论文标题
使用GLCM功能和共同信息的新滤波器降低维度和分类的新过滤器
A new filter for dimensionality reduction and classification of hyperspectral images using GLCM features and mutual information
论文作者
论文摘要
降低维度是高光谱图像分类（HSI）的重要预处理步骤，这是不可避免的任务。一些方法使用基于光谱和空间信息的特征选择或提取算法。在本文中，我们介绍了一种基于相互信息的光谱和空间信息，以考虑降低维度和分类的新方法。我们通过从灰度辅助矩阵（GLCM）提取的纹理特征来表征空间信息。我们使用同质性，对比度，相关性和能量。对于分类，我们使用支持向量机（SVM）。实验是在三个众所周知的高光谱基准数据集上进行的。将提出的算法与最先进的方法进行了比较。获得的融合结果表明，我们的方法通过在良好的时机中提高分类精度来优于其他方法。可以改进此方法以获得更多性能 关键字：高光谱图像；分类;光谱和空间特征；灰度合作矩阵； glcm;相互信息；支持向量机； SVM。
Dimensionality reduction is an important preprocessing step of the hyperspectral images classification (HSI), it is inevitable task. Some methods use feature selection or extraction algorithms based on spectral and spatial information. In this paper, we introduce a new methodology for dimensionality reduction and classification of HSI taking into account both spectral and spatial information based on mutual information. We characterise the spatial information by the texture features extracted from the grey level cooccurrence matrix (GLCM); we use Homogeneity, Contrast, Correlation and Energy. For classification, we use support vector machine (SVM). The experiments are performed on three well-known hyperspectral benchmark datasets. The proposed algorithm is compared with the state of the art methods. The obtained results of this fusion show that our method outperforms the other approaches by increasing the classification accuracy in a good timing. This method may be improved for more performance Keywords: hyperspectral images; classification; spectral and spatial features; grey level cooccurrence matrix; GLCM; mutual information; support vector machine; SVM.
