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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 (Sir)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. [ABSTRACT FROM AUTHOR]

In this study, temporal MODIS-Terra MOD13Q1 data have been used for identification of wheat crop uniquely, using the noise clustering (NC) soft classification approach. This research also optimises the selection of date combination and vegetation index for classification of wheat crop. First, a separability analysis is used to optimise the date combination for each case of number of dates and vegetation index. Then, these scenes have undergone for NC soft classification. The resolution parameter (δ) was optimised for the NC classifier and found to be a value of 1.6 × 104for wheat crop identification. Classified outputs were analysed by receiver operating characteristics (ROC) analysis for sub-pixel detection. Highest area under the ROC curve was found for soil-adjusted vegetation index corresponding to the three different phenological stages data sets. From this study, the data sets corresponding to the Sowing, Flowering and Maturity phenological stages of wheat crop were found more suitable to identify it uniquely. [ABSTRACT FROM PUBLISHER]