Lithology Identification of UAV Oblique Photography Images Based on Semantic Segmentation Neural Network Algorithm

Luo, Siyu; Yin, Senlin; Chen, Juan; Wu, Youxin; Chen, Xu

doi:10.1007/s11004-023-10108-3

Cited by 2 publications

(2 citation statements)

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“…As shown in Figure 3, the study area is located in the Karamay Hinterland of West Junggar, belonging to the southwestern part of the Central Asian Orogenic Belt, with the geographical coordinates of 84 • 26 ′ 35 ′′ to 85 • 17 ′ 30 ′′ E and 45 • 37 ′ 06 ′′ to 46 • 1 ′ 08 ′′ N, covering an area of about 2900 km 2 . It experiences a temperate continental climate characterized by low rainfall and arid conditions throughout the year.…”

Section: Study Areamentioning

confidence: 99%

“…Additionally, granite can also serve as a crucial raw material in the fields of construction and manufacturing. The traditional identification of granite relies on field exploration; however, remote sensing technology, with its wide coverage and dynamic monitoring capabilities, greatly reduces the workload in the field [ 2 ]. Therefore, the extraction of granite information from suitable remote sensing images is of significant importance for mineral resource exploration, geological evolution studies, and geological surveys.…”

Section: Introductionmentioning

confidence: 99%

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Granite Extraction Based on the SDGSAT-1 Satellite Thermal Infrared Spectrometer Imagery

Yuan,

Wang,

Yang

et al. 2024

Sensors

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Earth observation by remote sensing plays a crucial role in granite extraction, and many current studies use thermal infrared data from sensors such as ASTER. The challenge lies in the low spatial resolution of these satellites, hindering precise rock type identification. A breakthrough emerges with the Thermal Infrared Spectrometer (TIS) on the Sustainable Development Science Satellite 1 (SDGSAT-1) launched by the Chinese Academy of Sciences. With an exceptional 30 m spatial resolution, SDGSAT-1 TIS opens avenues for accurate granite extraction using remote sensing. This study, exemplified in Xinjiang’s Karamay region, introduces the BR-ISauvola method, leveraging SDGSAT-1 TIS data. The approach combines band ratio with adaptive k-value selection using local grayscale statistical features for Sauvola thresholding. Focused on large-scale granite extraction, results show F1 scores above 70% for Otsu, Sauvola, and BR-ISauvola. Notably, BR-ISauvola achieves the highest accuracy at 82.11%, surpassing Otsu and Sauvola by 9.62% and 0.34%, respectively. This underscores the potential of SDGSAT-1 TIS data as a valuable resource for granite extraction. The proposed method efficiently utilizes spectral information, presenting a novel approach for rapid granite extraction using remote sensing TIS imagery, even in scenarios with low spectral resolution and a single data source.

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Section: Study Areamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%