查看文章

Cotton crop classification is found to be a significant task in crop management. Literature has exploited unsupervised fuzzy based classification and various vegetation indices for cotton crop classification. However, fuzzy based classification has negative effect on performance, because of inliers and outliers in the image. Hence, it is not reliable to investigate the performance of vegetation indices and for cotton crop classification. To overcome this drawback, this paper introduces possiblistic fuzzy c-means (PFCM) clustering for labeling the learning data and exploits support vector machine (SVM), which enables supervised learning, for cotton crop classification. Subsequently, five vegetation indices namely, simple ratio (SR), Normalized Difference Vegetation Index (NDVI), Soil Adjusted Vegetation Index (SAVI), Triangular Vegetation Index (TVI) and Transformed Normalized Difference Vegetation Index (TNDVI) are …