Gaussian MutationSpider Monkey Optimization (GM-SMO) Model for Remote Sensing Scene Classification
- Title
- Gaussian MutationSpider Monkey Optimization (GM-SMO) Model for Remote Sensing Scene Classification
- Creator
- Shaik A.L.H.P.; Manoharan M.K.; Pani A.K.; Avala R.R.; Chen C.-M.
- Description
- 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.
- Source
- Remote Sensing, Vol-14, No. 24
- Date
- 2022-01-01
- Publisher
- MDPI
- Subject
- AlexNet; Gaussian MutationSpider Monkey Optimization; generative adversarial network; scene classification; VGG19
- Coverage
- 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
- Rights
- All Open Access; Gold Open Access
- Relation
- ISSN: 20724292
- Format
- Online
- Language
- English
- Type
- Article
Collection
Citation
Shaik A.L.H.P.; Manoharan M.K.; Pani A.K.; Avala R.R.; Chen C.-M., “Gaussian MutationSpider Monkey Optimization (GM-SMO) Model for Remote Sensing Scene Classification,” CHRIST (Deemed To Be University) Institutional Repository, accessed February 24, 2025, https://archives.christuniversity.in/items/show/14783.