Research Article
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Investigating the performance of super-resolved remote sensing images on coastline segmentation with deep learning based methods

Year 2025, Volume: 10 Issue: 1, 93 - 106

İlhan Pala , Ugur Algancı

https://doi.org/10.26833/ijeg.1522143

Abstract

The use of satellite imagery in critical areas, such as environmental monitoring and natural disaster management, is becoming increasingly important. Applications like monitoring coastal areas, detecting coastal erosion, and tracking land use changes demand high accuracy and detailed analysis. Traditional methods for coastline segmentation are often limited by the low resolution (LR) and high complexity of satellite imagery. To address this challenge, Super Resolution (SR) algorithms are employed to enhance the resolution of satellite images, which is particularly beneficial when examining areas with intricate structures, such as coastlines. In this context, the integration of SR and segmentation techniques presents an innovative approach to achieving greater accuracy and efficiency in satellite image analysis. In this study, the resolution of satellite images was enhanced using the Super Resolution Generative Adversarial Networks (SRGAN) model. Thanks to the flexible architecture of the SRGAN model, it was successfully adapted to work with satellite images, yielding satisfactory results. Coastal segmentation was performed using low-resolution, super-resolved, and high-resolution Gokturk-1 (GT-1) satellite images, employing U-net, LinkNet, and DeepLabV3+ segmentation models for comparison. The results indicated that increment in image resolution significantly affects segmentation success. Additionally, better performance in coastline segmentation was achieved with U-net and LinkNet models. Although the DeepLabV3+ model is effective for segmentation, it tends to capture less detail compared to the other two models. Overall, the combination of SRGAN and the LinkNet segmentation model produced results that were closest to reality

Keywords

Remote Sensing Super Resolution Image Processing Coastline Segmentation Deep Learning

Supporting Institution

TUBITAK

Project Number

121Y366

References

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  • Badrinarayanan, V., Kendall, A., Cipolla, R. (2017). SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 39(12), 2481 - 2495.
  • Zhao, H., Shi, J., Qi, X., Wang, X., Jia, J. (2016). Pyramid Scene Parsing Network (Version 2). arXiv, https://doi.org/10.48550/ARXIV.1612.01105
  • Jégou, S., Drozdzal, M., Vazquez, D., Romero, A., Bengio, Y. (2016). The One Hundred Layers Tiramisu: Fully Convolutional DenseNets for Semantic Segmentation (Version 3), arXiv, https://doi.org/10.48550/ARXIV.1611.09326
  • Lin, G., Milan, A., Shen, C., Reid, I. (2016). RefineNet: Multi-Path Refinement Networks for High-Resolution Semantic Segmentation (Version 3). arXiv, https://doi.org/10.48550/ARXIV.1611.06612
  • Lin, T-Y., Dollár, P., Girshick, R., He, K., Hariharan, B., Belongie, S. (2016). Feature Pyramid Networks for Object Detection (Version 2). arXiv, https://doi.org/10.48550/ARXIV.1612.03144
  • He, K., Zhang, X., Ren, S., Sun, J. (2016). Deep Residual Learning for Image Recognition. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA, 770-778
  • He, K., Zhang, X., Ren, S., Sun, J. (2015). Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification (Version 1). arXiv, https://doi.org/10.48550/ARXIV.1502.01852
  • Simonyan, K., Zisserman, A. (2014). Very Deep Convolutional Networks for Large-Scale Image Recognition (Version 6). arXiv, https://doi.org/10.48550/ARXIV.1409.1556
  • Chen, L-C., Papandreou, G., Kokkinos, I., Murphy, K., Yuille, A. L. (2018). DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs. IEEE Transactions on Pattern Analysis and Machine Intelligence, 40(10), 834 - 848.
  • Akar, Ö., Saralıoğlu, E., Güngör, O., Bayata, H. F. (2024). Semantic segmentation of very-high spatial resolution satellite images: A comparative analysis of 3D-CNN and traditional machine learning algorithms for automatic vineyard detection. International Journal of Engineering and Geosciences, 9(1), 12-24.
  • Mogaraju, J. K. (2024). Machine learning assisted prediction of land surface temperature (LST) based on major air pollutants over the Annamayya District of India. International Journal of Engineering and Geosciences, 9(2), 233-246.
  • Çetin, Ş. B. (2023). Real-ESRGAN: A deep learning approach for general image restoration and its application to aerial images. Advanced Remote Sensing, 3(2), 90–99.

Year 2025, Volume: 10 Issue: 1, 93 - 106

İlhan Pala , Ugur Algancı

https://doi.org/10.26833/ijeg.1522143

Abstract

Project Number

121Y366

References

  • Saleem, A., Mahmood, S. (2023). Spatio-temporal assessment of urban growth using multi-stage satellite imageries in Faisalabad, Pakistan. Advanced Remote Sensing, 3(1), 10–18.
  • Zadbagher, E., Marangoz, A.M, Becek, K. (2023). Characterizing and estimating forest structure using active remote sensing: An overview. Advanced Remote Sensing, 3(1), 38–46.
  • Efe, E., Algancı, U. (2023). Çok zamanlı Sentinel 2 uydu görüntüleri ve makine öğrenmesi tabanlı algoritmalar ile arazi örtüsü değişiminin belirlenmesi. Geomatik, 8(1), 27-34.
  • Yiğit, A.Y., Şenol, H.İ., Kaya, Y. (2022). Çok zamanlı multispektral uydu verilerinin Marmara Gölü kıyı değişimi analizinde kullanılması. Geomatik. 2022, 7(3), 253-260.
  • Bakırman, T., Sertel, E. (2023). A benchmark dataset for deep learning-based airplane detection: HRPlanes. International Journal of Engineering and Geosciences, 8(3), 212-223.
  • Goodfellow, I.J., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., … & Bengio, Y. (2014). Generative Adversarial Networks (Version 1). arXiv, https://doi.org/10.48550/ARXIV.1406.2661
  • Ledig, C., Theis, L., Huszar, F., Caballero, J., Cunningham, A., Acosta, A., … & Shi, W. (2016). Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network (Version 5). arXiv, https://doi.org/10.48550/ARXIV.1609.04802
  • Salgueiro Romero, L., Marcello, J., Vilaplana, V. (2020). Super-Resolution of Sentinel-2 Imagery Using Generative Adversarial Networks. Remote Sensing, 12(15), 2424.
  • Xiong, Y., Guo, S., Chen, J., Deng, X., Sun, L., Zheng, X., Xu, W. (2020). Improved SRGAN for Remote Sensing Image Super-Resolution Across Locations and Sensors. Remote Sensing, 12(8), 1263.
  • 10.Ronneberger, O., Fischer, P., Brox, T. (2015). U-Net: Convolutional Networks for Biomedical Image Segmentation. Medical Image Computing and Computer-Assisted Intervention – MICCAI 2015, 234-241, https://doi.org/10.1007/978-3-319-24574-4_28
  • 11.Chaurasia, A., Culurciello, E. (2017). LinkNet: Exploiting encoder representations for efficient semantic segmentation. IEEE Visual Communications and Image Processing (VCIP), St. Petersburg, FL, USA, 1- 4. https://doi.org/10.1109/vcip.2017.8305148
  • Chen, L-C., Papandreou, G., Kokkinos, I., Murphy, K., Yuille, A. L. (2014). Semantic Image Segmentation with Deep Convolutional Nets and Fully Connected CRFs (Version 4). arXiv, https://doi.org/10.48550/ARXIV.1412.7062
  • Chen, L-C., Zhu, Y., Papandreou, G., Schroff, F., Adam, H. (2018). Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation (Version 3). arXiv, https://doi.org/10.48550/ARXIV.1802.02611
  • Alsabhan, W., Alotaiby, T. (2022). Automatic Building Extraction on Satellite Images Using Unet and ResNet50. Computational Intelligence and Neuroscience, (1), 5008854.
  • 15.Sariturk, B., Seker, D.Z., Ozturk, O., Bayram, B. (2022). Performance evaluation of shallow and deep CNN architectures on building segmentation from high-resolution images. Earth Science Informatics, 15, 1801–1823.
  • Zhang, Z., Liu, Q., Wang, Y. (2018). Road Extraction by Deep Residual U-Net. IEEE Geoscience and Remote Sensing Letters, 15(5), 749-753.
  • Han, J., Wang, Z., Wang, Y., Hou, W. (2022). Building extraction algorithm from remote sensing images based on improved DeepLabv3+ network. Journal of Physics: Conference Series, 2303, 012010.
  • Kaya, Y., Şenol, H. İ., Yiğit, A. Y., & Yakar, M. (2023). Car detection from very high-resolution UAV images using deep learning algorithms. Photogrammetric Engineering & Remote Sensing, 89(2), 117-123.
  • Şenol, H. İ., Kaya, Y., Yiğit, A. Y., & Yakar, M. (2024). Extraction and geospatial analysis of the Hersek Lagoon shoreline with Sentinel-2 satellite data. Survey Review, 56(397), 367-382.
  • Yang, T., Jiang, S., Hong, Z., Zhang, Y., Han, Y., Zhou, R., … & Kuc, T. (2020). Sea-Land Segmentation Using Deep Learning Techniques for Landsat-8 OLI Imagery. Marine Geodesy, 43(2), 105-133.
  • Panuntun, I. A., Chen, Y-N., Jamaluddin, I., Tran, T. L. C. (2024). Evaluation of Deep Learning Semantic Segmentation for Land Cover Mapping on Multispectral, Hyperspectral and High Spatial Aerial Imagery. arXiv ,https://doi.org/10.48550/ARXIV.2406.14220 Ünel, F. B., Kuşak, L., Çelik, M., Alptekin, A., & Yakar, M. (2020). Kıyı çizgisinin belirlenerek mülkiyet durumunun incelenmesi. Türkiye Arazi Yönetimi Dergisi, 2(1), 33-40.
  • Badrinarayanan, V., Kendall, A., Cipolla, R. (2017). SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 39(12), 2481 - 2495.
  • Zhao, H., Shi, J., Qi, X., Wang, X., Jia, J. (2016). Pyramid Scene Parsing Network (Version 2). arXiv, https://doi.org/10.48550/ARXIV.1612.01105
  • Jégou, S., Drozdzal, M., Vazquez, D., Romero, A., Bengio, Y. (2016). The One Hundred Layers Tiramisu: Fully Convolutional DenseNets for Semantic Segmentation (Version 3), arXiv, https://doi.org/10.48550/ARXIV.1611.09326
  • Lin, G., Milan, A., Shen, C., Reid, I. (2016). RefineNet: Multi-Path Refinement Networks for High-Resolution Semantic Segmentation (Version 3). arXiv, https://doi.org/10.48550/ARXIV.1611.06612
  • Lin, T-Y., Dollár, P., Girshick, R., He, K., Hariharan, B., Belongie, S. (2016). Feature Pyramid Networks for Object Detection (Version 2). arXiv, https://doi.org/10.48550/ARXIV.1612.03144
  • He, K., Zhang, X., Ren, S., Sun, J. (2016). Deep Residual Learning for Image Recognition. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA, 770-778
  • He, K., Zhang, X., Ren, S., Sun, J. (2015). Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification (Version 1). arXiv, https://doi.org/10.48550/ARXIV.1502.01852
  • Simonyan, K., Zisserman, A. (2014). Very Deep Convolutional Networks for Large-Scale Image Recognition (Version 6). arXiv, https://doi.org/10.48550/ARXIV.1409.1556
  • Chen, L-C., Papandreou, G., Kokkinos, I., Murphy, K., Yuille, A. L. (2018). DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs. IEEE Transactions on Pattern Analysis and Machine Intelligence, 40(10), 834 - 848.
  • Akar, Ö., Saralıoğlu, E., Güngör, O., Bayata, H. F. (2024). Semantic segmentation of very-high spatial resolution satellite images: A comparative analysis of 3D-CNN and traditional machine learning algorithms for automatic vineyard detection. International Journal of Engineering and Geosciences, 9(1), 12-24.
  • Mogaraju, J. K. (2024). Machine learning assisted prediction of land surface temperature (LST) based on major air pollutants over the Annamayya District of India. International Journal of Engineering and Geosciences, 9(2), 233-246.
  • Çetin, Ş. B. (2023). Real-ESRGAN: A deep learning approach for general image restoration and its application to aerial images. Advanced Remote Sensing, 3(2), 90–99.
There are 33 citations in total.

Details

Primary Language English
Subjects Photogrammetry and Remote Sensing
Journal Section Research Article
Authors

İlhan Pala 0009-0002-1600-3559

Ugur Algancı 0000-0002-5693-3614

Project Number 121Y366
Publication Date
Submission Date July 25, 2024
Acceptance Date October 11, 2024
Published in Issue Year 2025 Volume: 10 Issue: 1

Cite

APA Pala, İ., & Algancı, U. (n.d.). Investigating the performance of super-resolved remote sensing images on coastline segmentation with deep learning based methods. International Journal of Engineering and Geosciences, 10(1), 93-106. https://doi.org/10.26833/ijeg.1522143
AMA Pala İ, Algancı U. Investigating the performance of super-resolved remote sensing images on coastline segmentation with deep learning based methods. IJEG. 10(1):93-106. doi:10.26833/ijeg.1522143
Chicago Pala, İlhan, and Ugur Algancı. “Investigating the Performance of Super-Resolved Remote Sensing Images on Coastline Segmentation With Deep Learning Based Methods”. International Journal of Engineering and Geosciences 10, no. 1 n.d.: 93-106. https://doi.org/10.26833/ijeg.1522143.
EndNote Pala İ, Algancı U Investigating the performance of super-resolved remote sensing images on coastline segmentation with deep learning based methods. International Journal of Engineering and Geosciences 10 1 93–106.
IEEE İ. Pala and U. Algancı, “Investigating the performance of super-resolved remote sensing images on coastline segmentation with deep learning based methods”, IJEG, vol. 10, no. 1, pp. 93–106, doi: 10.26833/ijeg.1522143.
ISNAD Pala, İlhan - Algancı, Ugur. “Investigating the Performance of Super-Resolved Remote Sensing Images on Coastline Segmentation With Deep Learning Based Methods”. International Journal of Engineering and Geosciences 10/1 (n.d.), 93-106. https://doi.org/10.26833/ijeg.1522143.
JAMA Pala İ, Algancı U. Investigating the performance of super-resolved remote sensing images on coastline segmentation with deep learning based methods. IJEG.;10:93–106.
MLA Pala, İlhan and Ugur Algancı. “Investigating the Performance of Super-Resolved Remote Sensing Images on Coastline Segmentation With Deep Learning Based Methods”. International Journal of Engineering and Geosciences, vol. 10, no. 1, pp. 93-106, doi:10.26833/ijeg.1522143.
Vancouver Pala İ, Algancı U. Investigating the performance of super-resolved remote sensing images on coastline segmentation with deep learning based methods. IJEG. 10(1):93-106.