Aug 2026· Sustainable Engineering and Innovation· Vol 8, pp. 39-48· 0 citations· 26 references
TL;DR
Unlike the existing GLCM and LBP approaches, which do not work correctly in the presence of noise, the presented one successfully eliminates bias in the area distribution caused by noise.
Abstract
Classifying satellite images is complicated by the presence of Gaussian noise, which corrupts pixel relations and decreases the quality of the input data. In this research, an advanced hybrid statistical technique to classify satellite images into three classes: Urban, Vegetation, and Water is developed. The presented technique uses a two-step processing chain: the first step applies a median filter to reduce the effect of Gaussian noise; the next step combines the benefits of GLCM and LBP features for accurate texture classification. Testing of the developed algorithm was carried out using Sentinel-2 satellite images of Barcelona for different values of noise (from 0% to 20%). Experimental results show that the presented algorithm provides a classification accuracy of 98.55%. Unlike the existing GLCM and LBP approaches, which do not work correctly in the presence of noise, the presented one successfully eliminates bias in the area distribution caused by noise.
Cloud cover can substantially restrict the information available in optical satellite images, affecting applications including agricultural monitoring, disaster assessment, and urban planning. Although deep-learning and multi-temporal techniques have improved cloud mitigation, they commonly depend on considerable compu...
Multispectral camera arrays capture image data in various spectral bands, enabling image acquisition beyond human perception. These systems are widely used in medical, agricultural, environmental, and remote sensing applications. However, not all recorded bands are needed for classification tasks, thus reducing them ca...
Katja Kossira, Jürgen Seiler, André Kaup· International Conference on...· 0 citations
Multispectral satellite imagery provides rich information about the Earth's surface. However, strong correlations among spectral bands often introduce redundancy and increase computational complexity. In this study, high-resolution WorldView-3 imagery is used to analyze spectral band relationships and classify differen...
V. Meskova, Marcos Bosques-Perez, M. Cabrerizo et al.· International Conference on...· 0 citations
Automatic classification of military aircraft in satellite imagery is a challenging problem with a high risk of error due to the limited pixel area of targets, variations in image resolution and illumination conditions, background complexity, and strong visual similarity among classes. In this study, a deep learning ap...
Drone images are an important source of information for agriculture, green area monitoring, urban infrastructure analysis, and land cover object detection. However, real drone images may contain uneven illumination, shadows, sensor noise, texture variations, and blurred object boundaries, which reduce classification ac...
Ravshan Shirinboyev, D. Khasanov, K. Ibragimov· 2026 International Russian A...· 0 citations
This research proposes Adaptive Local Ternary Pattern (ALTP) that uses a dynamically calculated threshold to determine texture pattern for an image region that outperformed state-of-the-art texture feature descriptors.
