Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Articles Article Faculty Publications -Articles Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Gaussian MutationSpider Monkey Optimization (GM-SMO) Model for Remote Sensing Scene Classification Subject The topic of the resource AlexNet; Gaussian MutationSpider Monkey Optimization; generative adversarial network; scene classification; VGG19 Description An account of the resource Scene classification aims to classify various objects and land use classes such as farms, highways, rivers, and airplanes in the remote sensing images. In recent times, the Convolutional Neural Network (CNN) based models have been widely applied in scene classification, due to their efficiency in feature representation. The CNN based models have the limitation of overfitting problems, due to the generation of more features in the convolutional layer and imbalanced data problems. This study proposed Gaussian MutationSpider Monkey Optimization (GM-SMO) model for feature selection to solve overfitting and imbalanced data problems in scene classification. The Gaussian mutation changes the position of the solution after exploration to increase the exploitation in feature selection. The GM-SMO model maintains better tradeoff between exploration and exploitation to select relevant features for superior classification. The GM-SMO model selects unique features to overcome overfitting and imbalanced data problems. In this manuscript, the Generative Adversarial Network (GAN) is used for generating the augmented images, and the AlexNet and Visual Geometry Group (VGG) 19 models are applied to extract the features from the augmented images. Then, the GM-SMO model selects unique features, which are given to the Long Short-Term Memory (LSTM) network for classification. In the resulting phase, the GM-SMO model achieves 99.46% of accuracy, where the existing transformer-CNN has achieved only 98.76% on the UCM dataset. 2022 by the authors. Creator An entity primarily responsible for making the resource Shaik A.L.H.P.; Manoharan M.K.; Pani A.K.; Avala R.R.; Chen C.-M. Source A related resource from which the described resource is derived Remote Sensing, Vol-14, No. 24 Publisher An entity responsible for making the resource available MDPI Date A point or period of time associated with an event in the lifecycle of the resource 2022-01-01 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.3390/rs14246279" target="_blank" rel="noreferrer noopener">https://doi.org/10.3390/rs14246279</a> <br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85144615476&amp;doi=10.3390%2Frs14246279&amp;partnerID=40&amp;md5=d3585dfd43d21a344cc74a8bbb3087e6" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85144615476&amp;doi=10.3390%2frs14246279&amp;partnerID=40&amp;md5=d3585dfd43d21a344cc74a8bbb3087e6</a> Rights Information about rights held in and over the resource All Open Access; Gold Open Access Relation A related resource ISSN: 20724292 Format The file format, physical medium, or dimensions of the resource Online Language A language of the resource English Type The nature or genre of the resource Article Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Shaik A.L.H.P., Department of Electronics and Communication Engineering, Ballari Institute of Technology and Management, Ballari, 583104, India; Manoharan M.K., School of Computer Science and Engineering, Vellore Institute of Technology, Chennai, 600127, India; Pani A.K., Department of Computer Science and Engineering, CHRIST (Deemed to be University), Bangalore, 560029, India; Avala R.R., Department of Mechanical Engineering, CMR Technical Campus, Hyderabad, 501401, India; Chen C.-M., College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao, 266590, China