DOI: https://doi.org/10.14710/jtsiskom.2022.14346

Klasifikasi Citra Satelit menggunakan Lightweight Ensemble Convolutional Network

1Dept. of Computer Engineering, Institut Teknologi Sepuluh Nopember, Kampus ITS Sukolilo Surabaya, Indonesia 60111, Indonesia

2Dept. of Computer Engineering, Institut Teknologi Sepuluh Nopember, Kampus ITS Sukolilo, Surabaya, Indonesia 60111, Indonesia

Received: 16 Oct 2021; Revised: 2 Apr 2022; Accepted: 22 Jul 2022; Available online: 31 Jul 2022; Published: 24 Sep 2024.
Open Access Copyright (c) 2024 Reza Fuad Rachmadi, Kentani Langgalih Prioko, Supeno Mardi Susiki Nugroho, I Ketut Eddy Purnama
Creative Commons License This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

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Abstract
Citra satelit dapat digunakan salah satunya sebagai pengamatan kondisi atmosfer dan permukaan pada bumi. Dengan semakin berkembangnya teknologi citra satelit, waktu untuk pengambilan citra satelit menjadi lebih efisien. Makalah ini melakukan eksperimen menggunakan klasifier ensemble convolutional network untuk melakukan pengenalan kondisi atmosfer pada citra satelit. Empat buah arsitektur Convolutional Neural Network (CNN) digunakan dalam eksperimen ini, yaitu MobileNetV2, ResNet18, ResNet18Half, dan SqueezeNet. Keempat arsitektur CNN tersebut dipilih karena mempunyai jumlah parameter yang tidak terlalu besar (lightweight) serta dapat diterapkan pada banyak perangkat keras tertanam. Eksperimen yang dilakukan dengan menggunakan dataset USTC SmokeRS memperlihatkan bahwa klasifier ensemble memperoleh hasil yang baik dengan akurasi rata-rata tertinggi sebesar 97.06 %.
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Keywords: ensemble CNN, citra satelit, lightweight CNN

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Section: Original Research Articles
Language : ID
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  1. C. L. Parkinson, “Aqua: An Earth-observing satellite mission to examine water and other climate variables,” IEEE Transactions on Geoscience and Remote Sensing, Vol. 41, No. 2, pp.173-183, 2003
  2. S. Bahri, “Kajian Penyebaran Kabut Asap Kebakaran Hutan dan Lahan di Wilayah Sumatera Bagian Utara dan Kemungkinan Mengatasinya dengan TMC,“ Jurnal Sains & Teknologi Modifikasi Cuaca, Vol. 3, No. 2, pp. 99-104, 2002
  3. GISTEMP Team, “GISS Surface Temperature Analysis (GISTEMP) version 4,” NASA Goddard Institute for Space Studies, 2019
  4. N. Lenssen, G. Schmidt, J. Hansen, M. Menne, A. Persin, R. Ruedy, and D. Zyss, “Improvements in the GISTEMP uncertainty model,” J. Geophys. Res. Atmos., Vol. 124, No. 12, pp. 6307-6326, 2019
  5. A. Krizhevsky, I. Sutskever, and G. E. Hinton, “Imagenet classification with deep convolutional neural networks,” in Advances in neural information processing systems, 25, 2012, pp.1097-1105
  6. R. Ba, C. Chen, J. Yuan, W. Song, and S. Lo, “SmokeNet: Satellite Smoke Scene Detection Using Convolutional Neural Network with Spatial and Channel-Wise Attention,” Remote Sensing, Vol. 11, No. 14, 1702, 2019
  7. M. Sandler, A. Howard, M. Zhu, A. Zhmoginov, and L.C. Chen, “Mobilenetv2: Inverted residuals and linear bottlenecks,” In Proceedings of the IEEE conference on computer vision and pattern recognition, 2018, pp. 4510-4520
  8. K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” In Proceedings of the IEEE conference on computer vision and pattern recognition, 2016, pp. 770-778
  9. F.N. Iandola, S. Han, M.W. Moskewicz, K. Ashraf, W.J. Dally, and K. Keutzer, “SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5 MB model size,” arXiv preprint arXiv:1602.07360, 2016
  10. A. Paszke, S. Gross, F. Massa, A. Lerer, J. Bradbury, G. Chanan, T. Killeen, Z. Lin, N. Gimelshein, L. Antiga, and A. Desmaison, “Pytorch: An imperative style, high-performance deep learning library,” in Advances in neural information processing systems, 32, 2019, pp. 8026-8037
  11. I. Sutskever, J. Martens, G. Dahl, and G.E. Hinton, “On the importance of initialization and momentum in deep learning,” In International conference on machine learning (PMLR), 2013, pp. 1139-1147
  12. A. Jamali, M. Mahdianpari, B. Brisco, J. Granger, F. Mohammadimanesh, and B. Salehi, "Comparing Solo Versus Ensemble Convolutional Neural Networks for Wetland Classification Using Multi-Spectral Satellite Imagery," Remote Sensing, Vol. 13, No. 11:2046, 2021
  13. J. Chi and H.-C. Kim. "Retrieval of daily sea ice thickness from AMSR2 passive microwave data using ensemble convolutional neural networks," GIScience & Remote Sensing, Vol. 58, No. 6, pp. 812-830, 2021
  14. I. Priyadarshini and V. Puri. "A convolutional neural network (CNN) based ensemble model for exoplanet detection," Earth Science Informatics, Vol. 14, No. 2, pp. 735-747, 2021
  15. A.K. Das, S. Ghosh, S. Thunder, R. Dutta, S. Agarwal and A. Chakrabarti. "Automatic COVID-19 detection from X-ray images using ensemble learning with convolutional neural network," Pattern Analysis and Applications, Vol. 24, No. 3, pp. 1111-1124, 2021
  16. S. Chen, Y. Cao, X. Feng, and X. Lu, “Global2Salient: Self-Adaptive Feature Aggregation for Remote Sensing Smoke Detection,”, Neurocomputing, Vol. 446, pp. 202-220, 2021
  17. M. M. Alhaisoni, R. A., Ramadan, and A. Y. Khedr, “SCF: Smart Big Data Classification Framework,” Indian Journal of Science and Technology, Vol. 12, No. 37, pp. 1-8, 2019
  18. Z. Yin, B. Wan, F. Yuan, X. Xia, and J. Shi, “A deep normalization and convolutional neural network for image smoke detection,” IEEE Access, Vol. 5, pp.18429-18438, 2017
  19. S. Aslan, U. Güdükbay, B. U., Töreyin, and A. E. Cetin, “Early wildfire smoke detection based on motion-based geometric image transformation and deep convolutional generative adversarial networks,” In IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2019, pp. 8315-8319
  20. K. Gu, Z. Xia, J. Qiao, and W. Lin, “Deep dual-channel neural network for image-based smoke detection,” IEEE Transactions on Multimedia, Vol. 22 No. 2, pp. 311-323, 2019

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