地震研究

选取新疆阿图什市下辖的琼哈拉峻村为研究区,将小型旋翼无人机拍摄的图像作为数据源,分别采用面向对象以及面向像元2种影像分析方法对研究区的房屋建筑进行提取,并对2种算法的提取结果进行对比,分析了各自的优势。结果表明:面向对象方法可以有效地去除椒盐噪声对分类带来的影响,保证房屋形态的完整性,但影响内部相似的光谱、纹理信息若对应多种物体则会导致影像对象的错分。在面向像元的提取方法中加入了改进的数学形态学算法,可有效的抑制椒盐噪声,保持建筑物边缘的连续性与完整性,较好地解决了面向对象方法中部分农田与房屋出现错分的问题。

Taking Qiongharajun Village in Atushi City,Xinjiang as the research area,using the image taken by the small-scale rotorcraft UAV as the data source,we extract the housing buildings in the research area by object-oriented and pixel-oriented image analysis methods,compared the results of the two algorithms,and analyzed their respective advantages. The results show that the object-oriented method can effectively eliminate the impact of salt and pepper noise on classification and ensure the integrity of the housing morphology. However, if the similar spectral and texture information in the image corresponds to a variety of ground objects, the image object will be misclassified. The improved mathematical morphology algorithm is added to the pixel-oriented extraction method, which can effectively suppress salt and pepper noise, maintain the continuity and integrity of the building edge, and solve the problem of some farmland and houses in the object-oriented method.

引言
1 数据来源及影像拼接
2 研究方法
3 实验过程
4 讨论与结论

图1 哈拉峻乡Hillshade图(a)和无人机飞行路线规划图(b)<br/>Fig.1 Hillshade map of Halajun Township(a)and UAV flight route planning map(b)

图1 哈拉峻乡Hillshade图(a)和无人机飞行路线规划图(b)
Fig.1 Hillshade map of Halajun Township(a)and UAV flight route planning map(b)

图2 拼接而成的研究区DOM图<br/>Fig.2 Mosaic DOM diagram of study area

图2 拼接而成的研究区DOM图
Fig.2 Mosaic DOM diagram of study area

图3 膨胀原理(a)与腐蚀原理(b)示意图<br/>Fig.3 Sketch map of tprinciples of expansion(a) and corrosion(b)

图3 膨胀原理(a)与腐蚀原理(b)示意图
Fig.3 Sketch map of tprinciples of expansion(a) and corrosion(b)

图4 基于重建的膨胀原理<br/>Fig.4 Expansion principle based on reconstruction

图4 基于重建的膨胀原理
Fig.4 Expansion principle based on reconstruction

图5 开操作与闭操作原理<br/>Fig.5 Principles of open and close operations

图5 开操作与闭操作原理
Fig.5 Principles of open and close operations

图6 图像分割结果(a)和基于面向对象的分类结果(b)<br/>Fig.6 Image segmentation results(a)and object-oriented classification results(b)

图6 图像分割结果(a)和基于面向对象的分类结果(b)
Fig.6 Image segmentation results(a)and object-oriented classification results(b)

图7 原始影像的频数分布直方图(a)和前景图像(b)<br/>Fig.7 Frequency distribution histogram of original image(a)and foreground image(b)

图7 原始影像的频数分布直方图(a)和前景图像(b)
Fig.7 Frequency distribution histogram of original image(a)and foreground image(b)

图8 基于形态学图像处理的算法流程图<br/>Fig.8 Flow chart of algorithm based on morphological image processing

图8 基于形态学图像处理的算法流程图
Fig.8 Flow chart of algorithm based on morphological image processing

图9 加入形态学去噪后的边缘检测结果(a)及面向像元的房屋提取结果(b)<br/>Fig.9 Edge detection results after adding morphological denoising(a)and house extraction results based on pixel-oriented method(b)

图9 加入形态学去噪后的边缘检测结果(a)及面向像元的房屋提取结果(b)
Fig.9 Edge detection results after adding morphological denoising(a)and house extraction results based on pixel-oriented method(b)

图10 地表真实数据<br/>Fig.10 Real data on surface

图10 地表真实数据
Fig.10 Real data on surface

表1 分类结果的精度评价<br/>Tab.1 Accuracy evaluation of classification results

表1 分类结果的精度评价
Tab.1 Accuracy evaluation of classification results

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备注

引言

1 数据来源及影像拼接

2 研究方法

3 实验过程

4 讨论与结论

期刊信息