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边缘检测是图像识别的中心环节,快速、精确地提取图像物体的边缘信息一直是国内外研究的热点。本文介绍了Roberts算子、Prewitt算子、Sobel算子、LOG算子和Canny算子等经典边缘检测算子,对其性能和算法特点进行分析。运用Matlab进行算法的仿真,并对其检测结果进行分析和比较。得出Sobel算子、Prewitt算子检测斜向阶跃边缘效果较好,Roberts算子检测水平和垂直边缘效果较好,LOG算子和canny算子提取的边缘比较完整,位置比较准确,并能够检测出图像较细的边缘部分。为进一步研究图像边缘检测理论奠定了一定的基础。 相似文献
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基于色差的遥感影像海岸线提取 总被引:9,自引:0,他引:9
分析了Canny算子边缘检测的基本原理和存在的问题,介绍了彩色空间及计算方法.根据海岸地带遥感影像特征,探讨了基于色差的Canny算子自适应边缘提取算法.实验表明,改进的算子具有较高信噪比,能达到比较理想的海岸线检测效果. 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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基于Canny理论的彩色图像边缘检测 总被引:4,自引:0,他引:4
解振东 《物探化探计算技术》2007,29(4):370-372
选用定位准确的Canny算子,提出一种基于Canny理论的彩色图像边缘检测方法。彩色图像的梯度幅值用其r、g、b分量的梯度幅值之和计算,方向角用r、g、b分量的y方向梯度幅值之和与r、g、b分量的x方向梯度幅值之和的比来确定。从检测结果中可以看出,基于Can-ny理论的彩色图像边缘检测能检测出更多的边缘细节。这说明,用该方法检测彩色图像的边缘是有效的。 相似文献
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Since the collapse of the Soviet Union, the crop cultivation structure in the Aral Sea Basin has changed dramatically, and these changes are worth studying. However, historical crop remote sensing mapping at the watershed scale remains challenging, especially crop misclassification at the cropland edge due to mixed pixels. Therefore, we proposed a field segmentation approach to constrain field edges based on time-series Sentinel-2 remote sensing images and the Google Earth Engine platform and then employed the random forest algorithm to perform crop classification based on time series Landsat/Sentinel-2 images and crop phenology information to produce historical crop maps in the Aral Sea Basin from the 1990s onward. The results showed that the intersection over union between the extracted field edges and in situ-measured field size data was 0.65. The overall accuracy of crop mapping was 95.2% in 2019. Then, we extended our method to historical mapping over the 1991–2015 period with accuracies ranging from 82.8% to 91.3%. Moreover, our method applied to historical mapping works well in terms of accuracy and policy matching. These findings indicate that our method can accurately distinguish cropland edges to reduce classification errors due to mixed pixels. This method is promising for solving the cropland edge problem for historical crop mapping in the Aral Sea Basin and can potentially provide a reference for historical crop classification in other watersheds of the world. 相似文献
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