Spectrum Classification of Easily Confused Ground Objects in ALI Remote Sensing Image Based on Texture Features

机译：基于纹理特征的ALI遥感图像中容易混淆地对象的光谱分类

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According to the problem that the classification result of shrub and forest land was easy to confuse when used spectrum of Advanced Land Image (ALI) to classify. This paper used the Meijiang River watershed as the study area. Used the Principal Component Analysis (PCA) to reduce dimension, taken the Contrast, Second moment, Mean and Dissimilarity as the texture values, and extracted the texture by Gray level co-occurrence matrix (GLCM). The texture features extracted from different window sizes were used the Maximum likelihood method to classify, and chosen the texture features extracted by the most suitable window size to join the classification. The research result shows that the texture features extracted by window size of 11×11 can distinguish well the two easily ground objects; moreover, the overall accuracy of classification used texture and spectrum features reached to 87.55%, which is 4.4% higher than the classification with spectrum.

机译：根据灌木和森林土地的分类结果，当使用频谱的高级陆地形象（ALI）进行分类时易于混淆。本文使用了梅江流域作为研究区。使用主成分分析（PCA）来减少尺寸，采取对比度，第二矩，均值和异化，作为纹理值，并通过灰度级共出矩阵（GLCM）提取纹理。从不同窗口尺寸提取的纹理功能使用最大似然方法来分类，并选择最适合窗口大小提取的纹理特征以加入分类。研究结果表明，窗口大小提取的纹理特征11×11可以区分两个易于接地物体;此外，使用纹理和谱特征的总体精度达到87.55％，比频谱分类高4.4％。

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《International Conference on Energy, Environment and Sustainable Development》|2013年||共6页
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Lingling Zhang; Geying Lai; Xianggui Zeng; Fazhao YI;
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中图分类 X1-532;
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Meijiang River watershed; Texture features; GLCM; Maximum likelihood method;

机译：梅江流域;纹理特点;glcm;最大似然方法;

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机译：遥感图像分类的纹理特征

Spectrum Classification of Easily Confused Ground Objects in ALI Remote Sensing Image Based on Texture Features

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