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1.
一种四叉树和测地线活动轮廓模型相结合的海陆影像分割方法 总被引：1，自引：0，他引：1
海陆影像分割对于后续的海岸线提取、潮间带地形反演、海岸演化状况分析等都具有十分重要的意义。本文在分析了四叉树、测地线活动轮廓(GAC)模型和Canny边缘检测算子等在海陆影像分割中优缺点的基础上,提出了一种四叉树、Canny算子和GAC模型相结合的海陆影像分割方法。该方法综合利用上述各方法的优点,将Canny算子边缘检测结果融入到基于四叉树初分割的GAC模型中,重构边界停止函数,演化水平集方程,实现海陆影像分割。试验结果表明,该方法具有海陆影像分割速度快、精度高、可靠性强和自动化程度高等优点,对于弱边缘以及严重凹陷边缘,都能实现自动和准确分割。 相似文献
2.
基于数学形态学细化算子的改进Canny算法研究 总被引：3，自引：0，他引：3
边缘检测是图像处理领域研究的一个重要内容。本文基于数学形态学改进现有Canny算子。该方法首先用Canny算子进行滤波,然后采用非极大抑制技术,将强边缘图像和弱边缘图像的边缘进行连接,再对提取的边缘利用形态学细化算子细化。针对不同图像采用不同门限比例,可取得不同的边缘提取效果。本文采用峰值信噪比、均方误差、平均绝对误差三个评价指标对边缘检测算法的优越性进行度量。实验分析表明,本文算法优于Sobel算子、Roberts算子、Log算子及传统Canny算法。 相似文献
3.
由于SAR影像具有乘性相干斑噪声,某些对光学影像有较好检测性能的边缘检测算子,对于SAR影像并不适用,因此本文提出了一种将改进后的Ratio算子与Canny算子相组合的新方法。首先采用多种客观评价指标,得出经典方法中的Lee滤波能有效去除噪声,且最大限度保留了梯度信息,适用于边缘检测;然后对边缘检测具有恒虚警特性的Ratio算子进行改进,改进后的算子更利于影像归一化处理;最后利用改进后的Ratio算子与Canny算子组合成新方法。试验结果证明,组合后的新方法显著提高了边缘检测正确率和定位精度,改善了边缘连续性,具有一定的有效性和实用性。 相似文献
4.
由于人类识别图像特征涉及非线性的识别机制,本文提出了基于改进二维Log Butterworth滤波器的全方向边缘检测方法,该方法从频域角度出发,利用正反快速傅里叶变换来实现边缘检测工作。首先,将非线性Log函数引入Butterworth滤波器,获得二维Log Butterworth滤波器。当图像行列数不一致时,中心频率分布于椭圆之上,椭圆的长短轴之比与图像长宽比相等,进而给出以角度为变量滤波器表达式;其次,为方便滤波器参数的优化选取,本文对二维 Log Butterworth滤波器参数进行归一化等处理;再次,本文利用F-measure和PSNR (峰值信噪比)值来衡量不同参数下的边缘检测结果,确定最优的二维 Log Butterworth滤波器参数范围;然后,为了分析本文方法的边缘检测效率,对比了本文方法与空域算子（Canny算子）的乘法次数和加法次数,同时以不同大小的图像作为实验数据来比较两种方法的边缘检测耗时;最后,以BSDS（伯克利图像分割数据库）图像和高空间分辨率遥感图像为实验数据,对本文方法的边缘检测结果进行了评价分析。结果表明：本文方法可以有效地应用于图像边缘检测。 相似文献
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万雪 《武汉大学学报(信息科学版)》2012,37(2):145-148
提出了一种基于非局部极值抑制的Harris算子,实现了特征点和边缘线的联合提取。采用基于主方向的边缘跟踪,实现了边缘的矢量化存储。实验证明,改进后的Harris算子在特征点和边缘线联合提取时较原始算法效果更好。本算法采用一定的数学模型对边缘线进行拟合,从而进一步对边缘特征进行描述,有利于数据管理和后期的特征匹配。 相似文献
6.
无人机对电力线巡检的关键问题是如何从复杂背景的航拍图像中准确地提取电力线。本文提出了一种基于二维变分模态分解 (2D-VMD) 提取电力线的新算法。首先对原始航拍图像进行预处理,加快数据处理速度;然后采用2D-VMD算法对预处理后的图像进行分解,通过改进后的点锐度算法,选取带有电力线特征的IMF分量图,并利用Roberts算子进行边缘检测;最后利用形态学改进的Hough变换,完成对电力线的提取。试验结果表明,本文方法比传统的Canny算子结合Hough变换方法、LSD方法、Roberts 算法结合形态学改进的Hough变换方法更具精确性、抗噪性、自动化。 相似文献
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Rapid urbanization threatens urban green spaces and vegetation, demonstrated by a decrease in connectivity and higher levels of fragmentation. Understanding historic spatial and temporal patterns of such fragmentation is important for habitat and biological conservation, ecosystem management and urban planning. Despite their potential value, Local Indicators of Spatial Autocorrelation (LISA) measures have not been sufficiently exploited in monitoring the spatial and temporal variability in clustering and fragmentation of vegetation patterns in urban areas. LISA statistics are an important structural measure that indicates the presence of outliers, zones of similarity (hot spots) and of dissimilarity (cold spots) at proximate locations, hence they could be used to explicitly capture spatial patterns that are clustered, dispersed or random. In this study, we applied landscape metrics, LISA indices to analyse the temporal variability in clustering and fragmentation patterns of vegetation patches in Harare metropolitan city, Zimbabwe using Landsat series data for 1994, 2001 and 2017. Analysis of landscape metrics showed an increase in the fragmentation of vegetation patches between 1994–2017 as shown by the decrease in mean patch size, an increase in number of patches, edge density and shape complexity of vegetation patches. The study further demonstrates the utility of LISA indices in identifying key hot spot and cold spots. Comparatively, the highly vegetated northern parts of the city were characterised by significantly high positive spatial autocorrelation (p < 0.05) of vegetation patches. Conversely, more dispersed vegetation patches were found in the highly and densely urbanized western, eastern and southern parts of the city. This suggest that with increasing vegetation fragmentation, small and isolated vegetation patches do not spatially cluster but are dispersed geographically. The findings of the study underline the potential of LISA measures as a valuable spatially explicit method for the assessment of spatial clustering and fragmentation of urban vegetation patterns. 相似文献
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一种改进的Canny算子边缘检测算法 总被引：1，自引：0，他引：1
介绍Canny算子边缘检测的基本原理,并对其性能进行分析和评价。针对传统Canny算子在滤波过程中存在的缺陷,提出一种基于自适应平滑滤波的改进Canny边缘检测算子,该算法根据图像中像元灰度值的突变特性,自适应的改变滤波器的权值,在平滑图像的过程中使图像的边缘锐化。在计算梯度幅值的时候采用了邻域的梯度幅值计算方法,考虑了像素对角线方向的梯度计算,进一步抑制了噪声的影响。通过对实验图像的分析表明,改进的检测算法对图像边缘提取具有较好的检测精度和准确性。 相似文献
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《地理信息系统科学与遥感》2013,50(4):510-537
The spread of tamarisk (Tamarix spp., also known as saltcedar) is a significant ecological disturbance in western North America and has long been targeted for control, leading to the importation of the northern tamarisk beetle (Diorhabda carinulata) as a biological control agent. Following its initial release along the Colorado River near Moab, Utah in 2004, the beetle has successfully established and defoliated tamarisk across much of the upper Colorado River Basin. However, the spatial distribution and seasonal timing of defoliation are complex and difficult to quantify over large areas. To address this challenge, we tested and compared two remote sensing approaches to mapping tamarisk defoliation: Disturbance Index (DI) and a decision tree method called Random Forest (RF). Based on multitemporal Landsat 5 TM imagery for 2006-2010, changes in DI and defoliation probability from RF were calculated to detect tamarisk defoliation along the banks of Green, Colorado, Dolores and San Juan rivers within the Colorado Plateau area. Defoliation mapping accuracy was assessed based on field surveys partitioned into 10 km sections of river and on regions of interest created for continuous riparian vegetation. The DI method detected 3711 ha of defoliated area in 2007, 7350 ha in 2008, 10,457 ha in 2009 and 5898 ha in 2010. The RF method detected much smaller areas of defoliation but proved to have higher accuracy, as demonstrated by accuracy assessment and sensitivity analysis, with 784 ha in 2007, 960 ha in 2008, 934 ha in 2009, and 1008 ha in 2010. Results indicate that remote sensing approaches are likely to be useful for studying spatiotemporal patterns of tamarisk defoliation as the tamarisk leaf beetle spreads throughout the western United States. 相似文献
11.
Although traditional cellular automata (CA)‐based models can effectively simulate urban land‐use changes, they typically ignore the spatial evolution of urban patches, due to their use of cell‐based simulation strategies. This research proposes a new patch‐based CA model to incorporate a spatial constraint based on the growth patterns of urban patches into the conventional CA model for reducing the uncertainty of the distribution of simulated new urban patches. In this model, the growth pattern of urban patches is first estimated using a developed indicator that is based on the local variations in existing urban patches. The urban growth is then simulated by integrating the estimated growth pattern and land suitability using a pattern‐calibrated method. In this method, the pattern of new urban patches is gradually calibrated toward the dominant growth pattern through the steps of the CA model. The proposed model is applied to simulate urban growth in the Tehran megalopolitan area during 2000–2006–2012. The results from this model were compared with two common models: cell‐based CA and logistic‐patch CA. The proposed model yields a degree of patch‐level agreement that is 23.4 and 7.5% higher than those of these pre‐existing models, respectively. This reveals that the patch‐based CA model simulates actual development patterns much better than the two other models. 相似文献
12.
中国西部地壳现今变形特征及其机制探讨 总被引：7，自引：0，他引：7
以中国大陆及周边10年来近400个GPS测站的复测资料为基础，获取并绘制了现今地壳水平运动与变形图像，结果表明：青藏块体内西半部南北向只存在微弱的缩短变化（5mm/a左右），东半部南北向则没有缩短的迹象，南北缩短的区域主要位于青藏块体南缘的喜马拉雅条带（约15mm/a），北缘中部的柴达木西区（约15mm/a）和青藏块体北面的天山块体及周围地区（约15mm/a）；青藏块体大约有10mm/a的东西拉张，但不均匀，自西向东经历了由弱到强再有所减弱的过程，整个西部地区东边缘的东向运动表现为南强北弱的左旋特征；川滇菱形块体不是逃逸块体而是变形块体；青藏块体东缘及附近地区是东向运动的消减区带，面应变结果显示，青藏块体周边以面收缩为主，内部则以面膨胀为主；其以北的地区以面收缩为主（但压中有张），面应变的量级为10^-8/a。这样的变形结果，若只靠来处于印度板块的北向挤压是无法解释的。由此并结合最新的地球物理研究成果可推断或证实，地壳下部来自南向的物质涌入是控制青藏块体乃至中国大陆形变的另一大动力源，甚至可能是主导动力源；或者具有深、浅立体活动的某种协同性。 相似文献
13.
基于边缘特征的变化检测方法研究 总被引：5，自引：0，他引：5
提出了一种结合边缘特征和灰度信息的变化检测方法．该检测方法对于检测线状目标变化具有较好的效果，同时也能减小由于配准而产生的误差。试验结果表明了该方法的可行性。 相似文献
14.
利用多光谱图像的两像素点灰度差异强度指数,定量分析了多光谱图像的边缘特征及响应特征差异,指出传统灰度算子会导致弱边缘特征漏检。为减小边缘响应强度差异,在光谱特征空间中分别利用特征值和特征向量表征多光谱图像的梯度变化大小和方向,同时采用B样条小波对影像进行多尺度变换,获取不同尺度的边缘特征。实验结果表明,此方法对多光谱图像检测出的边缘特征响应均匀且较为显著,综合多尺度边缘能准确检测并定位边缘点,且能有效地抑制噪声。 相似文献
15.
基于灰度形态学的高分辨率遥感图像边缘检测研究 总被引：2，自引：0，他引：2
提出一种用于高分辨率遥感图像边缘检测的灰度形态学方法,该算法在数学形态学的基础上,针对图像中噪声和边缘形态的不同建立了多结构元素,利用灰度形态变换原理的膨胀、腐蚀、开启和闭合等算法进行边缘提取。实验表明,与经典的边缘检测算子相比,该算法保持图像的细节特征,较好地解决边缘检测精度与抗噪声性能的协调问题,具有很好的边缘提取能力。本文给出了用Matlab6.5实现的具体代码。 相似文献
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Spatiotemporal filtering for regional GPS network in China using independent component analysis 总被引：2，自引：0，他引：2
Removal of the common mode error (CME) is a routine procedure in postprocessing regional GPS network observations, which is commonly performed using principal component analysis (PCA). PCA decomposes a network time series into a group of modes, where each mode comprises a common temporal function and corresponding spatial response based on second-order statistics (variance and covariance). However, the probability distribution function of a GPS time series is non-Gaussian; therefore, the largest variances do not correspond to the meaningful axes, and the PCA-derived components may not have an obvious physical meaning. In this study, the CME was assumed statistically independent of other errors, and it was extracted using independent component analysis (ICA), which involves higher-order statistics. First, the ICA performance was tested using a simulated example and compared with PCA and stacking methods. The existence of strong local effects on some stations causes significant large spatial responses and, therefore, a strategy based on median and interquartile range statistics was proposed to identify abnormal sites. After discarding abnormal sites, two indices based on the analysis of the spatial responses of all sites in each independent component (east, north, and vertical) were used to define the CME quantitatively. Continuous GPS coordinate time series spanning \(\sim \)4.5 years obtained from 259 stations of the Tectonic and Environmental Observation Network of Mainland China (CMONOC II) were analyzed using both PCA and ICA methods and their results compared. The results suggest that PCA is susceptible to deriving an artificial spatial structure, whereas ICA separates the CME from other errors reliably. Our results demonstrate that the spatial characteristics of the CME for CMONOC II are not uniform for the east, north, and vertical components, but have an obvious north–south or east–west distribution. After discarding 84 abnormal sites and performing spatiotemporal filtering using ICA, an average reduction in scatter of 6.3% was achieved for all three components. 相似文献
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一种基于相位一致的高分辨率遥感图像特征检测方法 总被引：3，自引：0，他引：3
精确检测图像边缘特征是进行高分辨率遥感图像分割和识别的关键。空域特征检测算子以解决阶跃形边缘为主,得到的边缘特征对图像的亮度和对比度敏感。本文引入了一种基于频域相位一致的图像特征检测方法,该方法对遥感图像亮度和对比度具有不变性,同时适用于多种边缘特征的检测。使用Log Gabor小波计算IKONOS Pan图像的相位一致多尺度梯度,对农田、道路和厂房等典型地物进行特征检测的结果表明,相位一致算法对图像局部亮度和对比度不敏感;并且对线形物体产生单线响应,不似空域检测算子产生双线响应。最后考察滤波器尺度和方向参数变化及添加高斯噪声对检测结果的影响,发现相位一致算法无需先使用低通滤波去除噪声,因而具有更稳定的特征定位精度;并且抗噪声干扰的能力强,检测结果不会因为噪声而出现波动。基于相位一致的遥感图像不变特征提取,为高分辨率遥感图像的分割和对象识别提供了基础。 相似文献
18.
以城市区域内高大建筑阴影为研究对象,针对现有的阴影检测算法在复杂地物环境下检测精度和可靠性不高的问题,提出了一种结合颜色空间特征和空间关系的遥感影像阴影检测方法。首先,采用SLIC超像素算法对影像进行分割;然后基于Lab和HSI颜色空间构建初步检测条件,将阴影划分为阴影主体区域和待检测区域;最后,借助Canny边缘检测信息合并待判别区域内的超像素块,并利用阴影区域与造成干扰区域间的空间位置关系构建的检测条件进行判别。实验结果表明,该方法可以有效提高复杂地物环境下遥感影像阴影的检测精度和算法可靠性。 相似文献
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针对目前高分辨率遥感影像变化检测算法对于光谱变化过敏感问题,本文提出了一种基于超像素分割与条件随机场（CRF）的遥感影像变化检测算法。首先采用空间约束的t混合模型驱动的分割模型,获得同质性超像素块,实现良好的边界附着性和亮度均匀性。然后计算分割得到的双时相影像块之间的特征差异性,获取变化幅度图像。最后利用模糊聚类算法（FCM）对变化幅度图像进行聚类,得到隶属度图像作为CRF一阶势,并利用光谱-空间相似度约束的函数构建CRF二阶势。试验结果表明,与现有方法相比,该方法检测精度可提高5%,错检率和漏检率可降低3%,能较好地应对输入图像的光谱变化,并保持变化检测结果的边缘细节。 相似文献