Super Resolution pada Citra Udara menggunakan Convolutional Neural Network

MUHAMMAD EFAN ABDULFATTAH, LEDYA NOVAMIZANTI, SYAMSUL RIZAL

Sari


ABSTRAK

Bencana di Indonesia didominasi oleh bencana hidrometeorologi yang mengakibatkan kerusakan dalam skala besar. Melalui pemetaan, penanganan yang menyeluruh dapat dilakukan guna membantu analisa dan penindakan selanjutnya. Unmanned Aerial Vehicle (UAV) dapat digunakan sebagai alat bantu pemetaan dari udara. Namun, karena faktor kamera maupun perangkat pengolah citra yang tidak memenuhi spesifikasi, hasilnya menjadi kurang informatif. Penelitian ini mengusulkan Super Resolution pada citra udara berbasis Convolutional Neural Network (CNN) dengan model DCSCN. Model terdiri atas Feature Extraction Network untuk mengekstraksi ciri citra, dan Reconstruction Network untuk merekonstruksi citra. Performa DCSCN dibandingkan dengan Super Resolution CNN (SRCNN). Eksperimen dilakukan pada dataset Set5 dengan nilai scale factor 2, 3 dan 4. Secara berurutan SRCNN menghasilkan nilai PSNR dan SSIM sebesar 36.66 dB / 0.9542, 32.75 dB / 0.9090 dan 30.49 dB / 0.8628. Performa DCSCN meningkat menjadi 37.614dB / 0.9588, 33.86 dB / 0.9225 dan 31.48 dB / 0.8851.

Kata kunci: citra udara, deep learning, super resolution

 

ABSTRACT

Disasters in Indonesia are dominated by hydrometeorological disasters, which cause large-scale damage. Through mapping, comprehensive handling can be done to help the analysis and subsequent action. Unmanned Aerial Vehicle (UAV) can be used as an aerial mapping tool. However, due to the camera and image processing devices that do not meet specifications, the results are less informative. This research proposes Super Resolution on aerial imagery based on Convolutional Neural Network (CNN) with the DCSCN model. The model consists of Feature Extraction Network for extracting image features and Reconstruction Network for reconstructing images. DCSCN's performance is compared to CNN Super Resolution (SRCNN). Experiments were carried out on the Set5 dataset with scale factor values 2, 3, and 4. The SRCNN sequentially produced PSNR and SSIM values of 36.66dB / 0.9542, 32.75dB / 0.9090 and 30.49dB / 0.8628. DCSCN's performance increased to 37,614dB / 0.9588, 33.86dB / 0.9225 and 31.48dB / 0.8851.

Keywords: aerial imagery, deep learning, super resolution


Kata Kunci


citra udara; deep learning; super resolution.

Teks Lengkap:

PDF

Referensi


Ahn, H., Chung, B., & Yim, C. (2019). Super-Resolution Convolutional Neural Networks Using Modified and Bilateral ReLU. International Conference on Electronics, Information, and Communication (ICEIC), (pp. 1–4).

Budiman, G., & Novamizanti, L. (2015). White Space Steganography On Text By Using LZWHuffman Double Compression, International Journal of Computer Networks & Communications, 7(2), 136A. https://doi.org/10.5121/ijcnc.2015.7210.

Dong, C., Loy, C.C., He, K., & Tang, X. (2016). Image Super-Resolution Using Deep Convolutional Networks. IEEE Transactions on Pattern Analysis and Machine Intelligence, 38(2), 295-307. https://doi.org/10.1109/TPAMI.2015.2439281.

Fadnavis, S. (2014). Image Interpolation Techniques in Digital Image Processing: An Overview. International Journal of Engineering Research and Applications (IJERA), 4(10), 70–73.

Géron, A. (2017). Hands-On Machine Learning with Scikit-Learn and TensorFlow. Sebastopol: O’Reilly.

Heaton, J. (2015). Artificial Intelligence for Humans, Volume 3: Deep Learning and Neural Networks (1st ed.; T. Heaton, Ed.). Chesterfield: Heaton Research, Inc.

Nurfitri, K., & Suyanto, M. (2016). Penilaian Kualitas Pemampatan Citra Pada Aplikasi-Aplikasi Instant Messenger. Jurnal Ilmiah Multitek Indonesia, 10(2), 78–90. https://doi.org/10.24269/mtkind.v10i2.346.

Lin, M., Chen, Q., & Shuicheng, Y. (2014). Network In Network. Neural and Evolutionary Computing. arXiv:1312.4400

Patel, P., Nandu, M., & Raut, P. (2018). Initialization of Weights in Neural Networks. International Journal of Scientific Development and Research (IJSDR), 3(11), 73–79.

Sewak, M., Karim, Md. R., & Pujari, P. (2018). Practical Convolutional Neural Networks. Birmingham: Packt Publishing Ltd.

Shahmoradi, J., Talebi, E., Roghanci, P., & Hassanalian, M. (2020). A Comprehensive Review of Applications of Drone Technology in the Mining Industry. Drones 2020, 4(34), 1-25. https://doi.org/10.3390/drones4030034

Simonyan, K., & Zisserman, A. (2015). Very Deep Convolutional Networks for Large-Scale Image Recognition. International Conference on Learning Representations (ICLR), (pp. 1–14).

Sun, Y., Zhang, W., Gu, H., Liu, C., Hong, S., Xu, W., & Gui, G. (2019). Convolutional Neural Network Based Models for Improving Super-Resolution Imaging. IEEE Access, 7, 43042–43051. https://doi.org/10.1109/ACCESS.2019.2908501.

Stenroos, O. (2017). Object Detection from Images Using CNN. Aalto University.

Suyanto. (2018). Machine Learning Tingkat Dasar dan Lanjut. Penerbit Informatika.

Tong, T., Li, G., Liu, X., & Gao, Q. (2017). Image Super-Resolution Using Dense Skip Connections. IEEE International Conference on Computer Vision (ICCV), (pp. 4799–4807). https://doi.org/10.1109/ICCV.2017.514.

Welle, D. (2019, September 16). Bencana Alam di Indonesia Tahun 2019 Diprediksi Terus Terjadi. Retrieved from https://www.dw.com/id/bencana-alam-di-indonesia-tahun-2019-diprediksi-terus-terjadi/a-46909297.

Wibawa, M. S. (2016). Pengaruh Fungsi Aktivasi, Optimisasi dan Jumlah Epoch Terhadap Performa Jaringan Saraf Tiruan. Jurnal Sistem Dan Informatika, 11(1), 167–174. https://doi.org/10.13140/RG.2.2.21139.94241.

Yamanaka, J., Kuwashima, S., & Kurita, T. (2017). Fast and Accurate Image Super Resolution by Deep CNN with Skip Connection and Network in Network. 24th International Conference of Neural Information Processing (ICONIP), (pp. 217–225).




DOI: https://doi.org/10.26760/elkomika.v9i1.71

Refbacks

  • Saat ini tidak ada refbacks.


_______________________________________________________________________________________________________________________

ISSN (print) : 2338-8323 | ISSN (electronic) : 2459-9638

Publisher:

Department of Electrical Engineering Institut Teknologi Nasional Bandung

Address: 20th Building  Institut Teknologi Nasional Bandung PHH. Mustofa Street No. 23 Bandung 40124

Contact: +627272215 (ext. 206)

Email: jte.itenas@itenas.ac.id________________________________________________________________________________________________________________________


Free counters!

Web Analytics Made Easy - StatCounter

Statistic Journal

Jurnal ini terlisensi oleh Creative Commons Attribution-ShareAlike 4.0 International License.

Creative Commons License