Open Access

ARTICLE

A New Method for Scene Classification from the Remote Sensing Images

Purnachand Kollapudi¹, Saleh Alghamdi², Neenavath Veeraiah^3,*, Youseef Alotaibi⁴, Sushma Thotakura⁵, Abdulmajeed Alsufyani⁶

1 Department of CSE, B V Raju Institute of Technology, Narsapur, Medak, Telangana, India
2 Department of Information Technology, College of Computers and Information Technology, Taif University, Taif, 21944, Saudi Arabia
3 Department of Electronics and Communications, DVR&DHS MIC Engineering College, Kanchikacharla, Vijayawada, A.P., India
4 Department of Computer Science, College of Computer and Information Systems, Umm Al-Qura University, Makkah, 21955, Saudi Arabia
5 Department of ECE, P.V.P Siddhartha Institute of Technology, Vijayawada, India
6 Department of Computer Science, College of Computers and Information Technology, Taif University, Taif, 21944, Saudi Arabia

* Corresponding Author: Neenavath Veeraiah. Email:

Computers, Materials & Continua 2022, 72(1), 1339-1355. https://doi.org/10.32604/cmc.2022.025118

Received 12 November 2021; Accepted 06 January 2022; Issue published 24 February 2022

Abstract

The mission of classifying remote sensing pictures based on their contents has a range of applications in a variety of areas. In recent years, a lot of interest has been generated in researching remote sensing image scene classification. Remote sensing image scene retrieval, and scene-driven remote sensing image object identification are included in the Remote sensing image scene understanding (RSISU) research. In the last several years, the number of deep learning (DL) methods that have emerged has caused the creation of new approaches to remote sensing image classification to gain major breakthroughs, providing new research and development possibilities for RS image classification. A new network called Pass Over (POEP) is proposed that utilizes both feature learning and end-to-end learning to solve the problem of picture scene comprehension using remote sensing imagery (RSISU). This article presents a method that combines feature fusion and extraction methods with classification algorithms for remote sensing for scene categorization. The benefits (POEP) include two advantages. The multi-resolution feature mapping is done first, using the POEP connections, and combines the several resolution-specific feature maps generated by the CNN, resulting in critical advantages for addressing the variation in RSISU data sets. Secondly, we are able to use Enhanced pooling to make the most use of the multi-resolution feature maps that include second-order information. This enables CNNs to better cope with (RSISU) issues by providing more representative feature learning. The data for this paper is stored in a UCI dataset with 21 types of pictures. In the beginning, the picture was pre-processed, then the features were retrieved using RESNET-50, Alexnet, and VGG-16 integration of architectures. After characteristics have been amalgamated and sent to the attention layer, after this characteristic has been fused, the process of classifying the data will take place. We utilize an ensemble classifier in our classification algorithm that utilizes the architecture of a Decision Tree and a Random Forest. Once the optimum findings have been found via performance analysis and comparison analysis.

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Keywords

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