Based on the statistical characteristics of remote sensing data, the spatial geometric structure characteristics of spectral data and distribution of background, interference and alteration information in characterist...Based on the statistical characteristics of remote sensing data, the spatial geometric structure characteristics of spectral data and distribution of background, interference and alteration information in characteristic space were researched through the analysis of two-dimensional and three-dimensional scatter diagrams. The results indicate that the hyper-space of remote sensing multi-data aggregation belongs to low-dimensional geometric structure, i.e. hyperplane form, and anomalous point groups including alteration information usually dissociate out of hyperplane. Scatter diagrams of remote sensing data band are mainly presented as two distribution forms of single-ellipse and dual-ellipse. Clarifying the relations of three objects of background, disturbance and alteration information in remote sensing images provides an important technical thought and guidance for accurately detecting and extracting remote sensing alteration information.展开更多
Accurate estimation of soil lead pollution degree is one of the key steps in controlling soil lead pollution; vegetable hyperspectral features research provided a new approach to discovering and monitoring soil heavy ...Accurate estimation of soil lead pollution degree is one of the key steps in controlling soil lead pollution; vegetable hyperspectral features research provided a new approach to discovering and monitoring soil heavy metal pollution.Spectral reflectance implies information of pollution impacts on vegetation;estimation of lead pollution degree based on the spectral reflectance is equivalent to extraction of weak information.This study puts forward a new feature extraction method based展开更多
针对遥感图像微小目标检测中存在的浅层细化特征、深层语义表征和多尺度信息提取3个问题,提出一种综合运用多项技术的跨尺度YOLOv7(cross-scale YOLOv7,CSYOLOv7)网络。首先,设计跨阶段特征提取模块(cross-stage feature extraction mod...针对遥感图像微小目标检测中存在的浅层细化特征、深层语义表征和多尺度信息提取3个问题,提出一种综合运用多项技术的跨尺度YOLOv7(cross-scale YOLOv7,CSYOLOv7)网络。首先,设计跨阶段特征提取模块(cross-stage feature extraction module,CFEM)和感受野特征增强模块(receptive field feature enhancement module,RFFEM)。CFEM提高模型细化特征提取能力并抑制浅层下采样过程中特征的丢失,RFFEM加大网络对深层语义特征的提取力度,增强模型对目标上下文信息获取能力。其次,设计跨梯度空间金字塔池化模块(cross-gradient space pyramid pool module,CSPPM)有效融合微小目标多尺度的全局和局部特征。最后,用形状感知交并比(shape-aware intersection over union,Shape IoU)替换完全交并比(complete intersection over union,CIoU),提高模型在边界框定位任务中的精确度。实验结果表明,CSYOLOv7网络在DIOR(dataset for image object recognition)数据集和NWPU VHR-10(Northwestern Polytechnical University Very High Resolution-10)数据集上分别取得了74%和89.6%的检测精度,有效提升遥感图像微小目标的检测效果。展开更多
利用遥感技术快速准确地提取耕地信息是耕地保护的关键环节。以山东省商河县为例,提出了一种基于多季相分形特征的Landsat 8 OLI影像耕地信息提取方法。首先采用毯子覆盖法计算多季相遥感影像每个像元的上分形信号和下分形信号,对比分...利用遥感技术快速准确地提取耕地信息是耕地保护的关键环节。以山东省商河县为例,提出了一种基于多季相分形特征的Landsat 8 OLI影像耕地信息提取方法。首先采用毯子覆盖法计算多季相遥感影像每个像元的上分形信号和下分形信号,对比分析耕地和其他土地利用类型的分形特征,选取上分形信号的第3尺度作为特征尺度,提取商河县耕地空间分布特征;其次采用同时期的土地利用矢量数据、Esri land cover数据和统计数据进行耕地信息提取精度评价;最后分别设置多季相分形提取与单季相分形提取、现有土地利用数据产品的对比实验,并基于点位匹配度和面积匹配度进行评价。结果表明:多季相数据更能反映农作物生长的复杂性,有助于提高耕地信息的提取精度;不同土地利用类型在不同分形尺度的信号值各不相同,分形特征可以在不同尺度上清晰地刻画出不同土地利用类型的分异性;基于矢量数据和Esri land cover数据评价的多季相分形特征耕地提取点位匹配度为87.13%和89.83%,面积匹配度为99.73%和97.91%,均比单季相分形提取结果精度高;综合考虑点位匹配度、面积匹配度和空间分布特征,研发方法能有效区分耕地和其他土地利用类型,提取结果更优,且与统计数据有更高的一致性。该方法可准确提取耕地信息,为耕地的动态监测和损害评估提供技术支撑。展开更多
基金Project(2006BAB01A06) supported by the National Science and Technology Pillar Program Project during the 11th Five-Year Plan PeriodProject(1212010761503) supported by Land and Resources Investigation Project
文摘Based on the statistical characteristics of remote sensing data, the spatial geometric structure characteristics of spectral data and distribution of background, interference and alteration information in characteristic space were researched through the analysis of two-dimensional and three-dimensional scatter diagrams. The results indicate that the hyper-space of remote sensing multi-data aggregation belongs to low-dimensional geometric structure, i.e. hyperplane form, and anomalous point groups including alteration information usually dissociate out of hyperplane. Scatter diagrams of remote sensing data band are mainly presented as two distribution forms of single-ellipse and dual-ellipse. Clarifying the relations of three objects of background, disturbance and alteration information in remote sensing images provides an important technical thought and guidance for accurately detecting and extracting remote sensing alteration information.
文摘Accurate estimation of soil lead pollution degree is one of the key steps in controlling soil lead pollution; vegetable hyperspectral features research provided a new approach to discovering and monitoring soil heavy metal pollution.Spectral reflectance implies information of pollution impacts on vegetation;estimation of lead pollution degree based on the spectral reflectance is equivalent to extraction of weak information.This study puts forward a new feature extraction method based
文摘针对遥感图像微小目标检测中存在的浅层细化特征、深层语义表征和多尺度信息提取3个问题,提出一种综合运用多项技术的跨尺度YOLOv7(cross-scale YOLOv7,CSYOLOv7)网络。首先,设计跨阶段特征提取模块(cross-stage feature extraction module,CFEM)和感受野特征增强模块(receptive field feature enhancement module,RFFEM)。CFEM提高模型细化特征提取能力并抑制浅层下采样过程中特征的丢失,RFFEM加大网络对深层语义特征的提取力度,增强模型对目标上下文信息获取能力。其次,设计跨梯度空间金字塔池化模块(cross-gradient space pyramid pool module,CSPPM)有效融合微小目标多尺度的全局和局部特征。最后,用形状感知交并比(shape-aware intersection over union,Shape IoU)替换完全交并比(complete intersection over union,CIoU),提高模型在边界框定位任务中的精确度。实验结果表明,CSYOLOv7网络在DIOR(dataset for image object recognition)数据集和NWPU VHR-10(Northwestern Polytechnical University Very High Resolution-10)数据集上分别取得了74%和89.6%的检测精度,有效提升遥感图像微小目标的检测效果。
文摘利用遥感技术快速准确地提取耕地信息是耕地保护的关键环节。以山东省商河县为例,提出了一种基于多季相分形特征的Landsat 8 OLI影像耕地信息提取方法。首先采用毯子覆盖法计算多季相遥感影像每个像元的上分形信号和下分形信号,对比分析耕地和其他土地利用类型的分形特征,选取上分形信号的第3尺度作为特征尺度,提取商河县耕地空间分布特征;其次采用同时期的土地利用矢量数据、Esri land cover数据和统计数据进行耕地信息提取精度评价;最后分别设置多季相分形提取与单季相分形提取、现有土地利用数据产品的对比实验,并基于点位匹配度和面积匹配度进行评价。结果表明:多季相数据更能反映农作物生长的复杂性,有助于提高耕地信息的提取精度;不同土地利用类型在不同分形尺度的信号值各不相同,分形特征可以在不同尺度上清晰地刻画出不同土地利用类型的分异性;基于矢量数据和Esri land cover数据评价的多季相分形特征耕地提取点位匹配度为87.13%和89.83%,面积匹配度为99.73%和97.91%,均比单季相分形提取结果精度高;综合考虑点位匹配度、面积匹配度和空间分布特征,研发方法能有效区分耕地和其他土地利用类型,提取结果更优,且与统计数据有更高的一致性。该方法可准确提取耕地信息,为耕地的动态监测和损害评估提供技术支撑。
