建筑物多边形高精度识别的傅里叶形状描述子神经网络方法
形状识别是地图空间认知的重要内容之一, 结合有效的形状特征向量提取方法和空间认知试验的神经网络方法是提高形状识别的有效途径。本文构建了一种融合了圆形度、偏心率和矩形度等宏观形状特征参量的傅里叶形状描述子作为形状特征向量的神经网络建筑多边形状识别器。首先, 利用傅里叶变换和计算几何方法分别提取建筑多边形的傅里叶形状描述子及圆形度、偏心率、矩形度参量, 并组成形状特征向量。然后, 通过样本数据的训练实现了建筑多边形与形状模板之间匹配的神经网络识别器。结果表明, 本文方法相较于以往的方法大幅度提高了精度(达到98.7%), 而且特征提取算法不受多边形点数不一致的限制。通过对武汉、郑州两大城市的真实建...
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| Published in | Ce hui xue bao Vol. 51; no. 9; pp. 1969 - 1976 |
|---|---|
| Main Authors | , , , |
| Format | Journal Article |
| Language | Chinese English |
| Published |
Beijing
Surveying and Mapping Press
01.09.2022
华中师范大学城市与环境科学学院,湖北 武汉 430079%武汉大学资源与环境科学学院,湖北 武汉 430079 华中师范大学地理过程分析与模拟湖北省重点实验室,湖北 武汉 430079 |
| Subjects | |
| Online Access | Get full text |
| ISSN | 1001-1595 1001-1595 |
| DOI | 10.11947/j.AGCS.2022.20210730 |
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| Abstract | 形状识别是地图空间认知的重要内容之一, 结合有效的形状特征向量提取方法和空间认知试验的神经网络方法是提高形状识别的有效途径。本文构建了一种融合了圆形度、偏心率和矩形度等宏观形状特征参量的傅里叶形状描述子作为形状特征向量的神经网络建筑多边形状识别器。首先, 利用傅里叶变换和计算几何方法分别提取建筑多边形的傅里叶形状描述子及圆形度、偏心率、矩形度参量, 并组成形状特征向量。然后, 通过样本数据的训练实现了建筑多边形与形状模板之间匹配的神经网络识别器。结果表明, 本文方法相较于以往的方法大幅度提高了精度(达到98.7%), 而且特征提取算法不受多边形点数不一致的限制。通过对武汉、郑州两大城市的真实建筑物数据进行形状识别, 证实该方法具有较好的识别效果。 |
|---|---|
| AbstractList | 形状识别是地图空间认知的重要内容之一, 结合有效的形状特征向量提取方法和空间认知试验的神经网络方法是提高形状识别的有效途径。本文构建了一种融合了圆形度、偏心率和矩形度等宏观形状特征参量的傅里叶形状描述子作为形状特征向量的神经网络建筑多边形状识别器。首先, 利用傅里叶变换和计算几何方法分别提取建筑多边形的傅里叶形状描述子及圆形度、偏心率、矩形度参量, 并组成形状特征向量。然后, 通过样本数据的训练实现了建筑多边形与形状模板之间匹配的神经网络识别器。结果表明, 本文方法相较于以往的方法大幅度提高了精度(达到98.7%), 而且特征提取算法不受多边形点数不一致的限制。通过对武汉、郑州两大城市的真实建筑物数据进行形状识别, 证实该方法具有较好的识别效果。 P208; 形状识别是地图空间认知的重要内容之一,结合有效的形状特征向量提取方法和空间认知试验的神经网络方法是提高形状识别的有效途径.本文构建了一种融合了圆形度、偏心率和矩形度等宏观形状特征参量的傅里叶形状描述子作为形状特征向量的神经网络建筑多边形状识别器.首先,利用傅里叶变换和计算几何方法分别提取建筑多边形的傅里叶形状描述子及圆形度、偏心率、矩形度参量,并组成形状特征向量.然后,通过样本数据的训练实现了建筑多边形与形状模板之间匹配的神经网络识别器.结果表明,本文方法相较于以往的方法大幅度提高了精度(达到98.7％),而且特征提取算法不受多边形点数不一致的限制.通过对武汉、郑州两大城市的真实建筑物数据进行形状识别,证实该方法具有较好的识别效果. |
| Author | 刘鹏程 杨敏 黄欣 马宏然 |
| AuthorAffiliation | 华中师范大学地理过程分析与模拟湖北省重点实验室,湖北 武汉 430079;华中师范大学城市与环境科学学院,湖北 武汉 430079%武汉大学资源与环境科学学院,湖北 武汉 430079 |
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| Author_FL | HUANG Xin LIU Pengcheng YANG Min MA Hongran |
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| Copyright | Sep 2022. This work is published under https://creativecommons.org/licenses/by-nc-nd/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. Copyright © Wanfang Data Co. Ltd. All Rights Reserved. |
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| Keywords | 神经网络 傅里叶形状描述子 建筑多边形形状识别 模板匹配 |
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| Snippet | 形状识别是地图空间认知的重要内容之一, 结合有效的形状特征向量提取方法和空间认知试验的神经网络方法是提高形状识别的有效途径。本文构建了一种融合了圆形度、偏心率和矩... P208; 形状识别是地图空间认知的重要内容之一,结合有效的形状特征向量提取方法和空间认知试验的神经网络方法是提高形状识别的有效途径.本文构建了一种融合了圆形度、偏心率... |
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| SubjectTerms | Algorithms Cognition Cognitive ability Computational geometry Computer applications Eccentricity Entropy (Information theory) Feature extraction Fourier transforms Neural networks Parameters Polygons Roundness Shape Shape recognition |
| Title | 建筑物多边形高精度识别的傅里叶形状描述子神经网络方法 |
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