Kernel-Based MPCM Algorithm with Spatial Constraints and Local Contextual Information for Mapping Paddy Burnt Fields

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.

Automated summary

This research aims to improve the accuracy of remote sensing image classification by integrating spatial constraints and local contextual information into the modified Possibilistic c-Means algorithm, primarily for extracting burnt paddy fields. The results indicate that the fuzzy learning algorithm achieves higher accuracy when combined with spatial constraints and local contextual information.