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Application of Spectral Angle Mapper Classification to Discriminate Hydrothermal Alteration in Southwest Birjand, Iran, Using Advanced Spaceborne Thermal Emission and Reflection Radiometer Image Processing 认领 引用 被引量：5
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作者 Maryam ABDI Mohammd H. KARIMPOUR 《Acta Geologica Sinica(English Edition)》 SCIE CAS CSCD 2012年第5期1289-1296,共8页
The purpose of this study is to evaluate the Spectral Angle Mapper （SAM） classification method for determining the optimum threshold （maximum spectral angle） to unveil the hydrothermal mineral assemblages related ... The purpose of this study is to evaluate the Spectral Angle Mapper （SAM） classification method for determining the optimum threshold （maximum spectral angle） to unveil the hydrothermal mineral assemblages related to mineral deposits. The study area indicates good potential for Cu-Au porphyry, epithermal gold deposits and hydrothermal alteration well developed in arid and semiarid climates, which makes this region significant for Advanced Spaceborne Thermal Emission and Reflection Radiometer （ASTER） image processing analysis. Given that achieving an acceptable mineral mapping requires knowing the alteration patterns, petrochemistry and petrogenesis of the igneous rocks while considering the effect of weathering, overprinting of supergene alteration, overprinting of hypogene alteration and host rock spectral mixing, SAM classification was implemented for argillic, sericitic, propylitic, alunitization, silicification and iron oxide zones of six previously known mineral deposits： Maherabad, a Cu-Au porphyry system; Sheikhabad, an upper part of Cu-Au porphyry system; Khoonik, an Intrusion related Au system; Barmazid, a low sulfidation epithermal system; Khopik, a Cu-Au porphyry system; and Hanish, an epithermal Au system. Thus, the investigation showed that although the whole alteration zones are affected by mixing, it is also possible to produce a favorable hydrothermal mineral map by such complementary data as petrology, petrochemistry and alteration patterns. 展开更多
关键词 hydrothermal alteration Spectral Angle Mapper Advanced Spaceborne Thermal Emission and Reflection Radiometer image process Iran
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A lightweight four-channel multi-modal model to improve computational performance of automated fire detection 认领 引用
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作者 Jingshuo Yu Qian Chen 《Journal of Safety Science and Resilience》 EI CSCD 2026年第2期358-368,共11页
The urgent need for advanced fire detection methods stems from the increased intensity of fire incidents,which cause massive property loss and irreversible damage.To overcome the limitations of traditional fire detect... The urgent need for advanced fire detection methods stems from the increased intensity of fire incidents,which cause massive property loss and irreversible damage.To overcome the limitations of traditional fire detection methods,such as those of smoke detectors,fire detection based on computer vision(CV)algorithms has been adopted to improve detection accuracy.Compared to single-modal fire detection,multi-modal fire detection has gained attention because it leverages the richer information present in both RGB and thermal images.However,prevalent multi-modal fire detection methods significantly increase model complexity by requiring two separate streams in the backbone to process RGB and thermal images independently.To address this issue,this paper proposes a four-channel single-stream fire detection method based on YOLOv5,which concatenates RGB and thermal images to form the required four-channel input.Comparison experiments with dual-stream YOLOv5 models using add fusion and transformer fusion demonstrate that the four-channel single-stream model reduces model complexity while improving detection accuracy.To further enhance detection accuracy and reduce model complexity,this study redesigned YOLOv5’s C3 module by integrating the convolutional block attention module(CBAM)to form the C3CBAM module and introduced the SCYLLA-Intersection over Union(SIoU)loss function.By comparing its performance with that of state-of-the-art(SOTA)models in multi-modal object detection,such as the YOLOv5-based dual-stream model,this study shows that the proposed approach improves detection in the diverse conditions presented in the selected dataset. 展开更多
关键词 Multi-modal fire detection RGB-T detection Lightweight Deep learning Thermal image processing
Thermography analyses of rock fracture due to excavation and overloading for tunnel in 30° inclined strata 认领 引用 被引量：3
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作者 SUN XiaoMing XU HuiChen +2 位作者 HE ManChao GONG WeiLi CHEN Feng 《Science China(Technological Sciences)》 SCIE EI CAS CSCD 2017年第6期911-923,共13页
Large-scale physical model test of 30°inclined strata was conducted to investigate the damage mechanisms during the excavation and overloading using infrared detection.The experiment results were presented with t... Large-scale physical model test of 30°inclined strata was conducted to investigate the damage mechanisms during the excavation and overloading using infrared detection.The experiment results were presented with thermal images which were divided into three stages including a full face excavation stage,a staged excavation stage,and an overloading stage.The obtained results were compared with the previously reported results from horizontal,45?,60?,and vertical strata models.Infrared temperature(IRT)for 30°inclined strata model descended with multiple fluctuations during the full-face excavation.For the staged excavation,the excavation damage zone(EDZ)showed enhanced faulting-like strips as compared in the 45?,60?,and vertical models,indicating the intensified stress redistribution occurred in the adjacent rock mass.In contrast,EDZ for the horizontal strata existed in a plastic-formed manner.During the overloading,abnormal features in the thermal images were observed preceding the coalescence of the propagating cracks.The ultimate failure of the model was due primarily to the floor heave and the roof fall. 展开更多
关键词 deep tunnel inclined strata failure process large-scale physical model infrared thermal imaging technology
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