A novel machine learning approach to classify the remote sensing optically images based on swarm intelligence

Remote sensing image scene classification has become a well-liked research subject because of its use in a number of domains, including object recognition, land use classification, picture extraction, and monitoring. Semantic data, which is crucial in direct measures like oceanography, vegetation, mineral extraction, temperature, and forest management, will be used by the Remote sensing image classification process to provide a class label to each scenario class. By the use of Swarm Intelligence and Support Vector Machines, this work develops an RSI categorization technique in order to obtain the optical images. The recommended SVM-SI strategy inherits the advantages of both methods. The RSI has undergone pre-processing to transform it into a suitable form. The features were also developed using the RetinaNet framework. Moreover, SVM-based classifiers are employed to give the RSIs the proper class labelling, and the design of the SI can improve categorization effectiveness. Empirical investigations have shown that the SVM-SI technique is superior to other cutting-edge methods, and common databases that are easily available to the public are used to validate the success of the strategy. Accuracy of 99.49% was attained using the SVM-SI model and approach that was presented.