Segmentasi Awan Pada Citra Satelit Multispektral Menggunakan Convolutional Neural Networks
DOI:
https://doi.org/10.25126/jtiik.2025125Kata Kunci:
cloud segmentation, satellite imagery, sentinel-2, deep learning, convolutional neural networks, CNNAbstrak
Citra satelit multispektral adalah jenis citra yang diambil oleh satelit penginderaan jauh yang menangkap informasi dari berbagai rentang spektrum elektromagnetik. Citra satelit multispektral memiliki peran yang sangat penting karena kemampuannya untuk memberikan informasi untuk area yang luas secara berkala. Akan tetapi, salah satu permasalahan utama dari citra satelit multispektral adalah kontaminasi awan. Tutupan awan pada area yang luas menyebabkan informasi yang ada pada citra satelit menjadi bias. Oleh karena itu, segmentasi awan yang akurat pada citra satelit multispektral menjadi sangat penting. Penelitian ini berfokus untuk mengembangkan model segmentasi awan berbasis Convolutional Neural Networks (CNN) dengan kinerja yang baik. Penelitian diawali dengan proses pembuatan dataset citra satelit multispektral Sentinel-2 Level-2A. Algoritma s2cloudless digunakan untuk membentuk label dengan 4 kelas, yaitu: shadow, clear, cirrus, dan cloud. Selanjutnya, model segmentasi awan berbasis CNN dikembangkan berdasarkan beberapa model segmentasi semantik yang ada. Model tersebut dilatih dan dievaluasi pada 11.240 citra yang telah dibuat sebelumnya. Melalui ablation study, diperoleh model segmentasi awan terbaik yaitu Deeplabv3+ dengan backbone ResNet18. Arsitektur tersebut memberikan kinerja yang sangat menjanjikan dengan nilai F1-score, pixel accuracy, dan mIoU sebesar 0.922, 0.923, dan 0.733 secara berurutan. Dengan demikian penelitian terkait citra satelit diharapkan menjadi lebih akurat dalam melakukan klasifikasi atau prediksi objek yang ada di permukaan bumi.
Abstract
Multispectral satellite imagery is a type of imagery captured by remote sensing satellites that record data from various ranges of the electromagnetic spectrum. Its importance lies in its ability to provide information over large areas periodically. However, one of the main challenges with multispectral satellite imagery is cloud contamination. Cloud cover over large regions can bias the information captured in the imagery. Therefore, accurate cloud segmentation in multispectral satellite imagery is crucial. This study focuses on developing a high-performance cloud segmentation model based on Convolutional Neural Networks (CNN). The research began with the creation of a multispectral satellite imagery dataset from Sentinel-2 Level-2A. Labels with four classes—shadow, clear, cirrus, and cloud—were generated using the s2cloudless algorithm. Subsequently, a CNN-based cloud segmentation model was developed using several existing semantic segmentation models. The model was trained and evaluated on 11,240 images from the dataset. Through an ablation study, the best cloud segmentation model was identified as Deeplabv3+ with a ResNet18 backbone. This architecture demonstrated a highly promising performance, achieving F1-score, pixel accuracy, and mIoU values of 0.922, 0.923, and 0.733, respectively. As a result, this research is expected to improve the accuracy of satellite imagery classification and object prediction on the Earth's surface.
Downloads
Referensi
BPS GARUT, 2024. Kabupaten Garut Dalam Angka Tahun 2023. Garut: Badan Pusat Statistik.
BPS INDRAMAYU, 2024. Kabupaten Indramayu Dalam Angka Tahun 2023. Indramayu: Badan Pusat Statistik.
CHEN, L.C., ZHU, Y., PAPANDREOU, G., SCHROFF, F., dan ADAM, H., 2018. Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation. In Proceedings of the European conference on computer vision (ECCV) (pp. 801-818).
GALDRAN, A., CARNEIRO, G., dan BALLESTER, M.Á.G., 2022. On The Optimal Combination of Cross-Entropy and Soft Dice Losses for Lesion Segmentation with Out-of-Distribution Robustness. In Diabetic Foot Ulcers Grand Challenge (pp. 40-51). Cham: Springer International Publishing.
GORELICK, N., HANCHER, M., DIXON, M., ILYUSHCHENKO, S., THAU, D., dan MOORE, R., 2017. Google Earth Engine: Planetary-scale Geospatial Analysis for Everyone. Remote Sensing of Environment, 202, 18–27.
HE, K., ZHANG, X., REN, S., dan SUN, J., 2015. Deep Residual Learning for Image Recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 770-778).
HOWARD, A., SANDLER, M., CHU, G., CHEN, L.C., CHEN, B., TAN, M., WANG, W., ZHU, Y., PANG, R., VASUDEVAN, V., LE, Q.V., dan ADAM, H., 2019. Searching for MobileNetV3. In Proceedings of the IEEE/CVF international conference on computer vision (pp. 1314-1324).
HU, K., ZHANG, D., dan XIA, M., 2021. CDUNet: Cloud Detection UNet for Remote Sensing Imagery. Remote Sensing, 13(22).
JEONG, S., KO, J., dan YEOM, J. M., 2022. Predicting Rice Yield at Pixel Scale Through Synthetic Use of Crop and Deep Learning Models with Satellite Data in South and North Korea. Science of the Total Environment, 802.
LI, J., ZHANG, Y., SHENG, Q., WU, Z., WANG, B., HU, Z., SHEN, G., SCHMITT, M., dan MOLINIER, M., 2022. Thin Cloud Removal Fusing Full Spectral and Spatial Features for Sentinel-2 Imagery. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 15, 8759–8775.
LI, J., WU, Z., HU, Z., LI, Z., WANG, Y., dan MOLINIER, M., 2021. Deep Learning Based Thin Cloud Removal Fusing Vegetation Red Edge and Short-Wave Infrared Spectral Information for Sentinel-2A Imagery. Remote Sensing, 13(1), 1–31.
LI, J., dan WANG, Q., 2024. CSDFormer: A Cloud and Shadow Detection Method for Landsat Images Based on Transformer. International Journal of Applied Earth Observation and Geoinformation, 129.
LI, X., YANG, X., LI, X., LU, S., YE, Y., & BAN, Y., 2022. GCDB-UNet: A Novel Robust Cloud Detection Approach for Remote Sensing Images. Knowledge-Based Systems 238: 107890.
LIN, T.Y., DOLLÁR, P., GIRSHICK, R., HE, K., HARIHARAN, B., dan BELONGIE, S., 2016. Feature Pyramid Networks for Object Detection. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 2117-2125).
LOSHCHILOV, I., dan HUTTER, F., 2016. SGDR: Stochastic Gradient Descent with Warm Restarts. In International Conference on Learning Representations, ICLR 2017.
LOSHCHILOV, I., dan HUTTER, F., 2017. Decoupled Weight Decay Regularization. arXiv preprint arXiv:1711.05101 (2017).
PETTORELLI, N., SCHULTE TOBÜHNE, H., SHAPIRO, A.C., dan GLOVER-KAPFER, P., 2018. Satellite Remote Sensing for Conservation. WWF Conservation Technology Series 1(4). WWF.
PU, W., WANG, Z., LIU, D., dan ZHANG, Q., 2022. Optical Remote Sensing Image Cloud Detection with Self-Attention and Spatial Pyramid Pooling Fusion. Remote Sensing, 14(17).
QIU, S., ZHU, Z., dan HE, B., 2019. Fmask 4.0: Improved Cloud and Cloud Shadow Detection in Landsats 4–8 and Sentinel-2 Imagery. Remote Sensing of Environment 231: 111205.
RADOSAVOVIC, I., KOSARAJU, R.P., GIRSHICK, R., HE, K., dan DOLLÁR, P., 2020. Designing Network Design Spaces. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition (pp. 10428-10436).
RICHTER, R., WANG, X., BACHMANN, M., SCHLÄPFER, D., 2011. Correction of Cirrus Effects in Sentinel-2 Type of Imagery. International Journal of Remote Sensing 32, 2931-2941.
TAN, H., SUN, S., CHENG, T., & SHU, X., 2024. Transformer-Based Cloud Detection Method for High-Resolution Remote Sensing Imagery. Computers, Materials and Continua 80 (1): 661-678.
TAN, M., dan LE, Q., 2019. EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks. In International Conference on Machine Learning, ICML 2019 (pp. 6105-6114).
TAN, Y., ZHANG, W., YANG, X., LIU, Q., MI, X., LI, J., YANG, J., & GU, X., 2023. Cloud and Cloud Shadow Detection of GF-1 Images Based on the Swin-UNet Method. Atmosphere 14 (11): 1669.
THORP, K.R., dan DRAJAT, D., 2021. Deep machine learning with Sentinel satellite data to map paddy rice production stages across West Java, Indonesia. Remote Sensing of Environment, 265.
WANG, B., ONO, A., MURAMATSU, K., FUJIWARA, N., 1999. Automated Detection and Removal of Clouds and Their Shadows from Landsat TM Images. IEICE Transactions on information and systems, 82(2), pp.453-460.
WIJAYA, B.S., MUNIR, R., dan UTAMA, N.P., 2023. Curating multimodal satellite imagery for precision agriculture datasets with Google Earth Engine. International Conference of Data Science and Official Statistics.
ZHANG, J., WU, J., WANG, H., WANG, Y., & LI, Y., 2021. Cloud Detection Method using CNN based on Cascaded Feature Attention and Channel Attention. IEEE Transactions on Geoscience and Remote Sensing 60: 1-17.
ZHAO, H., SHI, J., QI, X., WANG, X., dan JIA, J., 2016. Pyramid Scene Parsing Network. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 2881-2890).
Unduhan
Diterbitkan
Terbitan
Bagian
Lisensi
Hak Cipta (c) 2025 Jurnal Teknologi Informasi dan Ilmu Komputer
Artikel ini berlisensiCreative Commons Attribution-ShareAlike 4.0 International License.
Artikel ini berlisensi Creative Common Attribution-ShareAlike 4.0 International (CC BY-SA 4.0)
Penulis yang menerbitkan di jurnal ini menyetujui ketentuan berikut:
- Penulis menyimpan hak cipta dan memberikan jurnal hak penerbitan pertama naskah secara simultan dengan lisensi di bawah Creative Common Attribution-ShareAlike 4.0 International (CC BY-SA 4.0) yang mengizinkan orang lain untuk berbagi pekerjaan dengan sebuah pernyataan kepenulisan pekerjaan dan penerbitan awal di jurnal ini.
- Penulis bisa memasukkan ke dalam penyusunan kontraktual tambahan terpisah untuk distribusi non ekslusif versi kaya terbitan jurnal (contoh: mempostingnya ke repositori institusional atau menerbitkannya dalam sebuah buku), dengan pengakuan penerbitan awalnya di jurnal ini.
- Penulis diizinkan dan didorong untuk mem-posting karya mereka online (contoh: di repositori institusional atau di website mereka) sebelum dan selama proses penyerahan, karena dapat mengarahkan ke pertukaran produktif, seperti halnya sitiran yang lebih awal dan lebih hebat dari karya yang diterbitkan. (Lihat Efek Akses Terbuka).
