Affiliations: [a] Department of Computing Technologies, School of Computing, SRM Institute of Science and Technology, kattankulathur, chennai, Tamilnadu, India
| [b] Department of CSE, RGM College of Engineering and Technology, Nandyal, Andhra Pradesh | [c] Department of CSE, VFSTR Deemed to be University, Vadlamudi, Guntur, Andhra Pradesh, India
| [d] School of Computer Science and Engineering, VIT-AP University, Amaravati, Andhra Pradesh, India
| [e] School of Computer Science and Artificial Intelligence, SR University, Warangal, Telangana, India
Correspondence:
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Corresponding author. R. Kaviarasan, Department of CSE, RGM College of Engineering and Technology, Nandyal, 518501, Andhra Pradesh, India. Email: [email protected].
Abstract: This research focuses on scene segmentation in remotely sensed images within the field of Remote Sensing Image Scene Understanding (RSISU). Leveraging recent advancements in Deep Learning (DL), particularly Residual Neural Networks (RESNET-50 and RESNET-101), and the research proposes a methodology involving feature fusing, extraction, and classification for categorizing remote sensing images. The approach employs a dataset from the University of California Irvine (UCI) comprising twenty-one groups of pictures. The images undergo pre-processing, feature extraction using the mentioned DL frameworks, and subsequent categorization through an ensemble classification structure combining Kernel Extreme Learning Machine (KELM) and Support Vector Machine (SVM). The paper concludes with optimal results achieved through performance and comparison analyses.
Keywords: Remote sensing, image scene classification, deep learning, feature extraction, RESNET- 101, ensemble
