Crop identification (CI) utilizing hyperspectral pictures/images (HSI) collected from satellite is one of the effective research area considering various agriculture related applications. Wide range of research activity is carried out and modelled in the area of crop recognition (CR) for building efficient model. Correlation filter (CF) is considered to be one of an effective method and are been applied by existing methodologies for identifying similar signal features. Nonetheless, very limited is work is carried out using CF for crop classification using hyperspectral data. Further, effective method is required that bring good tradeoffs between memory and computational overhead. The crop classification model can be improved by combining machine learning (ML) technique with CF. HSI is composed of hundreds of channels with large data dimension that gives entire information of imaging. Thus, using classification model is very useful for real-time application uses. However, the accuracy of classification task is affected as HSI is composed of high number of redundant and correlated feature sets. Along with, induce computational overhead with less benefits using redundant features. Thus, effective band selection, texture analysis, and classification method is required for accurately classifying multiple crops. This paper analyses various existing techniques for identification and classification of crops using satellite imagery detection method. Then, identify the research issues, challenges, and problems of existing model for building efficient techniques for identification and classification of crops using satellite image. Experiment are conducted on standard hyperspectral data. The result attained shows proposed model attain superior classification accuracy when compared with existing hyperspectral image classification model.
MACHINE LEARNING IN CROP CLASSIFICATION OF TEMPORAL MULTISPECTRAL SATELLITE IMAGE
