基于无人机和卫星遥感影像的制种玉米田识别纹理特征尺度优选
发布时间：2019-07-16 14:51
【摘要】：制种玉米田在高空间分辨率遥感影像上呈现的明显条带状纹理,是有效区分光谱值相近的大田玉米和制种玉米的重要信息。该文在新疆维吾尔自治区奇台县玉米种植区以高空间分辨率的无人机遥感影像为数据源,针对制种玉米识别的纹理特征计算尺度问题,首先采用最近邻内插法对制种玉米和大田玉米样本田块的无人机影像进行重采样,得到不同分辨率的样本;然后用融合Uniform-LBP(local binary pattern)和GLCM(gray level co-occurrence matrix)方法得到提取玉米田块纹理特征合理GLCM参数,其中方向参数为0°、45°、90°和135°这4个方向上的纹理特征值的平均值、距离为5~7像元、灰度级为8;通过多尺度对比分析,得到最适宜区分制种玉米与大田玉米的纹理辨率为0.6~0.9 m。最后采用奇台县的0.7 m分辨率的Kompsat-3遥感影像进行验证,在多时相EVI(enhanced vegetation index)光谱信息识别玉米的基础上,利用本文确定的纹理分析方法,通过决策树建立规则识别制种玉米,识别精度达90.9%。通过该文的研究,可为高空间分辨率遥感制种玉米田监管提供支撑。
[Abstract]:The obvious banded texture of seed production corn field on the remote sensing image of high altitude resolution is an important information for effectively distinguishing field corn with similar spectral value from seed production corn. In this paper, the UAV remote sensing image with high spatial resolution is used as the data source in the corn growing area of Qitai County, Xinjiang Uygur Autonomous region. Aiming at the texture feature calculation scale of seed corn recognition, the UAV image of seed production corn and field corn sample field is resampled by nearest neighbor interpolation method, and the samples with different resolutions are obtained. Then the reasonable GLCM parameters of extracting texture features of corn field were obtained by fusion Uniform-LBP (local binary pattern) and GLCM (gray level co-occurrence matrix), in which the average value of texture eigenvalues in four directions was 0 掳, 45 掳, 90 掳and 135 掳, the distance was 5 鈮，
本文编号：2515131
[Abstract]:The obvious banded texture of seed production corn field on the remote sensing image of high altitude resolution is an important information for effectively distinguishing field corn with similar spectral value from seed production corn. In this paper, the UAV remote sensing image with high spatial resolution is used as the data source in the corn growing area of Qitai County, Xinjiang Uygur Autonomous region. Aiming at the texture feature calculation scale of seed corn recognition, the UAV image of seed production corn and field corn sample field is resampled by nearest neighbor interpolation method, and the samples with different resolutions are obtained. Then the reasonable GLCM parameters of extracting texture features of corn field were obtained by fusion Uniform-LBP (local binary pattern) and GLCM (gray level co-occurrence matrix), in which the average value of texture eigenvalues in four directions was 0 掳, 45 掳, 90 掳and 135 掳, the distance was 5 鈮，
本文编号：2515131
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