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Texture Feature Extraction for Land-cover Classification of Remote Sensing Data in Land Consolidation District Using Semi-variogram

机译：纹理特点提取用于使用半变型仪在土地固结区遥感数据遥感数据分类的特点提取

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The area of land consolidation projects are generally small, so remote sensing images used in land cover classification are generally of high resolution. The spectral characteristics of the high-resolution remote sensing data are unstable, while the texture feature is prominent. In view of this issue, this paper study the spatial relation between the adjacent pixels in the remote sensing image, and selected the lag distance of the semi-variogram that is determined when the value of the semi-variogram tended to be constant as the co-occurrence window size. Sometimes the window size is the most important influencing factor in the texture feature extraction process. Moreover, under the restraint of the classification results, this paper introduces a method to compute the co-occurrence features with a timely changeable co-occurrence window size according to the semi-variogram analysis. This paper takes Zhaoquanying land consolidation project located at Beijing Shunyi District as an example, the texture feature is extracted from SPOT5 remote sensing data of land consolidation project area in the TitanImage secondary development environment. The results show that the classification accuracy has improved.
机译：土地整理项目的面积一般较小，在土地覆盖分类使用，因此遥感影像通常高分辨率的。高分辨率遥感数据的光谱特性是不稳定的，而纹理特征是突出。鉴于这个问题，本文研究了遥感图像中的相邻像素之间的空间关系，并且选择所确定的半变差函数的滞后距离当半变差函数的值趋于恒定，作为助-occurrence窗口大小。有时，窗口大小处于纹理特征提取过程中，最重要的影响因素。此外，分类结果的约束下，本文介绍来计算同现的方法与根据半变差函数分析及时改变的共现窗口尺寸的特性。本文以位于北京顺义区为例赵全营镇土地整理项目，纹理特征是从SPOT5提取的TitanImage二次开发环境，土地整理项目区的遥感数据。结果表明，分类精度有所提高。

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