UMV2: Deep Learning Model for Semantic Segmentation of Satellite Imagery
DOI:
https://doi.org/10.22399/ijcesen.1362Keywords:
Satellite image segmentation, Unet++, MobileNetV2 encoder, deep learning model, land cover classification, environmental monitoringAbstract
Semantic segmentation is a computer vision task that categorizes each pixel in an image into a class or object. Although a number of relevant architectures have been proposed in recent years, they incur the problems like computational cost,large amounts of training data, class imbalance, edge uncertainty, varying sizes of objects, shadow and lighting variations. Such a more number of drawbacks degrades the semantic segmentation performance in terms of accuracy, efficiency and generalization capability. In this work, comprehensive architecture UMV2 for satellite image semantic segmentation is proposed. The UMV2 utilizing a fusion of Unet++ architecture and the lightweight MobileNetV2 encoder deep learning model. The Unet++ architecture, an extension of the widely adopted Unet, is employed for its ability to capture hierarchical features and enhance segmentation performance. Integrating MobileNetV2 as the encoder provides computational efficiency, making the model well-suited for resource- onstrained environments, such as satellite image analysis on edge devices. The proposed model leverages the strengths of both architectures, combining the expressive power of Unet++ with the efficiency of MobileNetV2. Extensive experiments are conducted on a diverse satellite image dataset, evaluating the model’s segmentation accuracy of 0.89, mean IOU of 0.52, precision of 0.80, recall of 0.83 and F1-score of 0.82 with the state of art methods. The results demonstrate the effectiveness of the proposed approach in achieving accurate and efficient satellite image segmentation, making it a promising solution for real-world applications in remote sensing and geospatial analysis.
References
Li, X., Li, J (2024). Mfca-net: a deep learning method for semantic segmentation of remote sensing images. Scientific Reports 14(1), 5745 DOI: https://doi.org/10.1038/s41598-024-56211-1
Jia, P., Chen, C., Zhang, D., Sang, Y., Zhang, L.: (2024). Semantic segmentation of deep learning remote sensing images based on band combination principle: Application in urban planning and land use. Computer Communications 217, 97–106 DOI: https://doi.org/10.1016/j.comcom.2024.01.032
Marrewijk, B.M., Dandjinou, C., Rustia, D.J.A., Gonzalez, N.F., Diallo, B., Dias, J., Melki, P., Blok, P.M. (2024) Active learning for efficient annotation in preci- sion agriculture: a use-case on crop-weed semantic segmentation. arXiv preprint arXiv:2404.02580
Wang, Z., Li, Z., Yu, X., Jia, Z., Xu, X., Schuller, B.W. (2024) Cross-scene semantic segmentation for medical surgical instruments using structural similarity based partial activation networks. IEEE Transactions on Medical Robotics and Bionics DOI: https://doi.org/10.1109/TMRB.2024.3359303
Do˘gan, G., Ergen, B. (2024) A new cnn-based semantic object segmentation for au- tonomous vehicles in urban traffic scenes. International Journal of Multimedia Information Retrieval 13(1), 11 DOI: https://doi.org/10.1007/s13735-023-00313-5
Ibrahem, ., Salem, A., Kang, H.-S.: Seg2depth (2024) Semi-supervised depth estima- tion for autonomous vehicles using semantic segmentation and single vanishing point fusion. IEEE Transactions on Intelligent Vehicles DOI: https://doi.org/10.1109/TIV.2024.3370930
Liao, Y., Kang, S., Li, J., Liu, Y., Liu, Y., Dong, Z., Yang, B., Chen, X. (2024) Mobile- seed: Joint semantic segmentation and boundary detection for mobile robots. IEEE Robotics and Automation Letters DOI: https://doi.org/10.1109/LRA.2024.3373235
Lee, C., Soedarmadji, S., Anderson, M., Clark, A.J., Chung, S.-J. (2024) Semantics from space: Satellite-guided thermal semantic segmentation annotation for aerial field robots. arXiv preprint arXiv:2403.14056 DOI: https://doi.org/10.1109/IROS58592.2024.10801479
Zahra, A., Ghafoor, M., Munir, K., Ullah, A., Ul Abideen, Z. (2024) Application of region-based video surveillance in smart cities using deep learning. Multimedia Tools and Applications 83(5), 15313–15338 DOI: https://doi.org/10.1007/s11042-021-11468-w
Sun, G., Liu, Y., Ding, H., Wu, M., Van Gool, L. (2024) Learning local and global tem- poral contexts for video semantic segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence DOI: https://doi.org/10.1109/TPAMI.2024.3387326
Yuan, K., Zhuang, X., Schaefer, G., Feng, J., Guan, L., Fang, H. (2021) Deep-learning- based multispectral satellite image segmentation for water body detection. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 14, 7422–7434 DOI: https://doi.org/10.1109/JSTARS.2021.3098678
Jia, H., Lang, C., Oliva, D., Song, W., Peng, X. (2019) Dynamic harris hawks optimiza- tion with mutation mechanism for satellite image segmentation. Remote sensing 11(12), 1421 DOI: https://doi.org/10.3390/rs11121421
Ghassemi, S., Fiandrotti, A., Francini, G., Magli, E. (2019) Learning and adapting ro- bust features for satellite image segmentation on heterogeneous data sets. IEEE Transactions on Geoscience and Remote Sensing 57(9), 6517–6529 DOI: https://doi.org/10.1109/TGRS.2019.2906689
Rahaman, J., Sing, M. (2021) An efficient multilevel thresholding based satellite image segmentation approach using a new adaptive cuckoo search algorithm. Expert Systems with Applications 174, 114633 DOI: https://doi.org/10.1016/j.eswa.2021.114633
Kotaridis, I., Lazaridou, M. (2021) Remote sensing image segmentation advances: A meta-analysis. ISPRS Journal of Photogrammetry and Remote Sensing 173, 309– 322 DOI: https://doi.org/10.1016/j.isprsjprs.2021.01.020
Pare, S., Kumar, A., Singh, G.K., Bajaj, V. (2020) Image segmentation using multi- level thresholding: a research review. Iranian Journal of Science and Technology, Transactions of Electrical Engineering 44, 1–29 DOI: https://doi.org/10.1007/s40998-019-00251-1
Gupta, A., Watson, S., Yin, H.(2021) Deep learning-based aerial image segmenta- tion with open data for disaster impact assessment. Neurocomputing 439, 22–33 DOI: https://doi.org/10.1016/j.neucom.2020.02.139
Iqbal, J., Ali, M. (2020) Weakly-supervised domain adaptation for built-up region seg- mentation in aerial and satellite imagery. ISPRS Journal of Photogrammetry and Remote Sensing 167, 263–275 DOI: https://doi.org/10.1016/j.isprsjprs.2020.07.001
Ovi, T.B., Mosharrof, S., Bashree, N., Islam, M.N., Islam, M.S.: Deeptrinet (2023).A tri-level attention-based deeplabv3+ architecture for semantic segmenta- tion of satellite images. In: International Conference on Human-Centric Smart Computing, pp. 373–384 DOI: https://doi.org/10.1007/978-981-99-7711-6_30
Fabel, Y., Nouri, B., Wilbert, S., Blum, N., Triebel, R., Hasenbalg, M., Kuhn, P., Zarzalejo, L.F., Pitz-Paal, R. (2022) Applying self-supervised learning for seman- tic cloud segmentation of all-sky images. Atmospheric Measurement Techniques 15(3), 797–809 DOI: https://doi.org/10.5194/amt-15-797-2022
Wagner, F.H., Dalagnol, R., S´anchez, A.H., Hirye, M., Favrichon, S., Lee, J.H., Mauceri, S., Yang, Y., Saatchi, S. (2022) K-textures, a self-supervised hard clus- tering deep learning algorithm for satellite image segmentation. Frontiers in Environmental Science 10, 946729 DOI: https://doi.org/10.3389/fenvs.2022.946729
Boulila, W., Khlifi, M.K., Ammar, A., Koubaa, A., Benjdira, B., Farah, I.R. (2022) A hybrid privacy-preserving deep learning approach for object classification in very high-resolution satellite images. Remote Sensing 14(18), 4631 DOI: https://doi.org/10.3390/rs14184631
Li, W., Chen, H., Shi, Z.(2021) Semantic segmentation of remote sensing images with self-supervised multitask representation learning. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 14, 6438–6450 DOI: https://doi.org/10.1109/JSTARS.2021.3090418
Li, H., Li, Y., Zhang, G., Liu, R., Huang, H., Zhu, Q., Tao, C. (2022) Global and local contrastive self-supervised learning for semantic segmentation of hr remote sensing images. IEEE Transactions on Geoscience and Remote Sensing 60, 1–14 DOI: https://doi.org/10.1109/TGRS.2022.3147513
Sun, W., Gao, Z., Cui, J., Ramesh, B., Zhang, B., Li, Z. (2021) Semantic segmentation leveraging simultaneous depth estimation. Sensors 21(3), 690 DOI: https://doi.org/10.3390/s21030690
Zu¨rn, J., Burgard, W., Valada, A. (2020) Self-supervised visual terrain classification from unsupervised acoustic feature learning. IEEE Transactions on Robotics 37(2), 466–481 DOI: https://doi.org/10.1109/TRO.2020.3031214
Dong, H., Ma, W., Wu, Y., Zhang, J., Jiao, L.(2020) Self-supervised representation learning for remote sensing image change detection based on temporal prediction. Remote Sensing 12(11), 1868 DOI: https://doi.org/10.3390/rs12111868
Shabbir, A., Ali, N., Ahmed, J., Zafar, B., Rasheed, A., Sajid, M., Ahmed, A., Dar, S.H. (2021) Satellite and scene image classification based on transfer learning and fine tuning of resnet50. Mathematical Problems in Engineering 2021, 1–18 DOI: https://doi.org/10.1155/2021/5843816
Aburaed, N., Al-Saad, M., Alkhatib, M., Zitouni, M., Almansoori, S., Al-Ahmad, H. (2023). Semantic segmentation of remote sensing imagery using an enhanced encoder- decoder architecture. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences 10, 1015–1020 DOI: https://doi.org/10.5194/isprs-annals-X-1-W1-2023-1015-2023
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2025 International Journal of Computational and Experimental Science and Engineering
This work is licensed under a Creative Commons Attribution 4.0 International License.
