For improving image attributes and increase robustness to handle the noise for image classification, fuzzy algorithms were introduced. The fuzzy c-means (FCM) algorithm has not been suggested to incorporate spectral and spatial neighborhood information for accomplishing image classification. In this study, evolution developed for overall accuracy (OA) calculated by FERM (fuzzy error matrix) technique for an algorithm, namely fuzzy local information c-means (FLICM) using base classifier FCM, adaptive fuzzy local information c-means (ADFLICM) using base classifier FCM and fuzzy c-means with constraints. For these algorithms, various distance measures and weighting components (m) are studied. This study provides a comparison among different forms of local convolution with an FCM classifier, with a combination of different distance measures and weighting components to select the best classification approach. FLICM using base classifier FCM and Euclidean distance measures with a weighting component (m) of 1.3 has resulted in the highest overall accuracy compared to other classifiers used with various distance measures.

为了改善图像属性并提高鲁棒性以处理噪声以进行图像分类，引入了模糊算法。还没有建议使用模糊c均值（FCM）算法来合并光谱和空间邻域信息以完成图像分类。在这项研究中，进化由FERM（模糊误差矩阵）技术一种算法，即模糊本地信息来计算总体准确度（OA）开发Ç使用基础分类器FCM，自适应模糊本地信息-means（FLICM）Ç -means（ADFLICM）使用基本分类器FCM和带有约束的模糊c均值。对于这些算法，各种距离度量和加权分量（m）进行研究。这项研究使用FCM分类器对不同形式的局部卷积进行了比较，并结合了不同的距离度量和加权分量以选择最佳分类方法。使用基本分类器FCM和加权分量（m）为1.3的欧几里得距离度量的FLICM与使用各种距离度量的其他分类器相比，具有最高的总体准确性。