ABSTRACT Due to increasing global population, the need of focusing on land cover and land uses by human beings is felt. Nowadays, most of the researchers and scientists are found showing much attention in agricultural landscapes because the effectual land-cover (LC) mapping provides the correct information regarding the land uses and land cover. Crop classification is one of the essential issues in the agriculture field because it affects the government and private agencies economically. The number of methodologies exist to classify the crop images from the remotely sensed image, but the traditional methodologies do not perform well due to some factors, like blurring effect, noise, etc. We have proposed an Adaptive Fuzzy-Centred Denoising Filter (AFDF) and Extended Elman Neural Network (E2N2) to enhance visualization of crop image to crop classification in order to overcome traditional techniques RSI. The remote-sensing crop images have been considered in the proposed work, and the AFDF preprocesses the image to eradicate the noise. After that, the Jaccard Coefficient-based Shared Nearest Neighbour (JCSNN) segments the object as preprocessed images. The LTrP, DWT, GLCM, and shape features are extracted as the optimal features chosen by Enhanced Elephant Herding Optimization (E2HO). Lastly, the crop is classified using the E2N2 classifier. The high-resolution images were utilized for the experiments to validate the proposed work. At last, various techniques have been compared against the proposed system, and outcomes have shown significant enhancement in accuracy and reliability. The proposed system obtains 88.01% F-measure, 95.2% accuracy with lesser FPR values that are greater when analogized to all prevalent methods. The advantage of using proposed effective protocols, like Adaptive Fuzzy-centred Denoising Filter and also E2N2 classifier, boost-up the crop images’ visualization and also crop categorization in remote-sensing images.
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