Classification Analysis Of Regional Characteristics Using Convolutional Neural Network On Satellite Image
DOI:
https://doi.org/10.32486/aksi.v7i1.260Keywords:
Map, artificial intelligence, deep learning, dataset, machine learningAbstract
One of the developments of Machine Learning technology is Deep Learning which uses an algorithm based on mathematical concepts that work like the human brain. An example of the use of deep learning is for digital image processing. Image Processing is used to identify, classify objects quickly, precisely, and can process multiple data simultaneously. In this study, an analysis of the classification of regional characteristics will be carried out. Regional characteristics are divided into two aspects, namely water areas and land areas. The land area is divided into mountains, highlands, lowlands, and valleys. While the territorial waters include straits, bays, rivers, and lakes. Classification will be done using one of the algorithms from Deep learning used in image processing, namely Convolutional Neural Network (CNN). The CNN algorithm consists of 3 main layers, namely Convolutional Layer, Pooling Layer, and Fully Connected Layer. In this study using CNN architecture with a combination of 3 Convolutional Neural Networks and 2 Fully Connected Layers. At the stage of making a regional characteristic classification system using deep learning, there are several main process stages, namely data collection, system design, training, and testing. The processed dataset is a regional image dataset originating from the satellite.
References
Adler, J., & Pratama, T. B. (2018). Identifikasi Pola Warna Citra Google Maps Menggunakan Jaringan Syaraf Tiruan Metode Levenberg –Marquardt dengan MatLab Versi 7.8. Komputika : Jurnal Sistem Komputer, 7(2), 95–101. https://doi.org/10.34010/komputika.v7i2.1396
Al-Ghrairi, A. H. T., Abed, Z. H., Fadhil, F. H., & Naser, F. K. (2018). Classification of Satellite Images Based on Color Features Using Remote Sensing. International Journal of Computer, 31(1), 42–52. Retrieved from http://ijcjournal.org/
Arora, D., Garg, M., & Gupta, M. (2020). Diving deep in Deep Convolutional Neural Network. Proceedings - IEEE 2020 2nd International Conference on Advances in Computing, Communication Control and Networking, ICACCCN 2020, 749–751. https://doi.org/10.1109/ICACCCN51052.2020.9362907
Beik, I. S. (2010). Tiga Dimensi Zakat. Harian Republik.
Eka Putra, W. S. (2016). Klasifikasi Citra Menggunakan Convolutional Neural Network (CNN) pada Caltech 101. Jurnal Teknik ITS, 5(1). https://doi.org/10.12962/j23373539.v5i1.15696
Fadlia, N., & Kosasih, R. (2019). Klasifikasi Jenis Kendaraan Menggunakan Metode Convolutional Neural Network (Cnn). Jurnal Ilmiah Teknologi Dan Rekayasa, 24(3), 207–215. https://doi.org/10.35760/tr.2019.v24i3.2397
From百度文库. (2013). 済無No Title No Title. Journal of Chemical Information and Modeling, 53(9), 1689–1699.
Ghandour, S. M., & Turab, N. M. (2016). Geographic maps classification based on L*A*B color system. International Journal of Computer Networks and Communications, 8(3), 81–92. https://doi.org/10.5121/ijcnc.2016.8306
Jianjun, G. A. O. (n.d.). Study on the Basic Tone of Thematic Map Color. 1005–1010.
Kristanto, Y., Agustin, T., & Muhammad, F. R. (2017). Pendugaan Karakteristik Awan berdasarkan Data Spektral Citra Satelit Resolusi Spasial Menengah Landsat 8 Oli / Tirs ( Studi Kasus : Provinsi Dki Jakarta ). Jurnal Meteorologi Klimatologi Dan Geofisika, 4(2), 42–51.
Leach, J. (1998). Dig@it. In New Scientist (Vol. 158). https://doi.org/10.1007/1-4020-0613-6_5006
Li, Y. (2020). Research on Application of Convolutional Neural Network in Intrusion Detection. Proceedings - 2020 7th International Forum on Electrical Engineering and Automation, IFEEA 2020, 720–723. https://doi.org/10.1109/IFEEA51475.2020.00153
Liu, Y., Zhong, Y., & Qin, Q. (2018). Scene classification based on multiscale convolutional neural network. IEEE Transactions on Geoscience and Remote Sensing, 56(12), 7109–7121. https://doi.org/10.1109/TGRS.2018.2848473
Lou, G., & Shi, H. (2020). Face image recognition based on convolutional neural network. China Communications, 17(2), 117–124. https://doi.org/10.23919/JCC.2020.02.010
Maulana, F. F., & Rochmawati, N. (2019). Klasifikasi Citra Buah Menggunakan Convolutional Neural Network. Journal of Informatics and Computer Science, 01, 104–108.
Putra, I. (2018). Klasifikasi Citra Satelit Dengan Menggunakan Algoritma K-Means. Proceeding Seminar Nasional Sistem …, 881–884. Retrieved from http://sisfotenika.stmikpontianak.ac.id/index.php/sensitek/article/view/913
Putra, I. W. K. E. (2017). Pemanfaatan Citra Pengideraan Jauh Pada Google Earth Untuk Pembuatan Peta Citra Di Kecamatan Marga, Kabupaten Tabanan. Media Komunikasi Geografi, 18(1), 54–65. https://doi.org/10.23887/mkg.v18i1.10557
Rozaqi, A. J., Sunyoto, A., & Arief, M. rudyanto. (2021). Deteksi Penyakit Pada Daun Kentang Menggunakan Pengolahan Citra dengan Metode Convolutional Neural Network. Creative Information Technology Journal, 8(1), 22. https://doi.org/10.24076/citec.2021v8i1.263
Samudre, P., Shende, P., & Jaiswal, V. (2019). Optimizing Performance of Convolutional Neural Network Using Computing Technique. 2019 IEEE 5th International Conference for Convergence in Technology, I2CT 2019, 2019–2022. https://doi.org/10.1109/I2CT45611.2019.9033876
Shen, W., & Wang, W. (2018). Node identification in wireless network based on convolutional neural network. Proceedings - 14th International Conference on Computational Intelligence and Security, CIS 2018, 238–241. https://doi.org/10.1109/CIS2018.2018.00059
Supianto, A. A. (2015). Klasifikasi Citra Satelit Menggunakan Kombinasi Fitur Warna Dan Fitur Tekstur. Jurnal Teknologi Informasi Dan Ilmu Komputer, 2(2), 102. https://doi.org/10.25126/jtiik.201522141
Uçkun, F. A., Özer, H., & Nurba, E. (2020). EVR øùø ML ø S ø N ø R A ö LARI ve EVR øùø ML ø TEKRARLAYAN S ø N ø R A ö LARI KULLANARAK YÖN KEST ø R ø M ø DIRECTION FINDING USING CONVOLUTIONAL NEURAL NETWORKS and CONVOLUTIONAL RECURRENT NEURAL NETWORKS. 8–11.
Wiley, V., & Lucas, T. (2018). Computer Vision and Image Processing: A Paper Review. International Journal of Artificial Intelligence Research, 2(1), 22. https://doi.org/10.29099/ijair.v2i1.42
Xin, R., Zhang, J., & Shao, Y. (2020). Complex network classification with convolutional neural network. Tsinghua Science and Technology, 25(4), 447–457. https://doi.org/10.26599/TST.2019.9010055
Zhou, Y., & Cui, J. (2020). Research and Improvement of Encrypted Traffic Classification Based on Convolutional Neural Network. 2020 IEEE 8th International Conference on Computer Science and Network Technology, ICCSNT 2020, 150–154. https://doi.org/10.1109/ICCSNT50940.2020.9305018
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