A New Method for Scene Classification from the Remote Sensing Images

作     者：Purnachand Kollapudi Saleh Alghamdi Neenavath Veeraiah Youseef Alotaibi Sushma Thotakura Abdulmajeed Alsufyani 

作者机构：Department of CSEB V Raju Institute of TechnologyNarsapurMedakTelanganaIndia Department of Information TechnologyCollege of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia Department of Electronics and CommunicationsDVR&DHS MIC Engineering CollegeKanchikacharlaVijayawadaA.P.India Department of Computer ScienceCollege of Computer and Information SystemsUmm Al-Qura UniversityMakkah21955Saudi Arabia Department of ECEP.V.P Siddhartha Institute of TechnologyVijayawadaIndia Department of Computer ScienceCollege of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia 

出 版 物：《Computers, Materials & Continua》 (计算机、材料和连续体（英文）)

年 卷 期：2022年第72卷第7期

页      面：1339-1355页

核心收录：

学科分类：08[工学] 0805[工学-材料科学与工程（可授工学、理学学位）] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：We deeply acknowledge Taif University for supporting this study through Taif University Researchers Supporting Project Number(TURSP-2020/115) Taif University Taif Saudi Arabia 

主　　题：Remote sensing RSISU DL RESNET-50 VGG-16 

摘      要：The mission of classifying remote sensing pictures based on their contents has a range of applications in a variety of *** recent years,a lot of interest has been generated in researching remote sensing image scene *** sensing image scene retrieval,and scene-driven remote sensing image object identification are included in the Remote sensing image scene understanding(RSISU)*** 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 *** benefits(POEP)include two *** 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 ***,we are able to use Enhanced pooling tomake the most use of themulti-resolution feature maps that include second-order *** enablesCNNs to better cope with(RSISU)issues by providing more representative feature *** data for this paper is stored in a UCI dataset with 21 types of *** the beginning,the picture was pre-processed,then the features were retrieved using RESNET-50,Alexnet,and VGG-16 integration of *** characteristics have been amalgamated and sent to the attention layer,after this characteristic has been fused,the process of classifying the data will take *** utilize an ensemble classifier in our classification a