Segmentation is the key step in auto-interpretation of high-resolution spaceborne synthetic aperture radar（SAR） images. A novel method is proposed based on integrating the geometric active contour（GAC） and the sup...Segmentation is the key step in auto-interpretation of high-resolution spaceborne synthetic aperture radar（SAR） images. A novel method is proposed based on integrating the geometric active contour（GAC） and the support vector machine（SVM）models. First, the images are segmented by using SVM and textural statistics. A likelihood measurement for every pixel is derived by using the initial segmentation. The Chan-Vese model then is modified by adding two items： the likelihood and the distance between the initial segmentation and the evolving contour. Experimental results using real SAR images demonstrate the good performance of the proposed method compared to several classic GAC models.展开更多
The Landsat image information has recently been widely applied to structural geology, especially to the analysis of lineaments, owing to their macroscopic, visual and comprehensive features. The images will be more ef...The Landsat image information has recently been widely applied to structural geology, especially to the analysis of lineaments, owing to their macroscopic, visual and comprehensive features. The images will be more effective when applied to the interpretation of active faults. Active faults are widely ditributed in China. Much attention has been paid to the study of active faults both in China and abroad. There is certain controversy concerning the implication of the term "active fault". Strictly speaking, the term should refer only to the faults that are still active in the present day. However, the term also usually refers to the faults which have been active continually or intermittently from the Quaternary (or the end of Tertiary) to the present day. We propose that the tones and the configurations of features on Landsat images are the principal keys to the interpretation of active faults. The faults, which display the most prominent展开更多
分数阶Active Demons(fractional active demons,FAD)算法是图像非刚性配准的有效方法,并且能解决灰度均匀和弱纹理图像配准精度低,优化易陷入局部极小而导致的配准速度缓慢问题,但是该算法中分数阶最佳阶次的寻找需要通过多次实验人工...分数阶Active Demons(fractional active demons,FAD)算法是图像非刚性配准的有效方法,并且能解决灰度均匀和弱纹理图像配准精度低,优化易陷入局部极小而导致的配准速度缓慢问题,但是该算法中分数阶最佳阶次的寻找需要通过多次实验人工选取,缺乏阶次自适应性.针对该问题,提出了基于多分辨率和自适应分数阶的Active Demons算法,该算法首先根据图像梯度模值和信息熵,构建了自适应分数阶阶次的数学模型,基于该模型自动计算出分数阶的最佳阶次和微分动态模板;然后将多分辨率策略加入到自适应分数阶Active Demons算法中,进一步提高了图像配准效率.理论分析和实验结果均表明:提出的算法可用于灰度均匀、弱边缘和弱纹理图像的配准,能根据图像的局部特征自适应计算最佳分数阶阶次,并避免了算法陷入局部最优,从而提高了图像配准的精度和效率.展开更多
To determine the distribution of active earth pressure on retaining walls, a series of model tests with the horizontally translating rigid walls are designed. Particle image velocimetry is used to study the movement a...To determine the distribution of active earth pressure on retaining walls, a series of model tests with the horizontally translating rigid walls are designed. Particle image velocimetry is used to study the movement and shear strain during the active failure of soil with height H and friction angle φ. The test results show that there are 3 stages of soil deformation under retaining wall translation: the initial stage, the expansion stage and the stability stage. The stable sliding surface in the model tests can be considered to be composed of two parts. Within the height range of 0.82 H-1.0 H, it is a plane at an angle of π/4+φ/2 to the horizontal plane. In the height range of 0-0.82 H, it is a curve between a logarithmic spiral and a plane at an angle of π/4+φ/2 to the horizontal. A new method applicable to any sliding surface is proposed for active earth pressure with the consideration of arching effect. The active earth pressure is computed with the actual shape of the slip surface and compared with model test data and with predictions obtained by existing methods. The comparison shows that predictions from the newly proposed method are more consistent with the measured data than the predictions from the other methods.展开更多
Objective:To reveal the neural network of active and passive hand movements. Method:Seven healthy aged people were checked, and acquired functional magnetic resonance imaging data on a 1.5T scanner. Active movement co...Objective:To reveal the neural network of active and passive hand movements. Method:Seven healthy aged people were checked, and acquired functional magnetic resonance imaging data on a 1.5T scanner. Active movement consisted of repetitive grasping and loosening of hand; passive movement involved the same movement performed by examiner. Both types of hand movements were assessed separately. These data were analysed by Statistical Parametric Mapping Microsoft. Result:The main activated brain areas were the contralateral supplemental motor area, primary motor area, primary sensory area and the ipsilateral cerebellum when subjects gripped right hands actively and passively. The supplemental area was less active in passive hand movement than active hand movement. The activated brain areas were mainly within Brodmann area 4 during active hand movement; in the contrast, the voxels triggered by passive movement were mainly within Brodmann areas 3,1,2 areas. Conclusion:The results suggest that the neural networks of passive and active tasks spared some common areas, and the passive movement could be as effective as active movement to facilitate the recovery of limbs motor function in patients with brain damage.展开更多
从遥感影像上自动解译铁路设计控制要素是实现“一键成图”的关键,但深度学习遥感影像智能解译需要大量标注样本。依据铁路线路设计原则,提出一种多源遥感数据的设计控制要素智能解译样本库构建方法。首先,基于数字正射影像图(Digital O...从遥感影像上自动解译铁路设计控制要素是实现“一键成图”的关键,但深度学习遥感影像智能解译需要大量标注样本。依据铁路线路设计原则,提出一种多源遥感数据的设计控制要素智能解译样本库构建方法。首先,基于数字正射影像图(Digital Orthophoto Map,DOM)、数字线划地图(Digital Line Graphic,DLG)和激光雷达(Light Detection and Ranging,Lidar)点云多源数据自动生成初始样本;其次,基于增量主动学习迭代方法对初始样本进行优化,达到高质量、全面覆盖铁路沿线的目的;然后,以长赣铁路为例,构建以铁路沿线周边房屋、道路、水体和植被四类铁路线路设计控制要素为重点的高分辨率智能解译样本数据库——铁路线路设计控制要素智能解译样本库(Wuhan University Sample Database of Control Elements of Railway Route Design,WHU-RRDSD),其地面分辨率为0.1 m,样本总数超过20万张;最后,为验证样本库的可用性,分别从定性评价、定量评价以及其他场景应用案例三方面进行详细验证,结果表明,基于房屋、道路、水体和植被四类样本库的IoU评价指标分别为84.43%、82.38%、90.19%、90.28%,表现出优异的解译效果;基于WHU-RRDSD训练得到的智能模型迁移至宜涪高铁场景中房屋、道路、水体和植被要素的解译,验证样本库在其他场景的可用性;简要介绍基于WHU-RRDSD样本库进行的高分辨率遥感图像弱监督建筑提取和高分辨率遥感图像地物分类两个应用案例,进一步验证本文方法所构建样本库可用性。展开更多
基金supported by the National Natural Science Foundation of China(4117132741301361)+2 种基金the National Key Basic Research Program of China(973 Program)(2012CB719903)the Science and Technology Project of Ministry of Transport of People’s Republic of China(2012-364-X11-803)the Shanghai Municipal Natural Science Foundation(12ZR1433200)
文摘Segmentation is the key step in auto-interpretation of high-resolution spaceborne synthetic aperture radar（SAR） images. A novel method is proposed based on integrating the geometric active contour（GAC） and the support vector machine（SVM）models. First, the images are segmented by using SVM and textural statistics. A likelihood measurement for every pixel is derived by using the initial segmentation. The Chan-Vese model then is modified by adding two items： the likelihood and the distance between the initial segmentation and the evolving contour. Experimental results using real SAR images demonstrate the good performance of the proposed method compared to several classic GAC models.
文摘The Landsat image information has recently been widely applied to structural geology, especially to the analysis of lineaments, owing to their macroscopic, visual and comprehensive features. The images will be more effective when applied to the interpretation of active faults. Active faults are widely ditributed in China. Much attention has been paid to the study of active faults both in China and abroad. There is certain controversy concerning the implication of the term "active fault". Strictly speaking, the term should refer only to the faults that are still active in the present day. However, the term also usually refers to the faults which have been active continually or intermittently from the Quaternary (or the end of Tertiary) to the present day. We propose that the tones and the configurations of features on Landsat images are the principal keys to the interpretation of active faults. The faults, which display the most prominent
基金Projects(51978084, 51678073) supported by the National Natural Science Foundation of ChinaProject(2020JJ4605) supported by the Natural Science Foundation of Hunan Province, ChinaProject(2019IC13) supported by the International Cooperation and Development Project of Double First-Class Scientific Research in Changsha University of Science & Technology, China。
文摘To determine the distribution of active earth pressure on retaining walls, a series of model tests with the horizontally translating rigid walls are designed. Particle image velocimetry is used to study the movement and shear strain during the active failure of soil with height H and friction angle φ. The test results show that there are 3 stages of soil deformation under retaining wall translation: the initial stage, the expansion stage and the stability stage. The stable sliding surface in the model tests can be considered to be composed of two parts. Within the height range of 0.82 H-1.0 H, it is a plane at an angle of π/4+φ/2 to the horizontal plane. In the height range of 0-0.82 H, it is a curve between a logarithmic spiral and a plane at an angle of π/4+φ/2 to the horizontal. A new method applicable to any sliding surface is proposed for active earth pressure with the consideration of arching effect. The active earth pressure is computed with the actual shape of the slip surface and compared with model test data and with predictions obtained by existing methods. The comparison shows that predictions from the newly proposed method are more consistent with the measured data than the predictions from the other methods.
基金supported by the Key Projects of Shanghai Science and Technology on Biomedicine(NO.10DZ1950800)the Major Project of Shanghai Zhabei District Health Bureau (No. 2011ZD01)
文摘Objective:To reveal the neural network of active and passive hand movements. Method:Seven healthy aged people were checked, and acquired functional magnetic resonance imaging data on a 1.5T scanner. Active movement consisted of repetitive grasping and loosening of hand; passive movement involved the same movement performed by examiner. Both types of hand movements were assessed separately. These data were analysed by Statistical Parametric Mapping Microsoft. Result:The main activated brain areas were the contralateral supplemental motor area, primary motor area, primary sensory area and the ipsilateral cerebellum when subjects gripped right hands actively and passively. The supplemental area was less active in passive hand movement than active hand movement. The activated brain areas were mainly within Brodmann area 4 during active hand movement; in the contrast, the voxels triggered by passive movement were mainly within Brodmann areas 3,1,2 areas. Conclusion:The results suggest that the neural networks of passive and active tasks spared some common areas, and the passive movement could be as effective as active movement to facilitate the recovery of limbs motor function in patients with brain damage.
文摘从遥感影像上自动解译铁路设计控制要素是实现“一键成图”的关键,但深度学习遥感影像智能解译需要大量标注样本。依据铁路线路设计原则,提出一种多源遥感数据的设计控制要素智能解译样本库构建方法。首先,基于数字正射影像图(Digital Orthophoto Map,DOM)、数字线划地图(Digital Line Graphic,DLG)和激光雷达(Light Detection and Ranging,Lidar)点云多源数据自动生成初始样本;其次,基于增量主动学习迭代方法对初始样本进行优化,达到高质量、全面覆盖铁路沿线的目的;然后,以长赣铁路为例,构建以铁路沿线周边房屋、道路、水体和植被四类铁路线路设计控制要素为重点的高分辨率智能解译样本数据库——铁路线路设计控制要素智能解译样本库(Wuhan University Sample Database of Control Elements of Railway Route Design,WHU-RRDSD),其地面分辨率为0.1 m,样本总数超过20万张;最后,为验证样本库的可用性,分别从定性评价、定量评价以及其他场景应用案例三方面进行详细验证,结果表明,基于房屋、道路、水体和植被四类样本库的IoU评价指标分别为84.43%、82.38%、90.19%、90.28%,表现出优异的解译效果;基于WHU-RRDSD训练得到的智能模型迁移至宜涪高铁场景中房屋、道路、水体和植被要素的解译,验证样本库在其他场景的可用性;简要介绍基于WHU-RRDSD样本库进行的高分辨率遥感图像弱监督建筑提取和高分辨率遥感图像地物分类两个应用案例,进一步验证本文方法所构建样本库可用性。
