Yazarlar

• Atasoy, M.(1)
• Bayrak, T.(1)
• Ekercin, S.(1)
• Erduran, M.(1)
• Günen, Mehmet Akif(1)
• Nisanci, R.(1)
• Reis, S.(1)
• Sancar, C.(1)
• Yalcin, A.(1)

• MakaleAçık Erişim2026/r/gumushane/13676
  Modified very deep super resolution network-Gaussian mixture models based single image super-resolution: A comprehensive comparative analysis

  Günen, Mehmet Akif

  Image Super-Resolution (SISR) is a vital, cost-effective solution for enhancing the spatial resolution of satellite imagery. However, a significant challenge in current SISR techniques is that while deep learning models excel at structural learning, they often struggle to accurately reconstruct intricate high-frequency details and preserve the statistical texture properties of high-resolution images. To address this gap, this paper proposes a novel hybrid method, termed mVDSR-GMM, which leverages the complementary strengths of Gaussian Mixture Models (GMM) and a modified Very Deep Super-Resolution (mVDSR) network. GMM is utilized to model the underlying statistical and probabilistic properties of the image for accurate high-frequency reconstruction while the mVDSR network enhanced with 25 convolutional layers and Leaky ReLU activations boosts the model’s structural feature extraction and non-linear mapping capabilities. This hybrid approach technically integrates the mVDSR and GMM through a parallel merging of their estimated residuals within the luminance channel, thereby enhancing the spatial reconstruction of satellite imagery. The enhanced mVDSR network, with increased depth and Leaky ReLU activation functions, further boosts the model's learning capability and robustness. Through extensive evaluations using satellite imagery datasets and metrics such as PSNR, SSIM, BRISQUE, MSE, UIQI, SAM, and FD, mVDSR-GMM consistently outperformed existing techniques, demonstrating its effectiveness in improving spatial resolution. Experimental results indicate that mVDSR-GMM outperforms main competitors such as SwinIR and SinSR across all evaluation metrics, providing a robust reconstruction performance that is further validated by improved accuracy in the binary separation of urban structures (buildings and roads) from natural environments. The proposed mVDSR-GMM emerges as a promising tool for advancing satellite imagery resolution, with potential applications in remote sensing and geospatial analysis.

  İncele
• MakaleYalnız künye2012/r/gumushane/13206
  Remote sensing and GIS-based landslide susceptibility mapping using frequency ratio and analytical hierarchy methods in Rize province (NE Turkey)

  Reis, S.; Yalcin, A.; Atasoy, M.; Nisanci, R.; Bayrak, T.; Erduran, M.; Sancar, C.; Ekercin, S.

  The northeast part of Turkey is prone to landslides because of the climatic conditions, as well as geologic and geomorphologic characteristics of the region. Especially, frequent landslides in the Rize province often result in significant damage to people and property. Therefore, in order to mitigate the damage from landslides and help the planners in selecting suitable locations for implementing development projects, especially in large areas, it is necessary to scientifically assess susceptible areas. In this study, the frequency ratio method and the analytical hierarchy process (AHP) were used to produce susceptibility maps. Especially, AHP gives best results because of allowing better structuring of various components, including both objective and subjective aspects and comparing them by a logical and thorough method, which involves a matrix-based pairwise comparison of the contribution of different factors for landslide. For this purpose, lithology, slope angle, slope aspect, land cover, distance to stream, drainage density, and distance to road were considered as landslide causal factors for the study area. The processing of multi-geodata sets was carried out in a raster GIS environment. Lithology was derived from the geological database and additional field studies; slope angle, slope aspect, distance to stream, distance to road and drainage density were invented from digital elevation models; land cover was produced from remote sensing imagery. In the end of study, the results of the analysis were verified using actual landslide location data. The validation results showed satisfactory agreement between the susceptibility map and the existing data on landslide locations.

  İncele