Crop image classification using spherical contact distributions from remote sensing images

Land use and land cover classification from a remote sensing image is a long standing research problem. It ranges from simple classifications like mapping water bodies to complex classifications like crop and forest strands. Crop image classification is complex because of various stages of growth of the same crop, same spectral values for various crops, an other multitude of problems. Crop image classification is very essential for agriculture monitoring, crop yield production, global food security, etc. A new unsupervised algorithm, Spherical Contact Distribution Classification Algorithm (SCDCA) is proposed in this paper which uses mathematical morphology, spherical contact distributions, and first order statistics. Later SCDCA is compared with linear contact distribution classification algorithm (LCDCA). Quantitative analyses prove the efficiency of the algorithm and present that the complexity of SCDCA is very much less when compared to that of LCDCA.