Handling heterogeneity within castor crop fields through “Individual Sample as Mean” training parameter approach

Abstract

.The high temporal resolution data has a great opportunity for specific crop mapping. The recent advancement in remote sensing data had a good impact on crop classification and mapping. In this research work, modified possibilistic c-means (MPCM) fuzzy machine learning approach has been applied for castor crop mapping, using sentinel 2A/2B satellite temporal images and local convolution as adaptive modified possibilistic local information c-means (ADMPLICM) algorithm was applied to remove isolated unwanted pixels. In training sample parameter “individual sample as mean” method was used to handle heterogeneity within the castor fields. The class-based sensor independent normalized difference vegetation index (CBSI-NDVI), normalized difference vegetation index (Red-NIR), and modified NDVI were produced, then separability analysis was conducted for the selecting optimum temporal date for castor crop mapping. The heterogeneity effect within the field was studied using variance parameters, and its values were observed low within the castor crop field. While considering the “individual sample as mean” training parameter approach on CBSI-NDVI, the variance of the target was decreased from 0.019 to 0.016. The identified castor field variance was close to the training field based on their high membership values and these fields had low mean membership difference (MMD) value. MMD values of castor training class with other crop classes were very high (0.425 – Isabgol, 0.502 – wheat, 0.259 – Fenugreek, 0.809 – Mustard, and 0.569 – Cumin), which indicates other crops were distinguished very well.