{"type":"ScholarlyArticle","title":"JM-Net and Cluster-SVM for Aerial Scene Classification","authors":[{"name":"Xiaoqiang Lu","slug":"xiaoqiang-lu","given":"Xiaoqiang","family":"Lu","affiliation":"Center for OPTical IMagery Analysis and Learning (OPTIMAL) , State Key Laboratory of Transient Optics and Photonics ,"},{"name":"Yuan Yuan","slug":"yuan-yuan","given":"Yuan","family":"Yuan","affiliation":"Center for OPTical IMagery Analysis and Learning (OPTIMAL) , State Key Laboratory of Transient Optics and Photonics ,; Xi'an Institute of Optics and Precision Mechanics , Chinese Academy of Sciences , Xi'an 710119 , Shaanxi , China."},{"name":"Jie Fang","slug":"jie-fang","given":"Jie","family":"Fang","affiliation":"Center for OPTical IMagery Analysis and Learning (OPTIMAL) , State Key Laboratory of Transient Optics and Photonics ,; University of the Chinese Academy of Sciences , 19A Yuquanlu , Beijing , 100047 , China."}],"abstract":"Aerial scene classification, which is a fundamental problem for remote sensing imagery, can automatically label an aerial image with a specific semantic category. Although deep learning has achieved competitive performance for aerial scene classification, training the conventional neural networks with aerial datasets will easily stick in overtting and local minimum. Because the aerial datasets only contain a few hundreds or thousands images, meanwhile the conventional networks usually contain millions of parameters to be trained. To address the problem, a novel convolutional neural network named JM-Net is proposed in this paper, which has different size of convolution kernels in same layer and ignores the fully convolytion layer, so it has fewer parameters and can be trained well on aerial datasets. Additionally, Cluster-SVM, a strategy to improve the accuracy and speed up the classification is used in the specific task. Finally, our method suparssed the state-of-art result on the challenging AID dataset while cost shorter time and used smaller storage space.","doi":"10.24963/ijcai.2017/332","url":"https://ijcai.org/proceedings/2017/332","pdf":"https://ijcai.org/proceedings/2017/0332.pdf","markdown":"https://ijcai.org/proceedings/2017/332.md","bibtex":"https://ijcai.org/proceedings/2017/bibtex/332","volume":{"slug":"2017","title":"Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2017","url":"https://ijcai.org/proceedings/2017/","year":2017,"location":"Melbourne, Australia","dates":"19-25 August 2017","editors":"Carles Sierra","isbn":"978-0-9992411-0-3","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2017"},"section":"Machine Learning A-R","track":"Main track","number":332,"pages":{"first":2386,"last":2392},"keywords":["Machine Learning: Data Mining","Machine Learning: Machine Learning","Machine Learning: Feature Selection/Construction","Machine Learning: Classification"],"videos":[],"references":[{"position":1,"raw":"[Castelluccio et al., 2015] Marco Castelluccio, Giovanni Poggi, Carlo Sansone, and Luisa Verdoliva. 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