最小距离分类

实例表明用该方法对图象进行的分类与用最小距离分类得出的结果一致。

Experiment shows that the pattern classification results by this method agree to that by minimum distance classification method .

所用方法是图像处理分类技术中的最小距离分类方法。

By means of the minimum distance classification of digital image processing , the results obtained are satisfactory .

文中讨论了各种混合像元分解算法，选择最小距离分类法、光谱角分类法作为IIM的影像分析模型，将该方法用于CE-1的光谱反射率数据，得到的结果现有的理论是基本一致的。

Different kinds of algorithms of pixel unmixing are discussed and compared . The minimum distance and spectrum angle mapper method are chosen as analyzing model for IIM data . Results show that the mineral distribution in the image is basically identical to the theory known to people in nowadays .

一种多光谱遥感图象的自适应最小距离分类方法

Remote Sensing Image Classification Using an Adaptive Min Distance Algorithm

然后，以特征系数作为识别特征量，采用最小距离分类法，实现了自动目标识别。

Then , the feature coefficients are classified by the minimum distance criterion to recognize the target automatically .

利用这种思路，本文对一种比较简单的分类算法&自适应最小距离分类方法加以改进，并将其应用于多光谱遥感图像的分类中，提出了一种核函数的选择策略。

Based on this method , this paper improved a simple classification method called adaptive min-distance algorithm , and applied it to the classification of multi-band remote sensing images .

C均值法是计算机模式分类中一种重要的动态聚类方法，它主要是利用模式间最小距离原则进行分类，其分类效果受到模式的协方差阵和初始类心选取方法的影响。

C-means algorithm is an important dynamic clustering method in pattern classification . The patterns are classified by the principle of the minimum distances among patterns . The clustering effect of the C-means lies on the covariance matrix of the classes and the selecting method of the initial clustering centers .

为了将频谱对纹理特性的表征能力应用于遥感图像分类提出了基于频域最小距离遥感图像纹理分类算法。

To use the frequency spectrum characteristics in texture classification of remotely sensed imagery , a texture classification algorithm based on minimum distance in frequency space is put forward .

实验中用这三类滤波器组分解图像，然后提取纹理图像的特征量，使用最小距离分类器进行纹理分类并比较了这三类滤波器组的分解速度和分类正确率。

The numerical test decomposes texture images with these three wavelet filter banks , then extracts texture features and classifies texture based on the minimum distance classifier . This paper compares these three wavelet filter banks from the speed of decomposition and the accuracy of classification .