Extraction of golf course based on texture feature of SPOT5 image
Extraction of golf course based on texture feature of SPOT5 image
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DOI：
10.1109/icecc.2011.6067611
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发表时间：
2011-11
期刊：
影响因子：
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通讯作者：
C.-I Chen;Jianfei Chen;Xiaolin Zhang
中科院分区：
文献类型：
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作者：
C.-I Chen;Jianfei Chen;Xiaolin Zhang
基于2008年的SPOT5遥感影像，我们选择了两个高尔夫球场——一个位于深圳市区，另一个位于深圳林区，作为两个研究区域。首先，我们对数据压缩和增强几何信息这两个领域进行了主成分分析。其次，我们通过小波变换对图像进行处理并滤除噪声，并使用灰度共生矩阵对 SPOT5 图像的纹理进行分析。在此分析的基础上，我们选择了四个统计指标（对比度Contrast、均匀性、相关性和熵）。最后，通过人机解释的选择，我们选择了高尔夫球场图像分割和信息提取的最佳阈值。结果表明，与传统方法相比，纹理特征提取方法在高尔夫球场信息提取过程中能够更好地对土地类型进行分类，并提供更精确的结果。
Based on SPOT5 remote sensing image in 2008, we selected two golf courses — one in urban area of Shenzhen and the other in the forest area of Shenzhen, as the two study areas. First, we made principal components analysis of the two areas for data compression and enhancing geometric information. Second, we processed the image and filtered the noise by the wavelet transformation, and the textures of the SPOT5 images were analyzed using Gray Level Co-occurrence Matrices. Based on these analyses, we selected four statistic indexes (contrast Contrast, homogeneity, correlation and entropy.) Finally, with the selection by man-machine interpretation, we chose the optimal threshold for image segmentation and information extraction of the golf course. The results showed that the method of texture feature extraction is better in classifying the land types during information extraction of the golf course and providing more precise results compared to the traditional method.