{"title":"基于多试向量差分进化算法和多支持向量机分类器的遥感场景类型分类","authors":"Sandeep Kumar, S. Setty","doi":"10.4018/ijec.301259","DOIUrl":null,"url":null,"abstract":"In recent decades, remote sensing scene type classification becomes a challenging task in remote sensing applications. In this paper, a new model is proposed for multi-class scene type classification in remote sensing images. Firstly, the aerial images are collected from the Aerial Image Dataset (AID), University of California Merced (UC Merced) and REmote Sensing Image Scene Classification 45 (RESISC45) datasets. Next, AlexNet, GoogLeNet, ResNet 18, and Visual Geometric Group (VGG) 19 models are used for extracting feature vectors from the collected aerial images. After feature extraction, the Multi-Trial vector based Differential Evolution (MTDE) algorithm is proposed to choose active feature vectors for better classification and to reduce system complexity and time consumption. The selected active features are fed to the Multi Support Vector Machine (MSVM) for final scene type classification. The simulation results showed that the proposed MTDE-MSVM model obtained high classification accuracy of 99.41%, 99.59% and 99.74% on RESISC45, AID and UC Merced datasets.","PeriodicalId":13957,"journal":{"name":"Int. J. e Collab.","volume":"4 1","pages":"1-20"},"PeriodicalIF":0.0000,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Remote Sensing Scene Type Classification Using Multi-Trial Vector-Based Differential Evolution Algorithm and Multi-Support Vector Machine Classifier\",\"authors\":\"Sandeep Kumar, S. Setty\",\"doi\":\"10.4018/ijec.301259\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In recent decades, remote sensing scene type classification becomes a challenging task in remote sensing applications. In this paper, a new model is proposed for multi-class scene type classification in remote sensing images. Firstly, the aerial images are collected from the Aerial Image Dataset (AID), University of California Merced (UC Merced) and REmote Sensing Image Scene Classification 45 (RESISC45) datasets. Next, AlexNet, GoogLeNet, ResNet 18, and Visual Geometric Group (VGG) 19 models are used for extracting feature vectors from the collected aerial images. After feature extraction, the Multi-Trial vector based Differential Evolution (MTDE) algorithm is proposed to choose active feature vectors for better classification and to reduce system complexity and time consumption. The selected active features are fed to the Multi Support Vector Machine (MSVM) for final scene type classification. The simulation results showed that the proposed MTDE-MSVM model obtained high classification accuracy of 99.41%, 99.59% and 99.74% on RESISC45, AID and UC Merced datasets.\",\"PeriodicalId\":13957,\"journal\":{\"name\":\"Int. J. e Collab.\",\"volume\":\"4 1\",\"pages\":\"1-20\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Int. J. e Collab.\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.4018/ijec.301259\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Int. J. e Collab.","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.4018/ijec.301259","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
摘要
近几十年来,遥感场景类型分类成为遥感应用中的一个具有挑战性的课题。本文提出了一种新的遥感图像多类场景类型分类模型。首先,从航空图像数据集(AID)、加州大学默塞德分校(UC Merced)和遥感图像场景分类45 (RESISC45)数据集中收集航空图像。接下来,使用AlexNet、GoogLeNet、ResNet 18和Visual Geometric Group (VGG) 19模型对收集到的航空图像进行特征向量提取。在特征提取后,提出基于多试验向量的差分进化算法(Multi-Trial vector based Differential Evolution, MTDE),选择有效的特征向量进行更好的分类,降低系统复杂度和时间消耗。选择的活动特征被馈送到多支持向量机(MSVM)进行最终的场景类型分类。仿真结果表明,所提出的MTDE-MSVM模型在RESISC45、AID和UC Merced数据集上的分类准确率分别为99.41%、99.59%和99.74%。
Remote Sensing Scene Type Classification Using Multi-Trial Vector-Based Differential Evolution Algorithm and Multi-Support Vector Machine Classifier
In recent decades, remote sensing scene type classification becomes a challenging task in remote sensing applications. In this paper, a new model is proposed for multi-class scene type classification in remote sensing images. Firstly, the aerial images are collected from the Aerial Image Dataset (AID), University of California Merced (UC Merced) and REmote Sensing Image Scene Classification 45 (RESISC45) datasets. Next, AlexNet, GoogLeNet, ResNet 18, and Visual Geometric Group (VGG) 19 models are used for extracting feature vectors from the collected aerial images. After feature extraction, the Multi-Trial vector based Differential Evolution (MTDE) algorithm is proposed to choose active feature vectors for better classification and to reduce system complexity and time consumption. The selected active features are fed to the Multi Support Vector Machine (MSVM) for final scene type classification. The simulation results showed that the proposed MTDE-MSVM model obtained high classification accuracy of 99.41%, 99.59% and 99.74% on RESISC45, AID and UC Merced datasets.