In remote sensing images, isolated pixels in the form of salt-and-pepper noisy pixels deteriorates the image classification results. To handle these noisy pixels, spatial constraints along with local contextual information were added in Gaussian kernel-based Modified Possibilistic c-Means (MPCM) algorithm. The spatial constraints added in MPCM results as MPCM-S (MPCM with spatial constraints) which is used as base classifier added with Modified Possibilistic Spatial constraint Local Information c-Means (MPSLICM) and Adaptive Modified Possibilistic Spatial constraint Local Information c-Means (ADMPSLICM). Along with the ability to handle noise and nonlinearity in the data, this research work aims at using these algorithms for extraction of paddy burnt fields as a single land cover class. The cross-validation of results has been done using Normalized Burnt Ratio (NBR). Further, a quantitative assessment for the recognition of paddy stubble burnt field was done using Mean Membership Difference (MMD) method from soft classified output. From this research work, it was finally concluded that fuzzy learning algorithm gave higher accuracy and performs better when local contextual information is incorporated along with spatial constraint.

在遥感图像中，盐和胡椒噪声像素形式的孤立像素会使图像分类结果变差。为了处理这些嘈杂的像素，在基于高斯核的改进的可能c均值（MPCM）算法中添加了空间约束以及局部上下文信息。MPCM中添加的空间约束为MPCM-S（具有空间约束的MPCM），它用作基础分类器，并添加了修正的可能空间约束局部信息c-均值（MPSLICM）和自适应的修正的可能性空间约束局部信息c-均值（ADMPSLICM）。除了能够处理数据中的噪声和非线性外，这项研究工作还旨在使用这些算法来提取稻田中的稻田作为单个土地覆盖类别。结果的交叉验证已使用归一化燃烧比（NBR）完成。此外，使用平均成员差异（MMD）方法从软分类输出中进行了对稻茬燃烧田地识别的定量评估。通过这项研究工作，最终得出结论，当将局部上下文信息与空间约束结合在一起时，模糊学习算法具有较高的准确性，并且性能更好。