A Light-Weight Deep Learning Model for Remote Sensing Image Classification
In this paper, we present a high-performance and light-weight deep learning model for Remote Sensing Image Classification (RSIC), the task of identifying the aerial scene of a remote sensing image. To this end, we first evaluate various benchmark convolutional neural network (CNN) architectures: Mob...
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| Vydané v: | 2023 International Symposium on Image and Signal Processing and Analysis (ISPA) s. 1 - 6 |
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| Hlavní autori: | , , , , , , |
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| Jazyk: | English |
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IEEE
18.09.2023
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| ISSN: | 1849-2266 |
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| Abstract | In this paper, we present a high-performance and light-weight deep learning model for Remote Sensing Image Classification (RSIC), the task of identifying the aerial scene of a remote sensing image. To this end, we first evaluate various benchmark convolutional neural network (CNN) architectures: MobileNet V1/V2, ResNet 50/151V2, InceptionV3/InceptionResNetV2, EfficientNet B0/B7, DenseNet 121/201, ConNeXt Tiny/Large. Then, the best performing models are selected to train a compact model in a teacher-student arrangement. The knowledge distillation from the teacher aims to achieve high performance with significantly reduced complexity. By conducting extensive experiments on the NWPU-RESISC45 benchmark, our proposed teacher and student models outper-forms the state-of-the-art systems, and has potential to be applied on a wide range of edge devices. |
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| AbstractList | In this paper, we present a high-performance and light-weight deep learning model for Remote Sensing Image Classification (RSIC), the task of identifying the aerial scene of a remote sensing image. To this end, we first evaluate various benchmark convolutional neural network (CNN) architectures: MobileNet V1/V2, ResNet 50/151V2, InceptionV3/InceptionResNetV2, EfficientNet B0/B7, DenseNet 121/201, ConNeXt Tiny/Large. Then, the best performing models are selected to train a compact model in a teacher-student arrangement. The knowledge distillation from the teacher aims to achieve high performance with significantly reduced complexity. By conducting extensive experiments on the NWPU-RESISC45 benchmark, our proposed teacher and student models outper-forms the state-of-the-art systems, and has potential to be applied on a wide range of edge devices. |
| Author | McLoughlin, Ian Nguyen, Anh Pham, Lam Lampert, Jasmin Le, Cam Ngo, Dat Schindler, Alexander |
| Author_xml | – sequence: 1 givenname: Lam surname: Pham fullname: Pham, Lam email: lam.pham@ait.ac.at organization: Austrian Institute of Technology,Vienna,Austria – sequence: 2 givenname: Cam surname: Le fullname: Le, Cam email: cam.levt123@hcmut.edu.vn organization: HCM University of Technology, HCM,VietNam – sequence: 3 givenname: Dat surname: Ngo fullname: Ngo, Dat email: dn22678@essex.ac.uk organization: University of Essex,Colchester,UK – sequence: 4 givenname: Anh surname: Nguyen fullname: Nguyen, Anh email: AnhNTN34@fsoft.com.vn organization: FPT Soft Company, HCM,VietNam – sequence: 5 givenname: Jasmin surname: Lampert fullname: Lampert, Jasmin email: Jasmin.Lampert@ait.ac.at organization: Austrian Institute of Technology,Vienna,Austria – sequence: 6 givenname: Alexander surname: Schindler fullname: Schindler, Alexander email: Alexander.Schindler@ait.ac.at organization: Austrian Institute of Technology,Vienna,Austria – sequence: 7 givenname: Ian surname: McLoughlin fullname: McLoughlin, Ian email: ian.mcloughlin@singaporetech.edu.sg organization: Singapore Institute of Technology,Singapore |
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| Snippet | In this paper, we present a high-performance and light-weight deep learning model for Remote Sensing Image Classification (RSIC), the task of identifying the... |
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| SubjectTerms | Analytical models Benchmark testing Complexity theory Computational modeling convolutional neural network (CNN) Convolutional neural networks data augmentation Deep learning high-level features Image edge detection Teacher-student model |
| Title | A Light-Weight Deep Learning Model for Remote Sensing Image Classification |
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