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A transfer learning framework for the real-time detection of atmospheric gravity waves from All-Sky Airglow Imager 认领 引用 被引量：1
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作者 YuBin He QingChen Xu +6 位作者 YaJun Zhu QinZeng Li Cui Tu Bing Cai Wei Yuan XinYing Wang Feng Wei 《Earth and Planetary Physics》 EI CSCD 2026年第3期454-462,共9页
Atmospheric gravity waves(AGWs)observed by the All-Sky Airglow Imager(ASAI)require accurate identification for the study of atmospheric coupling mechanisms and space weather prediction.However,the traditional manual s... Atmospheric gravity waves(AGWs)observed by the All-Sky Airglow Imager(ASAI)require accurate identification for the study of atmospheric coupling mechanisms and space weather prediction.However,the traditional manual screening methods and existing machine learning approaches do not meet the demands of practical station monitoring,which has significantly impeded climatological statistical research based on AGWs.Therefore,a real-time detection framework for ground-based airglow gravity waves that integrates transfer learning with adaptive image preprocessing has been proposed.By employing wavelength-adaptive median filtering and multiscale fusion,the framework effectively suppresses stellar noise while preserving weak gravity wave features.The model utilizes an EfficientNet-B3(convolutional neural network)backbone enhanced with a deformable convolutional layer,trained via a two-stage strategy:A frozen phase prevents overfitting by locking the lower level feature extractor,and a fine-tuning phase optimizes the deformable convolution through cosine annealing and layered optimization.This approach improves both feature transfer efficiency and gravity wave detection sensitivity.The resulting lightweight model achieves 91.2%accuracy with millisecond-level inference speed(23 ms per frame). 展开更多
关键词 atmospheric gravity waves All-Sky Airglow Imager real-time detection multiscale fusion transfer learning EfficientNet-B3 deformable convolution
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Mock Observations for the CSST Mission:Multi-Channel Imager——Instrument Simulation 认领 引用 被引量：1
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作者 Zhao-Jun Yan Huan-Yuan Shan +40 位作者 Zhen-Ya Zheng Xi-Yan Peng Zhao-Xiang Qi Chun Xu Lin Lin Xin-Rong Wen Chun-Yan Jiang Li-Xin Zheng Jing Zhong Fang-Ting Yuan Zhen-Lei Chen Wei Chen Mao-Chun Wu Zhen-Sen Fu Ke-Xin Li Lin Nie Chao Liu Nan Li Qiao Wang Zi-Huang Cao Shuai Feng Guo-Liang Li Lei Wang Cheng-Liang Wei Xiao-Bo Li Zhang Ban Xun Yang Yu-Xi Jiang De-Zi Liu Yong-He Chen Xiao-Hua Liu Fang Xu Xue Cheng Yue Su Tong-Fang Duan Chao Qi Na Li Geng Zheng Chong Ma Jing Tang Ran Li 《Research in Astronomy and Astrophysics》 SCIE CAS CSCD 2026年第2期78-97,共20页
The Chinese Space Station Survey Telescope(CSST),a two-meter aperture astronomical space telescope under China's manned space program,is equipped with multiple back-end scientific instruments.As an astronomical pr... The Chinese Space Station Survey Telescope(CSST),a two-meter aperture astronomical space telescope under China's manned space program,is equipped with multiple back-end scientific instruments.As an astronomical precision measurement module of the CSST,the Multi-Channel Imager(MCI)can cover a wide wavelength range from ultraviolet to near-infrared with three-color simultaneous high-precision photometry and imaging,which meets the scientific requirements for various fields.The diverse scientific objectives of MCI require not only a robust spaceborne platform,advanced optical systems,and observing facilities but also comprehensive software support for scientific operations and research.To this end,it is essential to develop realistic observational simulation software to thoroughly evaluate the MCI data stream and provide calibration tools for future scientific investigations.The MCI instrument simulation software will serve as a foundation for the development of the MCI data processing pipeline and will facilitate improvements in both hardware and software,as well as in the observational operation strategy,in alignment with the mission's scientific goals.In conclusion,we present a comprehensive overview of the MCI instrument simulation and some corresponding performances of the MCI data processing pipeline. 展开更多
关键词 methods numerical-telescopes-techniques image processing
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Low-subrate sparse reconstruction with threshold-adaptive denoising and basis learning for infrared aerial imagery 认领 引用
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作者 Maoji QIU Hao LIU 《Optoelectronics Letters》 EI 2026年第2期98-104,共7页
Due to the property of infrared aerial imagery, the local prior is sufficient especially for low-subrate block compressive sensing(BCS) reconstruction of infrared aerial images, while its complexity is much lower than... Due to the property of infrared aerial imagery, the local prior is sufficient especially for low-subrate block compressive sensing(BCS) reconstruction of infrared aerial images, while its complexity is much lower than nonlocal prior. The typical low-subrates can effectively improve the BCS transmission efficiency and reduce the burden of transmitter hardware. Therefore, this paper proposes a low-subrate sparse reconstruction algorithm with threshold-adaptive denoising and basis learning(TDBL), which adopts both split Bregman iteration(SBI) and adaptive threshold to implement the model-based BCS reconstruction for infrared aerial imagery. The experimental results show that as compared with the state-of-the-art algorithms, the proposed algorithm can obtain better recovery quality and less runtime on both HIT-UAV and M200-XT2 DroneVehicle datasets. the transmission efficiency and reduce the burden of transmitter hardware. The current NSS-guided reconstruction algorithms are trained and tested on natural image datasets by using relatively high subrates(more than 0.1). Due to significant difference in image contrast and pixel distribution between UAV infrared images and natural images, the performance of these algorithms on UAV infrared image datasets may be difficult to meet expectations. In recent years, the improvement of BCS recovery quality is not obvious with very high complexity, where the core step is to build a suitable dictionary, and then solve the associated sparsity of the dictionary. Previous BCS algorithms usually utilize the special iterative shrinkagehresholding(IST)[11] method to solve the l0 minimization problem. For BCS recovery quality and runtime of UAV infrared imagery, split Bregman iteration(SBI)[12] is a competitive mechanism, so we propose the low-subrate sparse reconstruction with threshold-adaptive denoising and basis learning(TDBL) algorithm under various low-subrate cases. By analyzing the UAV infrared imagery, it is concluded that infrared aerial images are usually characterized by large number of pixels on some gray levels with double or triple peaks on the histogram, and contain more low-frequency components on the Fourier magnitude spectrum. By jointly considering both recovery quality and runtime, we solve the above l0 minimization problem of BCS reconstruction by the SBI method, instead of IST. To obtain gains during different reconstruction phases, we design an adaptive threshold ρ which is related to model-based methods, such as K-singular value decomposition(SVD) sparse coding[13] and orthogonal matching pursuit(OMP) noise constraint[14]. According to the characteristics of UAV infrared images, an updating expression of ρ is designed by combining image variance and mean value. 展开更多
关键词 model based compressive sensing split bregman iteration transmitter hardware infrared aerial imagery basis learning infrared aerial images threshold adaptive denoising low subrate sparse reconstruction
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MCI:Multi-channel Imager on the Chinese Space Station Survey Telescope 认领 引用
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作者 Zhen-Ya Zheng Chun Xu +38 位作者 Xiaohua Liu Yong-He Chen Fang Xu Hu Zhan Xinfeng Li Lixin Zheng Huanyuan Shan Jing Zhong Zhaojun Yan Fang-Ting Yuan Chunyan Jiang Xiyan Peng Wei Chen Xue Cheng Zhen-Lei Chen Shuairu Zhu Lin Long Xin Zhang Yan Gong Li Shao Wei Wang Tianyi Zhang Guohao Ju Chenghao Li Wei Wang Zhiyuan Li Tao Wang Junfeng Wang Chengyuan Li Bin Ma Jianguo Wang Lei Wang Dezi Liu Nie Lin Kexin Li Xinrong Wen Maochun Wu Ruqiu Lin Xiang Ji 《Research in Astronomy and Astrophysics》 SCIE CAS CSCD 2026年第5期266-274,共9页
The Multi-Channel Imager(MCI)is a powerful near-ultraviolet(NUV)and visible imager onboard the Chinese Space Station Survey Telescope(CSST).The MCI provides three imaging channels,which are the NUV channel,the Blue ch... The Multi-Channel Imager(MCI)is a powerful near-ultraviolet(NUV)and visible imager onboard the Chinese Space Station Survey Telescope(CSST).The MCI provides three imaging channels,which are the NUV channel,the Blue channel and the Red channel,with the wavelength ranges of 255–430 nm,430–700 nm,and 700–1000 nm,respectively.MCI’s three channels can target the same field simultaneously,which is unique compared to other imagers onboard the Hubble Space Telescope(HST)or the James Webb Space Telescope(JWST).Each channel employs a CCD focal plane of 9216×9232 pixels and∼7.5×7.5 field of view(FOV),which are≳4 times the FOVs of HST imagers.The MCI’s three channels feature unprecedented sensitivities and FOVs,complementing the NUV and visible capabilities of the CSST for high-precision photometry and weaksignal detection,which would help build a new standard-star system and the deepest UV-Optical exposures for CSST.Rich filter sets of MCI would help explore other areas of science such as local emission line mapping,searching for high-z Lyαemitters,etc.Here we present key design features,results of current ground tests,and suggest observing strategies for the MCI. 展开更多
关键词 instrumentation photometers-space vehicles instruments-techniques photometric-techniques image processing
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A unified framework for small object analysis in UAV imagery:Tracking single target from multiple detected targets 认领 引用
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作者 Yanfang LIU Jiayu SHE +4 位作者 Yituo SONG Zhufei LIU Huarui XU Qiufan YUAN Naiming QI 《Chinese Journal of Aeronautics》 SCIE EI CAS CSCD 2026年第5期431-450,共20页
A framework for visual small target detection and tracking is introduced,leveraging Unmanned Aerial Vehicle(UAV)Remote Sensing Images(RSIs).The proposed Cropped Target Detection and Tracking(CTDT)framework comprises t... A framework for visual small target detection and tracking is introduced,leveraging Unmanned Aerial Vehicle(UAV)Remote Sensing Images(RSIs).The proposed Cropped Target Detection and Tracking(CTDT)framework comprises two integral stages:the detection stage and the tracking stage.During the detection stage,all targets can be identified from RSIs,providing a basis for the subsequent single-object tracking stage.Both stages are based on a cropping and random sampling strategy:the RSI is cropped into Small-Sized Images(SSIs),from which a random batch is constantly selected without repetition and fed into a network to locate the target until the target is discovered or all SSIs are used.This strategy improves the efficiency of detection and tracking.After cropping,the target may appear in multiple SSIs,and the target in each SSI may be incomplete.A Cropped Target Feature Extraction(CTFE)network is designed to detect and track the target by leveraging the information from small and incomplete targets in SSIs.CTFE achieves high precision and meets real-time requirements.The performance analysis of the detection network is also conducted in detail,and the results are instrumental in informing the design of the tracking network.By utilizing three UAV RSI datasets(UAVDT,UAV123,and DTB70),CTDT is compared to numerous state-of-the-art mainstream methods,such as PVT++,SiamBAN,SmallTrack,SiamAPN++,SiamIRCA,SiamFC,and CSK,to confirm its superiority and real-time performance.The results affirm that the proposed framework exhibits outstanding performance and adaptability to fast-moving targets,target loss,and camera failures,and holds promise for realtime applications.Additionally,real-world tests on a typical UAV platform demonstrate excellent performance and efficiency in a variety of UAV-specific tasks,as well as transferability for new missions. 展开更多
关键词 Convolutional neural networks Real-time application Remote sensing image Small target detection Small target tracking
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View factors estimation from street view imagery and its correlation with intra-urban thermal variation 认领 引用
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作者 Jiahui Chen Liyu Tang +2 位作者 Simin Liu Xueman Zuo Junqian Cao 《Geography and Sustainability》 CSCD 2026年第3期150-163,共14页
High temperatures negatively impact thermal comfort and public health.Understanding the relationship between urban morphology and thermal variation is crucial for mitigating excessive heat.In this study,we propose a f... High temperatures negatively impact thermal comfort and public health.Understanding the relationship between urban morphology and thermal variation is crucial for mitigating excessive heat.In this study,we propose a framework that uses the sky view factor(SVF),building view factor(BVF),and green view factor(GVF)to represent street-level spatial morphology,computed from Baidu street view images using semantic segmentation and fisheye transformation.The framework also integrates local climate zones(LCZs)classification with summer Landsat thermal data to explore the relationships between SVF,BVF,GVF,and intra-urban thermal variation.An empirical study is conducted in the core built-up area of Fuzhou.Key findings include:(1)Strong urban thermal intensity is characterized by high openness(SVFmean=0.60)and low vegetation(GVFmean=0.15).(2)In general,SVF(r=0.27)is positively associated with street thermal intensity,whereas BVF(r=-0.15)and GVF(r=-0.21)exhibit negative correlations,based on the entire study area.(3)Within LCZ classes(e.g.,LCZs 1-5),SVF(54.57%of the grids)is mostly positively correlated with temperature difference,while GVF(61.30%of the grids)is predominantly negatively correlated with temperature difference.(4)BVF shows the greatest uncertainty in its relationship with thermal variation,as it effectively quantifies exposure to heat-trapping building surfaces that dominate the local energy balance.The above findings imply that there is spatial heterogeneity in the relationship between view factors and intra-urban thermal variation.These results aid in developing street renewal strategies for urban heat mitigation. 展开更多
关键词 View factors Urban thermal environment Street view images(SVIs) Semantic segmentation Local climate zones(LCZs)
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Integration of Landsat and MODIS Imagery for Mapping 30-m Cotton Cultivation Areas in Xinjiang,China from 2000 to 2020 认领 引用
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作者 TAN Zhuting TAN Zhenyu +1 位作者 DUAN Hongtao ZHANG Kaili 《Chinese Geographical Science》 SCIE CAS CSCD 2026年第1期97-108,I0001,共12页
Cotton is an important global cash crops that serve as the primary source of natural fiber for textiles.A thorough understand-ing of the long-term variations in cotton cultivation is vital for optimizing cotton cultiv... Cotton is an important global cash crops that serve as the primary source of natural fiber for textiles.A thorough understand-ing of the long-term variations in cotton cultivation is vital for optimizing cotton cultivation management and promoting the sustainable development of the cotton industry.Xinjiang is the primary cotton-producing region in China.However,long-term data of cotton cultiv-ation areas with high spatial resolution are unavailable for Xinjiang,China.Therefore,this study aimed to identify and map an accurate 30-m cotton cultivation area dataset in Xinjiang from 2000 to 2020 by applying a Random Forest(RF)-based method that integrates Landsat and Moderate Resolution Imaging Spectroradiometer(MODIS)images,and validated the applicability and accuracy of dataset at a large spatial scale.Then,this study analyzed the spatiotemporal variations and influencing factors of cotton cultivation in the study period.The results showed that a high classification accuracy was achieved(overall accuracy>85%,F1>0.80),strongly agreeing with county-level agricultural statistical yearbook data(R2>0.72).Significant spatiotemporal variation in the cotton cultivation areas was found in Xinjiang,with a total increase of 1131.26 kha from 2000 to 2020.Notably,cotton cultivation area in southern Xinjiang expan-ded substantially,with that in Aksu increasing from 20.10%in 2000 to 28.17%in 2020,representing an expansion of 374.29 kha.In northern Xinjiang,the cotton areas in the Tacheng region also exhibited significant increased by almost ten percentage points in the same period.In contrast,cotton cultivation in eastern Xinjiang declined,decreasing from 2.22%in 2000 to merely 0.24%in 2020.Standard deviation ellipse analysis revealed a‘northeast-southwest’spatial distribution,with the centroid consistently located in Aksu and shifting 102.96 km over the 20-yr period.Pearson correlation analysis indicated that socioeconomic factors had a stronger influence on cotton cultivation than climatic factors,with effective irrigation area(r=0.963,P<0.05)and total agricultural machinery power(r=0.823)showing significant positive correlations,whereas climatic variables exhibiting weak associations(r<0.200).These results provide valuable scientific data for informed agricultural management,sustainable development,and policymaking. 展开更多
关键词 cotton cultivation mapping long-term series Landsat Moderate Resolution Imaging Spectroradiometer(MODIS) remote sensing Xinjiang,China
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Design of a compact wide-field-of-view infrared imager based on wavefront coding 认领 引用
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作者 Chonghui Zhu Jiaqian Yu Jingang Cui 《Chinese Physics B》 SCIE EI CAS CSCD 2026年第2期383-388,共6页
Compact size,high brightness,and wide field of view(FOV)are key requirements for long-wave infrared imagers used in military surveillance or night navigation.However,to meet the imaging requirements of high resolution... Compact size,high brightness,and wide field of view(FOV)are key requirements for long-wave infrared imagers used in military surveillance or night navigation.However,to meet the imaging requirements of high resolution and wide FOV,infrared optical systems often adopt complex optical lens groups,which will increase the size and weight of the optical system.In this paper,a strategy based on wavefront coding(WFC)is proposed to design a compact wide-FOV infrared imager.A cubic phase mask is inserted into the pupil plane of the infrared imager to correct the aberration.The simulated results show that,the WFC infrared imager has good imaging quality in a wide FOV of±16°.In addition,the WFC infrared imager achieves compactness with its 40 mm×40 mm×40 mm size.A fast focal ratio of 1 combined with an entrance pupil diameter of 25 mm ensures brightness.This work is of significance for designing a compact wide-FOV infrared imager. 展开更多
关键词 optical design infrared imager wavefront coding
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CCLNet:An End-to-End Lightweight Network for Small-Target Forest Fire Detection in UAV Imagery 认领 引用
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作者 Qian Yu Gui Zhang +4 位作者 Ying Wang Xin Wu Jiangshu Xiao Wenbing Kuang Juan Zhang 《Computers, Materials & Continua》 SCIE EI 2026年第3期1381-1400,共20页
Detecting small forest fire targets in unmanned aerial vehicle(UAV)images is difficult,as flames typically cover only a very limited portion of the visual scene.This study proposes Context-guided Compact Lightweight N... Detecting small forest fire targets in unmanned aerial vehicle(UAV)images is difficult,as flames typically cover only a very limited portion of the visual scene.This study proposes Context-guided Compact Lightweight Network(CCLNet),an end-to-end lightweight model designed to detect small forest fire targets while ensuring efficient inference on devices with constrained computational resources.CCLNet employs a three-stage network architecture.Its key components include three modules.C3F-Convolutional Gated Linear Unit(C3F-CGLU)performs selective local feature extraction while preserving fine-grained high-frequency flame details.Context-Guided Feature Fusion Module(CGFM)replaces plain concatenation with triplet-attention interactions to emphasize subtle flame patterns.Lightweight Shared Convolution with Separated Batch Normalization Detection(LSCSBD)reduces parameters through separated batch normalization while maintaining scale-specific statistics.We build TF-11K,an 11,139-image dataset combining 9139 self-collected UAV images from subtropical forests and 2000 re-annotated frames from the FLAME dataset.On TF-11K,CCLNet attains 85.8%mAP@0.5,45.5%mean Average Precision(mAP)@[0.5:0.95],87.4%precision,and 79.1%recall with 2.21 M parameters and 5.7 Giga Floating-point Operations Per Second(GFLOPs).The ablation study confirms that each module contributes to both accuracy and efficiency.Cross-dataset evaluation on DFS yields 77.5%mAP@0.5 and 42.3%mAP@[0.5:0.95],indicating good generalization to unseen scenes.These results suggest that CCLNet offers a practical balance between accuracy and speed for small-target forest fire monitoring with UAVs. 展开更多
关键词 Forest fire detection lightweight convolutional neural network UAV images small-target detection CCLNet
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Multiscale Long-Distance Feature Aggregation Network for Geospatial Semantic Segmentation in High-Resolution Remote Sensing Imagery 认领 引用
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作者 Guangyu Xu Yuxi Ban +3 位作者 Legend Zhang Junmin Lyu Feng Bao Wenfeng Zheng 《Computer Modeling in Engineering & Sciences》 SCIE EI 2026年第7期935-957,共23页
High-resolution remote sensing semantic segmentation is a fundamental task in Geospatial Artificial Intelligence(GeoAI).Existing CNN-based methods are effective for local and multiscale feature extraction but often la... High-resolution remote sensing semantic segmentation is a fundamental task in Geospatial Artificial Intelligence(GeoAI).Existing CNN-based methods are effective for local and multiscale feature extraction but often lack progressive cross-scale semantic propagation,while attention-and Transformer-based methods improve global spatial modeling but generally ignore frequency-domain regularities.To address these limitations,this study proposes a Multiscale Long-Distance Feature Aggregation Network(MLFANet),a unified spatial-frequency segmentation framework for high-resolution remote sensing imagery.MLFANet introduces three key components:a Multiscale Global Dependency Extraction module for cascaded cross-scale contextual refinement,an FFT-based frequency-domain branch with learnable global filtering for capturing structural and texture regularities,and a bidirectional Spatial-Frequency Fusion module for adaptively aligning spatial details with frequency responses.Experiments on the ISPRS Potsdam and Vaihingen datasets demonstrate the effectiveness and feasibility of the proposed model.MLFANet achieves AF,MIoU,and OA values of 86.03%,76.21%,and 88.70%on Potsdam,and 83.17%,71.90%,and 86.33%on Vaihingen,respectively,outperforming representative CNN-based,attention-based,and hybrid models in overall metrics.In terms of computational complexity,MLFANet requires 17.49 GFLOPs under an input size of 256×256 pixels,indicating its practical feasibility for patch-based high-resolution remote sensing segmentation.Ablation studies further verify that multiscale dependency extraction,frequency-domain modeling,and adaptive spatial-frequency fusion each contribute to the final performance. 展开更多
关键词 Geospatial artificial intelligence(GeoAI) high-resolution remote sensing images semantic segmentation spatial-frequency fusion multiscale feature aggregation attention mechanism
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Enhancing sea ice classification on SAR imagery by integrating texture and polarimetric information with a deep learning model 认领 引用
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作者 Lichen GAO 《Journal of Oceanology and Limnology》 SCIE CAS CSCD 2026年第4期1342-1357,共16页
The satellite synthetic aperture radar(SAR)sensor is one of the most critical tools for monitoring Arctic sea ice.Classifying sea ice types based on SAR images has been a research hotspot.Most existing deep-learning-b... The satellite synthetic aperture radar(SAR)sensor is one of the most critical tools for monitoring Arctic sea ice.Classifying sea ice types based on SAR images has been a research hotspot.Most existing deep-learning-based sea ice classification models rely on the polarimetric information of SAR images while ignoring the gray-level co-occurrence matrix(GLCM)feature.This study develops a three-branch U-Net model for classifying sea ice in SAR images.By integrating polarimetric information,GLCM features,and auxiliary data,the model can classify open water(OW),young ice(YIC),first-year ice(FYI),and old ice(OIC).The model is trained and tested on the well-known AI4Arctic sea ice challenge dataset.Experiments on 57 testing SAR images demonstrate that the proposed model achieves an overall classification accuracy of 91.45% and an Intersection over Union(IoU)of 0.8464 for the four-type classification.Ablation experiments were conducted to evaluate the sensitivity of various GLCM features to sea ice classification.The effectiveness of the three-branch input for fusing polarimetric information,GLCM feature,and auxiliary data is validated.Results indicate that incorporating HV_mean significantly enhances classification performance,with an accuracy increase of approximately 0.7% and an improvement in IoU of 0.9%.The three-branch input structure is more effective than the single-branch structure in fusing three types of inputs,resulting in an accuracy increase of 4.7% and an improvement in IoU of 7%.Therefore,the proposed three-branch U-Net model demonstrates stable and reliable capabilities for classifying OW,YIC,FYI,and OIC in SAR images,providing a new approach for Arctic sea ice monitoring. 展开更多
关键词 synthetic aperture radar(SAR)image sea ice classification gray-level co-occurrence matrix(GLCM) U-Net deep learning
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Snapshot multispectral imaging through defocusing and a Fourier imager network 认领 引用
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作者 Xilin Yang Michael John Fanous +6 位作者 Hanlong Chen Ryan Lee Paloma Casteleiro Costa Yuhang Li Luzhe Huang Yijie Zhang Aydogan Ozcan 《Advanced Photonics Nexus》 CSCD 2025年第5期24-35,共12页
Multispectral imaging,which simultaneously captures the spatial and spectral information of a scene,is widely used across diverse fields,including remote sensing,biomedical imaging,and agricultural monitoring.We intro... Multispectral imaging,which simultaneously captures the spatial and spectral information of a scene,is widely used across diverse fields,including remote sensing,biomedical imaging,and agricultural monitoring.We introduce a snapshot multispectral imaging approach employing a standard monochrome image sensor with no additional spectral filters or customized components.Our system leverages the inherent chromatic aberration of wavelength-dependent defocusing as a natural source of physical encoding of multispectral information;this encoded image information is rapidly decoded via a deep learning-based multispectral Fourier imager network(mFIN).We experimentally tested our method with six illumination bands and demonstrated an overall accuracy of 98.25%for predicting the illumination channels at the input and achieved a robust multispectral image reconstruction on various test objects.This deep learning-powered framework achieves high-quality multispectral image reconstruction using snapshot image acquisition with a monochrome image sensor and could be useful for applications in biomedicine,industrial quality control,and agriculture,among others. 展开更多
关键词 computational imaging multispectral imaging deep learning image reconstruction Fourier imager network
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RASI:the Robotic All-Sky narrowband Imager 认领 引用
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作者 Yuxin Xin Baoli Lun +6 位作者 Zejun Hu Yue Zhong Kai Ye Hongying Xu Yufeng Fan Bin Li Dehong Huang 《Astronomical Techniques and Instruments》 CSCD 2025年第6期348-357,共10页
We report a new standalone Robotic All-Sky narrowband Imager(RASI)for auroral and airglow studies.RASI has new optics and an electromechanical system,low operation and installation costs,easy deployment and fully auto... We report a new standalone Robotic All-Sky narrowband Imager(RASI)for auroral and airglow studies.RASI has new optics and an electromechanical system,low operation and installation costs,easy deployment and fully automatic features.The new optics provide an all-sky field of view with excellent image quality and sensitivity.The new electromechanical system design offers a more compact size and the capability for outdoor independent deployment.We have also developed a fully automatic data acquisition software for RASI,which is based on the perception of solar altitude and the all-sky cloud cover.In conclusion,the RASI demonstrates significant advantages over the traditional all-sky narrowband imager,and it is highly suitable for the intensity measurements of large-scale auroras and airglow distributions. 展开更多
关键词 Robotic All-sky imager Auroras Airglow Optical imaging
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Hyperspectral imagery quality assessment and band reconstruction using the prophet model 认领 引用 被引量：2
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作者 Ping Ma Jinchang Ren +2 位作者 Zhi Gao Yinhe Li Rongjun Chen 《CAAI Transactions on Intelligence Technology》 SCIE EI CSCD 2025年第1期47-61,共15页
In Hyperspectral Imaging(HSI),the detrimental influence of noise and distortions on data quality is profound,which has severely affected the following-on analytics and decisionmaking such as land mapping.This study pr... In Hyperspectral Imaging(HSI),the detrimental influence of noise and distortions on data quality is profound,which has severely affected the following-on analytics and decisionmaking such as land mapping.This study presents an innovative framework for assessing HSI band quality and reconstructing the low-quality bands,based on the Prophet model.By introducing a comprehensive quality metric to start,the authors approach factors in both spatial and spectral characteristics across local and global scales.This metric effectively captures the intricate noise and distortions inherent in the HSI data.Subsequently,the authors employ the Prophet model to forecast information within the low-quality bands,leveraging insights from neighbouring high-quality bands.To validate the effectiveness of the authors’proposed model,extensive experiments on three publicly available uncorrected datasets are conducted.In a head-to-head comparison,the framework against six state-ofthe-art band reconstruction algorithms including three spectral methods,two spatialspectral methods and one deep learning method is benchmarked.The authors’experiments also delve into strategies for band selection based on quality metrics and the quality evaluation of the reconstructed bands.In addition,the authors assess the classification accuracy utilising these reconstructed bands.In various experiments,the results consistently affirm the efficacy of the authors’method in HSI quality assessment and band reconstruction.Notably,the authors’approach obviates the need for manually prefiltering of noisy bands.This comprehensive framework holds promise in addressing HSI data quality concerns whilst enhancing the overall utility of HSI. 展开更多
关键词 band reconstruction band quality hyperspectral image(HSI) prophet model
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Improving Image Quality of the Solar Disk Imager(SDI)of the LyαSolar Telescope(LST)Onboard the ASO-S Mission 认领 引用 被引量：1
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作者 Hui Liu Hui Li +11 位作者 Sizhong Zou Kaifan Ji Zhenyu Jin Jiahui Shan Jingwei Li Guanglu Shi Yu Huang Li Feng Jianchao Xue Qiao Li Dechao Song Ying Li 《Research in Astronomy and Astrophysics》 SCIE CAS CSCD 2025年第2期36-45,共10页
The in-flight calibration and performance of the Solar Disk Imager(SDI),which is a pivotal instrument of the LyαSolar Telescope onboard the Advanced Space-based Solar Observatory mission,suggested a much lower spatia... The in-flight calibration and performance of the Solar Disk Imager(SDI),which is a pivotal instrument of the LyαSolar Telescope onboard the Advanced Space-based Solar Observatory mission,suggested a much lower spatial resolution than expected.In this paper,we developed the SDI point-spread function(PSF)and Image Bivariate Optimization Algorithm(SPIBOA)to improve the quality of SDI images.The bivariate optimization method smartly combines deep learning with optical system modeling.Despite the lack of information about the real image taken by SDI and the optical system function,this algorithm effectively estimates the PSF of the SDI imaging system directly from a large sample of observational data.We use the estimated PSF to conduct deconvolution correction to observed SDI images,and the resulting images show that the spatial resolution after correction has increased by a factor of more than three with respect to the observed ones.Meanwhile,our method also significantly reduces the inherent noise in the observed SDI images.The SPIBOA has now been successfully integrated into the routine SDI data processing,providing important support for the scientific studies based on the data.The development and application of SPIBOA also paves new ways to identify astronomical telescope systems and enhance observational image quality.Some essential factors and precautions in applying the SPIBOA method are also discussed. 展开更多
关键词 techniques:image processing Sun:chromosphere Sun:flares methods:numerical
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Attention Driven YOLOv5 Network for Enhanced Landslide Detection Using Satellite Imagery of Complex Terrain 认领 引用 被引量：1
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作者 Naveen Chandra Himadri Vaidya +2 位作者 Suraj Sawant Shilpa Gite Biswajeet Pradhan 《Computer Modeling in Engineering & Sciences》 SCIE EI 2025年第6期3351-3375,共25页
Landslide hazard detection is a prevalent problem in remote sensing studies,particularly with the technological advancement of computer vision.With the continuous and exceptional growth of the computational environmen... Landslide hazard detection is a prevalent problem in remote sensing studies,particularly with the technological advancement of computer vision.With the continuous and exceptional growth of the computational environment,the manual and partially automated procedure of landslide detection from remotely sensed images has shifted toward automatic methods with deep learning.Furthermore,attention models,driven by human visual procedures,have become vital in natural hazard-related studies.Hence,this paper proposes an enhanced YOLOv5(You Only Look Once version 5)network for improved satellite-based landslide detection,embedded with two popular attention modules:CBAM(Convolutional Block Attention Module)and ECA(Efficient Channel Attention).These attention mechanisms are incorporated into the backbone and neck of the YOLOv5 architecture,distinctly,and evaluated across three YOLOv5 variants:nano(n),small(s),and medium(m).The experiments use opensource satellite images from three distinct regions with complex terrain.The standard metrics,including F-score,precision,recall,and mean average precision(mAP),are computed for quantitative assessment.The YOLOv5n+CBAM demonstrates the most optimal results with an F-score of 77.2%,confirming its effectiveness.The suggested attention-driven architecture augments detection accuracy,supporting post-landslide event assessment and recovery. 展开更多
关键词 Attention mechanism convolutional neural networks landslides remote sensing images YOLOv5
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Transformer-Based Fusion of Infrared and Visible Imagery for Smoke Recognition in Commercial Areas 认领 引用
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作者 Chongyang Wang Qiongyan Li +2 位作者 Shu Liu Pengle Cheng Ying Huang 《Computers, Materials & Continua》 SCIE EI 2025年第9期5157-5176,共20页
With rapid urbanization,fires pose significant challenges in urban governance.Traditional fire detection methods often struggle to detect smoke in complex urban scenes due to environmental interferences and variations... With rapid urbanization,fires pose significant challenges in urban governance.Traditional fire detection methods often struggle to detect smoke in complex urban scenes due to environmental interferences and variations in viewing angles.This study proposes a novel multimodal smoke detection method that fuses infrared and visible imagery using a transformer-based deep learning model.By capturing both thermal and visual cues,our approach significantly enhances the accuracy and robustness of smoke detection in business parks scenes.We first established a dual-view dataset comprising infrared and visible light videos,implemented an innovative image feature fusion strategy,and designed a deep learning model based on the transformer architecture and attention mechanism for smoke classification.Experimental results demonstrate that our method outperforms existing methods,under the condition of multi-view input,it achieves an accuracy rate of 90.88%,precision rate of 98.38%,recall rate of 92.41%and false positive and false negative rates both below 5%,underlining the effectiveness of the proposed multimodal and multi-view fusion approach.The attention mechanism plays a crucial role in improving detection performance,particularly in identifying subtle smoke features. 展开更多
关键词 Multimodal image processing smoke recognition urban safety environmental monitoring
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Skin tone bias in online psoriasis imagery:Insights from an international study 认领 引用
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作者 Aman Sandhu Sanya Ailani +4 位作者 Smitesh Padte Priyal Mehta Neha Deo Salim Surani Rahul Kashyap 《World Journal of Clinical Cases》 2025年第36期6-12,共7页
BACKGROUND Psoriasis is often first recognized by patients through online image searches.However,search engine algorithms influenced by geographic location may still produce results that predominantly feature lighter ... BACKGROUND Psoriasis is often first recognized by patients through online image searches.However,search engine algorithms influenced by geographic location may still produce results that predominantly feature lighter skin tones,regardless of the region’s majority skin type.This underrepresentation may limit recognition and delay care for people of color.AIM To examine whether search algorithms tailor region-specific results in terms of skin color for psoriasis imagery.METHODS This observational study recruited 66 participants from 18 countries who conducted image searches for“psoriasis”across various web browsers.During the meeting,a Google form was posted to record observations,and participants reported the diversity of skin tones in the first three rows of search results using a reference image depicting Fitzpatrick types.RESULTS Results showed a global bias toward lighter skin tones,with 94%of participants identifying light skin predominance in the first row and minimal representation of medium or darker skin tones in subsequent results,verified via χ2 analysis.Participants who observed darker or mixed skin tones typically found them further down their results.CONCLUSION There remains a significant gap in global representation of psoriasis imagery.This paper deepens the current understanding of bias in online media and pushes for further exploration of more inclusive dermatologic imagery. 展开更多
关键词 Psoriasis Internet Skin tone bias Image search Global
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Comparative Analysis of Deep Learning Models for Banana Plant Detection in UAV RGB and Grayscale Imagery 认领 引用
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作者 Ching-Lung Fan Yu-Jen Chung Shan-Min Yen 《Computers, Materials & Continua》 SCIE EI 2025年第9期4627-4653,共27页
Efficient banana crop detection is crucial for precision agriculture;however,traditional remote sensing methods often lack the spatial resolution required for accurate identification.This study utilizes low-altitude U... Efficient banana crop detection is crucial for precision agriculture;however,traditional remote sensing methods often lack the spatial resolution required for accurate identification.This study utilizes low-altitude Unmanned Aerial Vehicle(UAV)images and deep learning-based object detection models to enhance banana plant detection.A comparative analysis of Faster Region-Based Convolutional Neural Network(Faster R-CNN),You Only Look Once Version 3(YOLOv3),Retina Network(RetinaNet),and Single Shot MultiBox Detector(SSD)was conducted to evaluate their effectiveness.Results show that RetinaNet achieved the highest detection accuracy,with a precision of 96.67%,a recall of 71.67%,and an F1 score of 81.33%.The study further highlights the impact of scale variation,occlusion,and vegetation density on detection performance.Unlike previous studies,this research systematically evaluates multi-scale object detection models for banana plant identification,offering insights into the advantages of UAV-based deep learning applications in agriculture.In addition,this study compares five evaluation metrics across the four detection models using both RGB and grayscale images.Specifically,RetinaNet exhibited the best overall performance with grayscale images,achieving the highest values across all five metrics.Compared to its performance with RGB images,these results represent a marked improvement,confirming the potential of grayscale preprocessing to enhance detection capability. 展开更多
关键词 Unmanned Aerial Vehicle image object detection deep learning banana crops
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High-resolution pixelated CdZnTe detector prototype system for solar hard X-ray imager 认领 引用 被引量：9
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作者 Shen Wang Jian-Hua Guo +1 位作者 Yan Zhang Wei Chen 《Nuclear Science and Techniques》 SCIE EI CAS CSCD 2019年第3期53-58,共6页
A multichannel low-noise electronic prototype system was designed for a pixelated CdZnTe detector. This system is the result of preliminary work on a solar hard X-ray imager, which is one of the three payloads for fut... A multichannel low-noise electronic prototype system was designed for a pixelated CdZnTe detector. This system is the result of preliminary work on a solar hard X-ray imager, which is one of the three payloads for future solar observations satellite-Advanced Space-based Solar Observatory(ASO-S). A new charge-sensitive amplifier application-specific integrated circuit, VATA450.3, with an on-chip analog-to-digital converter, is used to read out 8×8 anode pixel signals. Two CdZnTe detectors with a thickness of 2 mm and 5 mm were tested. The 2-mm-thick detector achieved energy resolution better than 5%(fullwidth at half-maximum, FWHM) at 59.5 keV, and the 5-mm-thick detector had better resolution than 1.2%(FWHM) at 662 keV. The design and test results of the prototype system are discussed in this paper. 展开更多
关键词 CdZnTe Readout system Solar observation Hard X-ray imager
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