Satellite Images Classification using MobileNet V-2 Algorithm


  • Bayu Angga Wijaya Universitas Prima Indonesi, Indonesia
  • Perisman Jaya Gea Universitas Prima Indonesi, Indonesia
  • Areta Delano Gea Universitas Prima Indonesi, Indonesia
  • Alvianus Sembiring Universitas Prima Indonesi, Indonesia
  • Christian Mitro Septiano Hutagalung Universitas Prima Indonesi, Indonesia




Satellite Images; RSI-CB256 Dataset; Classification; Object Recognition; MobileNet V-2;


Satellite imagery is an invaluable source of visual information for environmental monitoring and land mapping with high resolution and wide coverage. In this modern technological era, advances in Deep Learning technology have brought great benefits in utilizing satellite images for various purposes. One of the efficient Deep Learning models for satellite image classification is MobileNet V-2, which is specifically designed for devices with limited resources such as smartphones. This study aims to develop an accurate satellite image classification model using Convolutional Neural Network algorithm and MobileNet V-2 model. The data used is taken from the RSI-CB256 dataset developed through crowdsourcing data. This research resulted in the performance of three deep learning models, namely ResNet50, MobileNet V-2, and VGG-16. ResNet50 is the highest model performed best during the training phase, achieve an accuracy of 98.40%. MobileNet V-2 and VGG-16 followed with 95.64% and 96.62% accuracy, respectively. The evaluation results demonstrate the model's strong ability to accurately classify satellite imagery and strengthen the model's ability to generalize well. With high accuracy and the ability to run on smartphone devices, this model has the potential to provide valuable information for governments and scientists in preserving the earth and better responding to environmental changes.

GS Cited Analysis


Download data is not yet available.


Burra, R.L., Karuna, S., Tumma, S., Marlapalli, K, & Tumuluru, P. (2022). MobileNetV2-based Transfer Learning Model with Edge Computing for Automatic Fabric Defect Detection. J Sci Ind Res (India), vol. 82, no. 1, pp. 128–134.

Choi, K & Sobelman, G. E. (2022). An efficient CNN accelerator for low-cost edge systems. ACM Trans Embed Comput Syst, vol. 21, no. 4, pp. 1–20.

Firmansyah, S., Gaol, J.L & Susilo, S.B. (2019). Perbandingan klasifikasi SVM dan Decision Tree untuk pemetaan mangrove berbasis objek menggunakan citra satelit Sentinel-2B di Gili Sulat, Lombok Timur. Jurnal Pengelolaan Sumberdaya Alam dan Lingkungan (Journal of Natural Resources and Environmental Management), vol. 9, no. 3, pp. 746–757.

Grandini, M., Bagli, E & Visani, G. (2020). Metrics for multi-class classification: an overview. arXiv preprint arXiv:2008.05756.

Hadyanti, A. M. & Rudiawan, B. (2022). PENTINGNYA SATELIT UNTUK MENDUKUNG STRATEGI PERTAHANAN LAUT INDONESIA KHUSUSNYA DI WILAYAH CHOKEPOINT. Jurnal Strategi Pertahanan Laut, vol. 7, no. 3, pp. 195–202.

Hallman, J. (2019). A comparative study on Linear Regression and Neural Networks for estimating order quantities of powder blends. Degree Project Computer Science and Engineering, pp. 41–42.

Magdalena, R., Saidah, S., Pratiwi, N.K.C & Putra, A.T. (2021). Klasifikasi Tutupan Lahan Melalui Citra Satelit SPOT-6 dengan Metode Convolutional Neural Network (CNN). JEPIN (Jurnal Edukasi dan Penelitian Informatika), vol. 7, no. 3, pp. 335–339.

Miranda, E & Aryuni, M (2021). Klasifikasi Tutupan Lahan Menggunakan Convolutional Neural Network pada Citra Satelit Sentinel-2. SISTEMASI, vol. 10, no. 2, pp. 323–335.

Muthukumar, V. et al. (2021). Classification vs regression in overparameterized regimes: Does the loss function matter?. The Journal of Machine Learning Research, vol. 22, no. 1, pp. 10104–10172.

Prioko, K.L. (2020). Convolutional Neural Network Untuk Klasifikasi Citra Asap Pada Gambar Satelit. Institut Teknologi Sepuluh Nopember.

Simarmata, N., Elyza, F., & Vatiady, R. (2019). Kajian Citra Satelit Spot-7 Untuk Estimasi Standing Carbon Stock Hutan Mangrove Dalam Upaya Mitigasi Perubahan Iklim (Climate Changes) di Lampung Selatan. Jurnal Penginderaan Jauh dan Pengolahan Data Citra Digital, vol. 16, no. 1, pp. 1–8.

Song, X., Sun, Y., Mustafa, M.A., & Cordeiro, L.C. (2023). QNNRepair: Quantized Neural Network Repair. arXiv preprint arXiv:2306.13793.

Tazin, T., Sarker, S., Gupta, P & Ayaz, F.I. (2021). A robust and novel approach for brain tumor classification using convolutional neural network,” Comput Intell Neurosci.

Tehsin, S., Kausar, S., Jameel, A., Humayun, M & Almofarreh, D.K. (2023). Satellite Image Categorization Using Scalable Deep Learning. Applied Sciences (Switzerland), vol. 13, no. 8, doi: 10.3390/app13085108.

Wijaya, B. A., Nugraha, A., Juandry, J., Okinawa, J. and Kinoto, J. (2020) “Film Recommendation System with Social-Union Algorithm: Film Recommendation System with Social-Union Algorithm”, Jurnal Mantik, 4(2), pp. 1278-1284. doi: 10.35335/mantik.Vol4.2020.932.pp1278-1284.

Wijaya, B. A., Manalu, A. J. . ., Tarigan, B. A. . . and Silitonga, L. S. . . (2021) “Steganography Text Message Using LSB and DCT Methods”, Jurnal Mantik, 5(3), pp. 1825-1832. Available at: (Accessed: 17July2022)

Xiang, Q., Wang, X., Li, R., Zhang, G., Lai, J & Q. Hu. (2019). Fruit image classification based on Mobilenetv2 with transfer learning technique. Proceedings of the 3rd international conference on computer science and application engineering, pp. 1–7.

Yanan, G., Xiaoqun, C., Bainian, L &


Crossmark Updates

How to Cite

Wijaya, B. A., Perisman Jaya Gea, Gea, A. D. ., Alvianus Sembiring, & Christian Mitro Septiano Hutagalung. (2023). Satellite Images Classification using MobileNet V-2 Algorithm. Sinkron : Jurnal Dan Penelitian Teknik Informatika, 8(4), 2316-2326.