O. Korzh, Gregory Cook, T. Andersen, Edoardo Serra
{"title":"遥感图像CNN迁移学习集成的叠加方法","authors":"O. Korzh, Gregory Cook, T. Andersen, Edoardo Serra","doi":"10.1109/INTELLISYS.2017.8324356","DOIUrl":null,"url":null,"abstract":"In this paper we propose a stacking approach for Convolutional Neural Network (CNN) transfer learning ensemble for remote sensing imagery, in particular for the task of scene classification. We propose to use a combination of features produced by an ensemble of CNNs as one feature vector for classification. At the same time the original data set can be processed with different up-sampling and image enhancement methods and then used to obtain more features from pretrained networks. We investigate both fine-tuning and non fine-tuning approaches for transfer learning. We have selected Brazilian Coffee Scenes data set as a benchmark to measure the classification accuracy. Proposed method in case of a non fine-tuned model shows 89.18% classification accuracy. For a fine-tuned model the best classification rate is 96.11%. We analyzed how networks that have appeared recently (VGG-19 and SqueezeNet), can be applied to the task of transfer learning for remote sensing. Also we describe a method of decreasing processing time and memory consumption while preserving classification accuracy by using feature selection based on feature importance.","PeriodicalId":131825,"journal":{"name":"2017 Intelligent Systems Conference (IntelliSys)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2017-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"9","resultStr":"{\"title\":\"Stacking approach for CNN transfer learning ensemble for remote sensing imagery\",\"authors\":\"O. Korzh, Gregory Cook, T. Andersen, Edoardo Serra\",\"doi\":\"10.1109/INTELLISYS.2017.8324356\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this paper we propose a stacking approach for Convolutional Neural Network (CNN) transfer learning ensemble for remote sensing imagery, in particular for the task of scene classification. We propose to use a combination of features produced by an ensemble of CNNs as one feature vector for classification. At the same time the original data set can be processed with different up-sampling and image enhancement methods and then used to obtain more features from pretrained networks. We investigate both fine-tuning and non fine-tuning approaches for transfer learning. We have selected Brazilian Coffee Scenes data set as a benchmark to measure the classification accuracy. Proposed method in case of a non fine-tuned model shows 89.18% classification accuracy. For a fine-tuned model the best classification rate is 96.11%. We analyzed how networks that have appeared recently (VGG-19 and SqueezeNet), can be applied to the task of transfer learning for remote sensing. Also we describe a method of decreasing processing time and memory consumption while preserving classification accuracy by using feature selection based on feature importance.\",\"PeriodicalId\":131825,\"journal\":{\"name\":\"2017 Intelligent Systems Conference (IntelliSys)\",\"volume\":\"1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2017-09-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"9\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2017 Intelligent Systems Conference (IntelliSys)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/INTELLISYS.2017.8324356\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2017 Intelligent Systems Conference (IntelliSys)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/INTELLISYS.2017.8324356","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Stacking approach for CNN transfer learning ensemble for remote sensing imagery
In this paper we propose a stacking approach for Convolutional Neural Network (CNN) transfer learning ensemble for remote sensing imagery, in particular for the task of scene classification. We propose to use a combination of features produced by an ensemble of CNNs as one feature vector for classification. At the same time the original data set can be processed with different up-sampling and image enhancement methods and then used to obtain more features from pretrained networks. We investigate both fine-tuning and non fine-tuning approaches for transfer learning. We have selected Brazilian Coffee Scenes data set as a benchmark to measure the classification accuracy. Proposed method in case of a non fine-tuned model shows 89.18% classification accuracy. For a fine-tuned model the best classification rate is 96.11%. We analyzed how networks that have appeared recently (VGG-19 and SqueezeNet), can be applied to the task of transfer learning for remote sensing. Also we describe a method of decreasing processing time and memory consumption while preserving classification accuracy by using feature selection based on feature importance.