. However, these images often suffer from various types of noise that can significantly impair their quality. Noise in this context refers to the random and undesired fluctuations in image intensity, which may emerge during the processes of image acquisition, conversion, transmission, and processing. Noise can reduce image quality and lead to erroneous conclusions during analysis.

Consequently, it is of paramount importance to assess the efficacy of noise reduction techniques in satellite images to enhance image quality and ensure more accurate analyses. This study aims to eliminate thermal noise present in optical satellite images, determine appropriate noise reduction methods, and compare these methods using specific metrics. The noise reduction techniques employed encompass spatial domain filtering, transform domain filtering, variational denoising methods, and deep learning-based methods. The effectiveness and performance of these methods are evaluated based on several metrics. The tests were carried out on high spatial resolution Göktürk-2 satellite imagery. In addition, the images acquired from the CalVal (Calibration and Validation) site and SPOT-5 satellite imagery were also used in the experimental tests.

Based on the results, each denoising algorithm has distinct strengths and weaknesses, excelling in different aspects of image restoration. For preserving structural accuracy and fine details Expected Path Log-Likelihood (EPLL), Denoising CNN (DnCNN), and Fast and Flexible Denoising Network (FFDNet) are well-suited. Weighted Nuclear Norm Minimization (WNNM) and Nonlocally Centralized Sparse Representation (NCSR) offer a balanced performance, effectively reducing noise while maintaining structural fidelity. The Wavelet and Bilateral Filter methods are useful for enhancing edge contrast but should be applied cautiously due to their potential to introduce artifacts. These findings emphasize the importance of selecting an algorithm based on the specific requirements and desired outcomes of the task.