Abstract

In this research work, non-linearity in data has been handled by incorporating kernel with the Modified Possibilistic c-Means (MPCM) algorithm. Nine different types of kernel function have been proposed to classify nine different classes and have non-linearity among them. Gaussian has been identified as the best performing kernel at an optimized fuzzified value m = 1.5 with an overall accuracy 92.45%. The composite kernels have been generated with an aim of improvement of accuracy in comparison to single kernel. Further, role of spatial constraints has been analyzed by adding neighboring pixel information to handle the noise. It was observed that overall accuracy depends on the spatial parameter that has been included. Thus, local information having local similarity measure parameters (S_ir)in the image produces highest accuracy compared to others. Moreover, the identified best kernel (Gaussian Kernel) has been then used for extraction of the burnt paddy fields in a different test site.

摘要

在这项研究工作中，通过将内核与改进的可能性 c 均值 (MPCM) 算法相结合来处理数据中的非线性。已经提出了九种不同类型的核函数来对九个不同的类进行分类，并且它们之间具有非线性。Gaussian 已被确定为在优化的模糊化值 m = 1.5 时性能最佳的内核，总体准确度为 92.45%。生成复合内核的目的是与单内核相比提高准确性。此外，通过添加相邻像素信息来处理噪声，分析了空间约束的作用。据观察，整体精度取决于已包含的空间参数。因此，具有局部相似性度量参数的局部信息（S _ir) 与其他图像相比，图像中的精度最高。此外，确定的最佳内核（高斯内核）已被用于提取不同测试地点的烧毁稻田。