矿产资源开采引发的地表沉陷积水是高潜水位矿区耕地资源破坏的重要原因,也是制约矿山绿色发展的瓶颈之一。面向矿区沉陷积水早期监测识别需求,建立了基于多时相合成孔径雷达(SAR)遥感影像的矿区沉陷积水监测方法,命名为矿区雷达水分指...矿产资源开采引发的地表沉陷积水是高潜水位矿区耕地资源破坏的重要原因,也是制约矿山绿色发展的瓶颈之一。面向矿区沉陷积水早期监测识别需求,建立了基于多时相合成孔径雷达(SAR)遥感影像的矿区沉陷积水监测方法,命名为矿区雷达水分指数(Mining-area Radar Water Index,MRWI)。以山东省龙堌镇某井工(地下开采)煤矿区为研究区域,收集并处理了2017年哨兵1号(Sentinel-1)GRD产品IW模式29景10 m空间分辨率的SAR影像数据,进行了沉陷区积水的动态监测及其演变分析。利用查准率P(precision)、查全率R(recall)以及两者的调和平均数F1这3种定量指标,对比分析了MRWI与光学水体指数(Visible and Shortwave Infrared Drought Index,VSDI)和单时相雷达水体指数(Sentinel-1 Dual-polarized Water Index,SDWI)的监测性能。结果表明:对于存在明显地表水体覆盖区域,MRWI、VSDI以及SDWI均能有效识别出水体范围,其中MRWI与VSDI相比的P、R和F1平均值分别达到了94.45%、94.83%和94.46%;MRWI与SDWI相比,P、R和F1平均值分别达到了99.86%、94.03%和96.85%。MRWI具有较强的沉陷积水早期监测能力,以2号积水区为例,MRWI在2017年6月6日监测到了沉陷积水现象,此时VSDI和SDWI均不能指示这一现象;在2017年7月12日左右,沉陷积水导致地表被水体覆盖,此时MRWI、VSDI和SDWI均能指示出沉陷积水现象。MRWI具备较强的时序监测能力,基于Sentinel-1雷达数据的MRWI能够做到至少每12 d为一期的时序监测,并且能够利用长时序的结果对矿区沉陷区积水变化进行时空演变趋势分析。MRWI可为高潜水位矿区沉陷积水现象的早期监测与治理提供有力技术支撑。展开更多
针对深度学习模型易出现灾难性遗忘的关键难点,提出了一种基于原型对比的合成孔径雷达(synthetic aperture radar,SAR)图像增量小样本目标检测算法-InFSAR(prototype contrast based incremental few-shot SAR object detection)。首先...针对深度学习模型易出现灾难性遗忘的关键难点,提出了一种基于原型对比的合成孔径雷达(synthetic aperture radar,SAR)图像增量小样本目标检测算法-InFSAR(prototype contrast based incremental few-shot SAR object detection)。首先,采用基础数据集对检测器进行预训练,以构建初步的特征提取能力;其次,设计一种类原型表征生成模块,以构建一组能够代表数据内在特征的类原型。在增量学习阶段,设计一种混合类原型对比编码模块,以有效学习新类别与基础类别之间的区分性特征。此外,为缓解灾难性遗忘问题,引入类原型校准策略,使模型在类原型上的预测分布逐步逼近真实分布,从而保持对基础类别识别的稳定性。在小样本目标检测数据集SRSDD-v1.0上的实验结果表明,在5-shot设置下,InFSAR对船舶细粒度目标的检测精度达到46.5%。同时,该方法能够在无需访问基础类训练数据的情况下,实现对少量标注新类别的增量检测与识别。展开更多
Landslide susceptibility mapping(LSM)is an essential tool for mitigating the escalating global risk of landslides.However,challenges such as the heterogeneity of different landslide triggers,extensive engineering acti...Landslide susceptibility mapping(LSM)is an essential tool for mitigating the escalating global risk of landslides.However,challenges such as the heterogeneity of different landslide triggers,extensive engineering activities exacerbated reactivation,and the interpretability of data-driven models have hindered the practical application of LSM.This work proposes a novel framework for enhancing LSM considering different triggers for accumulation and rock landslides,leveraging interpretable machine learning and Multi-temporal Interferometric Synthetic Aperture Radar(MT-InSAR)technology.Initially,a refined fieldinvestigation was conducted to delineate the accumulation and rock area according to landslide types,leading to the identificationof relevant contributing factors.Deformation along the slope was then combined with time-series analysis to derive a landslide activity level(AL)index to recognize the likelihood of reactivation or dormancy.The SHapley Additive exPlanation(SHAP)technique facilitated the interpretation of factors and the identificationof determinants in high susceptibility areas.The results indicate that random forest(RF)outperformed other models in both accumulation and rock areas.Key factors including thickness and weak intercalation were identifiedfor accumulation and rock landslides.The introduction of AL substantially enhanced the predictive capability of the LSM and outperformed models that neglect movement trends or deformation rates with an average ratio of 81.23%in high susceptibility zones.Besides,the fieldvalidation confirmedthat 83.8%of newly identifiedlandslides were correctly upgraded.Given its efficiencyand operational simplicity,the proposed hybrid model opens new avenues for the feasibility of enhancement in LSM at urban settlements worldwide.展开更多
The unique advantage of x-ray ghost imaging(XGI)is its potential in low dose radiology.One of the practical ways to reduce the radiation exposure is to reduce the measurements while remaining sufficient image quality....The unique advantage of x-ray ghost imaging(XGI)is its potential in low dose radiology.One of the practical ways to reduce the radiation exposure is to reduce the measurements while remaining sufficient image quality.Synthetic aperture x-ray ghost imaging(SAXGI)is invented to achieve megapixel XGI with limited measurements,which is expected to implement XGI simultaneously with large field of view and low radiation exposure.In this paper,we experimentally investigate the effect of measurements reduction on the spatial resolution and image quality of SAXGI with standard sample and biomedical specimen.The results with a resolution chart demonstrated that at 360 measurements,SAXGI successfully retrieved the sample image of 1960×1960 pixels with spatial resolution of 4μm.With measurement reduction,the spatial resolution deteriorates but the sparser structures are still discernable.Even with measurements reduced to 10,a spatial resolution of 10μm can still be achieved by SAXGI.A biomedical sample of a fish specimen is employed to evaluate the method and the fish image of 2000×1000 pixels with an SSIM of 0.962 is reconstructed by SAXGI with 770measurements,corresponding to an accumulative exposure reduction of more than 2 times.With the measurements reduced to 10 which corresponds to 1/160 of the accumulative radiation exposure for conventional radiology,bulky structure like the fish skeleton can still be definitely discerned and the SSIM for the reconstructed image still retained 0.9179.Results of this paper demonstrate that measurements reduction is practicable for the radiation exposure reduction of the sample,which implicates that SAXGI with limited measurements is an efficient solution for low dose radiology.展开更多
文摘矿产资源开采引发的地表沉陷积水是高潜水位矿区耕地资源破坏的重要原因,也是制约矿山绿色发展的瓶颈之一。面向矿区沉陷积水早期监测识别需求,建立了基于多时相合成孔径雷达(SAR)遥感影像的矿区沉陷积水监测方法,命名为矿区雷达水分指数(Mining-area Radar Water Index,MRWI)。以山东省龙堌镇某井工(地下开采)煤矿区为研究区域,收集并处理了2017年哨兵1号(Sentinel-1)GRD产品IW模式29景10 m空间分辨率的SAR影像数据,进行了沉陷区积水的动态监测及其演变分析。利用查准率P(precision)、查全率R(recall)以及两者的调和平均数F1这3种定量指标,对比分析了MRWI与光学水体指数(Visible and Shortwave Infrared Drought Index,VSDI)和单时相雷达水体指数(Sentinel-1 Dual-polarized Water Index,SDWI)的监测性能。结果表明:对于存在明显地表水体覆盖区域,MRWI、VSDI以及SDWI均能有效识别出水体范围,其中MRWI与VSDI相比的P、R和F1平均值分别达到了94.45%、94.83%和94.46%;MRWI与SDWI相比,P、R和F1平均值分别达到了99.86%、94.03%和96.85%。MRWI具有较强的沉陷积水早期监测能力,以2号积水区为例,MRWI在2017年6月6日监测到了沉陷积水现象,此时VSDI和SDWI均不能指示这一现象;在2017年7月12日左右,沉陷积水导致地表被水体覆盖,此时MRWI、VSDI和SDWI均能指示出沉陷积水现象。MRWI具备较强的时序监测能力,基于Sentinel-1雷达数据的MRWI能够做到至少每12 d为一期的时序监测,并且能够利用长时序的结果对矿区沉陷区积水变化进行时空演变趋势分析。MRWI可为高潜水位矿区沉陷积水现象的早期监测与治理提供有力技术支撑。
文摘针对深度学习模型易出现灾难性遗忘的关键难点,提出了一种基于原型对比的合成孔径雷达(synthetic aperture radar,SAR)图像增量小样本目标检测算法-InFSAR(prototype contrast based incremental few-shot SAR object detection)。首先,采用基础数据集对检测器进行预训练,以构建初步的特征提取能力;其次,设计一种类原型表征生成模块,以构建一组能够代表数据内在特征的类原型。在增量学习阶段,设计一种混合类原型对比编码模块,以有效学习新类别与基础类别之间的区分性特征。此外,为缓解灾难性遗忘问题,引入类原型校准策略,使模型在类原型上的预测分布逐步逼近真实分布,从而保持对基础类别识别的稳定性。在小样本目标检测数据集SRSDD-v1.0上的实验结果表明,在5-shot设置下,InFSAR对船舶细粒度目标的检测精度达到46.5%。同时,该方法能够在无需访问基础类训练数据的情况下,实现对少量标注新类别的增量检测与识别。
基金supported by the National Key R&D Program of China(Grant No.2023YFC3007201)the National Natural Science Foundation of China(Grant No.42377161)the Opening Fund of Key Laboratory of Geological Survey and Evaluation of Ministry of Education(Grant No.GLAB 2024ZR03).
文摘Landslide susceptibility mapping(LSM)is an essential tool for mitigating the escalating global risk of landslides.However,challenges such as the heterogeneity of different landslide triggers,extensive engineering activities exacerbated reactivation,and the interpretability of data-driven models have hindered the practical application of LSM.This work proposes a novel framework for enhancing LSM considering different triggers for accumulation and rock landslides,leveraging interpretable machine learning and Multi-temporal Interferometric Synthetic Aperture Radar(MT-InSAR)technology.Initially,a refined fieldinvestigation was conducted to delineate the accumulation and rock area according to landslide types,leading to the identificationof relevant contributing factors.Deformation along the slope was then combined with time-series analysis to derive a landslide activity level(AL)index to recognize the likelihood of reactivation or dormancy.The SHapley Additive exPlanation(SHAP)technique facilitated the interpretation of factors and the identificationof determinants in high susceptibility areas.The results indicate that random forest(RF)outperformed other models in both accumulation and rock areas.Key factors including thickness and weak intercalation were identifiedfor accumulation and rock landslides.The introduction of AL substantially enhanced the predictive capability of the LSM and outperformed models that neglect movement trends or deformation rates with an average ratio of 81.23%in high susceptibility zones.Besides,the fieldvalidation confirmedthat 83.8%of newly identifiedlandslides were correctly upgraded.Given its efficiencyand operational simplicity,the proposed hybrid model opens new avenues for the feasibility of enhancement in LSM at urban settlements worldwide.
基金Project supported by the National Key Research and Development Program of China(Grant Nos.2022YFA1603601,2021YFF0601203,and 2021YFA1600703)。
文摘The unique advantage of x-ray ghost imaging(XGI)is its potential in low dose radiology.One of the practical ways to reduce the radiation exposure is to reduce the measurements while remaining sufficient image quality.Synthetic aperture x-ray ghost imaging(SAXGI)is invented to achieve megapixel XGI with limited measurements,which is expected to implement XGI simultaneously with large field of view and low radiation exposure.In this paper,we experimentally investigate the effect of measurements reduction on the spatial resolution and image quality of SAXGI with standard sample and biomedical specimen.The results with a resolution chart demonstrated that at 360 measurements,SAXGI successfully retrieved the sample image of 1960×1960 pixels with spatial resolution of 4μm.With measurement reduction,the spatial resolution deteriorates but the sparser structures are still discernable.Even with measurements reduced to 10,a spatial resolution of 10μm can still be achieved by SAXGI.A biomedical sample of a fish specimen is employed to evaluate the method and the fish image of 2000×1000 pixels with an SSIM of 0.962 is reconstructed by SAXGI with 770measurements,corresponding to an accumulative exposure reduction of more than 2 times.With the measurements reduced to 10 which corresponds to 1/160 of the accumulative radiation exposure for conventional radiology,bulky structure like the fish skeleton can still be definitely discerned and the SSIM for the reconstructed image still retained 0.9179.Results of this paper demonstrate that measurements reduction is practicable for the radiation exposure reduction of the sample,which implicates that SAXGI with limited measurements is an efficient solution for low dose radiology.
