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Geospatial Object Detection for Scene Understanding Using Remote Sensing Images

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Second International Conference on Image Processing and Capsule Networks (ICIPCN 2021)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 300))

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Abstract

With the rapid advancement in remote sensing (RS) technology, a massive amount of high spatial resolution RS images are available. Extracting geo-objects from HSR remote sensing images is the first and foremost step in RS image analysis. There is a need to design effective techniques which are capable of identifying contextual refinements in RS images, to relate observations to both the scene and the real world. This paper presents a review of different approaches for scene classification and object detection from high spatial resolution remote sensing images. This includes Machine Learning based techniques, Deep Learning based techniques, Object-Based Image Analysis (OBIA) and You Only Look Once (YOLO).

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References

  1. Gu, Y., Wang, Y., Li, Y.: A survey on deep learning-driven remote sensing image scene understanding: scene classification, scene retrieval and scene-guided object detection. Appl. Sci. 9, 2110 (2019)

    Article  Google Scholar 

  2. Cheng, G., Han, J., Lu, X.: Remote sensing image scene classification: benchmark and state of the art. Proc. IEEE 105, 1865–1883 (2017)

    Article  Google Scholar 

  3. Cheng, J., Li, L., Luo, B., Wang, S., Liu, H.: High-resolution remote sensing image segmentation based on improved RIU-LBP and SRM. Eurasip J. Wirel. Commun. Netw. 2013, 1–12 (2013)

    Article  Google Scholar 

  4. Merkle, N., Auer, S., Muller, R., Reinartz, P.: Exploring the potential of conditional adversarial networks for optical and SAR image matching. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 11, 1811–1820 (2018)

    Google Scholar 

  5. Zhou, Q., Zheng, B., Zhu, W., Jan Latecki, L.: Multi-scale context for scene labeling via flexible segmentation graph. Pattern Recognit. (2016). https://doi.org/10.1016/j.patcog.2016.03.023

  6. Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2005), pp. 29–45 (2005). https://doi.org/10.1109/CVPR.2005.177

  7. Zafar, B., et al.: A novel discriminating and relative global spatial image representation with applications in CBIR. Appl. Sci. 8, 1–23 (2018)

    Article  Google Scholar 

  8. Cheng, G., Han, J.: A survey on object detection in optical remote sensing images. ISPRS J. Photogramm. Remote Sens. 117, 11–28 (2016)

    Article  Google Scholar 

  9. Zhang, L., Zhang, Y.: Airport detection and aircraft recognition based on two-layer saliency model in high spatial resolution remote-sensing images. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 10, 1511-1524 (2017)

    Google Scholar 

  10. Hamedianfar, A., Barakat, A., Gibril, M.: Large-scale urban mapping using integrated geographic object-based image analysis and artificial bee colony optimization from worldview-3 data. Int. J. Remote Sens. 40, 6796–6821 (2019)

    Article  Google Scholar 

  11. Tu, J., Li, D., Feng, W., Han, Q., Sui, H.: Detecting damaged building regions based on semantic scene change from multi-temporal high-resolution remote sensing images. ISPRS Int. J. Geo-Information (2017). https://doi.org/10.3390/ijgi6050131

  12. Cheng, G., Ma, C., Zhou, P., Yao, X., Han, J.: Scene classification of high resolution remote sensing images using convolutional neural networks. In: 2016 IEEE International Geoscience and Remote Sensing Symposium, pp. 767–770 (2016)

    Google Scholar 

  13. Shafaey, M.A., Salem, M.A.-M., Ebied, H.M., Al-Berry, M.N., Tolba, M.F.: Deep learning for satellite image classification. In: Hassanien, A.E., Tolba, M.F., Shaalan, K., Azar, A.T. (eds.) AISI 2018. AISC, vol. 845, pp. 383–391. Springer, Cham (2019). https://doi.org/10.1007/978-3-319-99010-1_35

    Chapter  Google Scholar 

  14. Li, W., Fu, H., Yu, L., Cracknell, A.: Deep learning based oil palm tree detection and counting for high-resolution remote sensing images. Remote Sens. 9, 22 (2017)

    Article  Google Scholar 

  15. Kampffmeyer, M., Salberg, A.B., Jenssen, R.: Semantic segmentation of small objects and modeling of uncertainty in urban remote sensing images using deep convolutional neural networks. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops (2016). https://doi.org/10.1109/CVPRW.2016.90

  16. Najibi, M., Rastegari, M., Davis, L.S.: G-CNN: an iterative grid based object detector. In: Proceedings of the IEEE Computer Society Conference on Computer Vision Pattern Recognition, 2016 December, pp. 2369–2377 (2016)

    Google Scholar 

  17. Liang, Y., Monteiro, S.T., Saber, E.S.: Transfer learning for high resolution aerial image classification. In: Proceedings of the Applied Image Pattern Recognition Workshop (2017). https://doi.org/10.1109/AIPR.2016.8010600

  18. Xie, M., Jean, N., Burke, M., Lobell, D., Ermon, S.: Transfer learning from deep features for remote sensing and poverty mapping. In: 30th AAAI Conference on Artificial Intelligence AAAI 2016, pp. 3929–3935 (2016)

    Google Scholar 

  19. Nogueira, K., Dalla Mura, M., Chanussot, J., Schwartz, W.R., Santos Dos, J.A.: Learning to semantically segment high-resolution remote sensing images. In: Proceedings of the International Conference on Pattern Recognition (2016). https://doi.org/10.1109/ICPR.2016.7900187

  20. Shi, Z., Zou, Z.: Can a machine generate humanlike language descriptions for a remote sensing image? IEEE Trans. Geosci. Remote Sens. 55, 3623–3634 (2017)

    Article  Google Scholar 

  21. Muruganandham, S.: Semantic segmentation of satellite images using deep learning. Czech Technical University Prague Lulea University Technology, pp. 1–94 (2016). https://doi.org/10.1007/s00211-012-0485-5

  22. Farahani, M., Mohammadzadeh, A.: Domain adaptation for unsupervised change detection of multisensor multitemporal remote-sensing images. Int. J. Remote Sens. 41(10), 3902–3923 (2020)

    Article  Google Scholar 

  23. Wang, M., Deng, W.: Deep visual domain adaptation: a survey. Neurocomputing 312, 135–153 (2018)

    Article  Google Scholar 

  24. Tuia, D., Persello, C., Bruzzone, L.: Domain adaptation for the classification of remote sensing data: an overview of recent advances. IEEE Geosci. Remote Sens. Mag. 4, 41–57 (2016)

    Article  Google Scholar 

  25. Cheng, G., Yang, C., Yao, X., Guo, L., Han, J.: When deep learning meets metric learning: remote sensing image scene classification via learning discriminative CNNs. IEEE Trans. Geosci. Remote Sens. 56, 2811–2821 (2018)

    Article  Google Scholar 

  26. Gong, Z., Zhong, P., Yu, Y., Hu, W.: Diversity-promoting deep structural metric learning for remote sensing scene classification. IEEE Trans. Geosci. Remote Sens. 56, 371–390 (2018)

    Article  Google Scholar 

  27. Blaschke, T.: Object based image analysis for remote sensing. ISPRS J. Photogramm. Remote Sens. 65, 2–16 (2010)

    Article  Google Scholar 

  28. Redmon, J., Divvala, S., Girshick, R., Farhadi, A.: You only look once: unified, real-time object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 779–788 (2016)

    Google Scholar 

  29. Blaschke, T., et al.: Geographic object-based image analysis – towards a new paradigm. ISPRS J. Photogramm. Remote Sens. 87, 180–191 (2014)

    Article  Google Scholar 

  30. Chen, G., Weng, Q., Hay, G.J., He, Y.: Geographic Object-Based Image Analysis (GEOBIA): emerging trends and future opportunities. GISci. Remote Sens. 55, 159–182 (2018)

    Article  Google Scholar 

  31. Benedetti, P., et al.: M 3 fusion: a deep learning architecture for multiscale multimodal multitemporal satellite data fusion. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 11, 4939–4949 (2018)

    Google Scholar 

  32. Hossain, M.D., Chen, D.: Segmentation for Object-Based Image Analysis (OBIA): a review of algorithms and challenges from remote sensing perspective. ISPRS J. Photogramm. Remote. Sens. 150, 115–134 (2019)

    Article  Google Scholar 

  33. Yadav, S., Rizvi, I., Kadam, S.: Urban tree canopy detection using object-based image analysis for very high resolution satellite images: a literature review. In Proceedings of the International Conference on Technologies for Sustainable Development, ICTSD 2015 (2015). https://doi.org/10.1109/ICTSD.2015.7095889

  34. Lang, S., Hay, G.J., Baraldi, A., Tiede, D., Blaschke, T.: GEOBIA achievements and spatial opportunities in the era of big earth observation data. ISPRS Int. J. Geo-Inf. 8, 474 (2019)

    Article  Google Scholar 

  35. Gao, Y., Mas, J.: A comparison of the performance of pixel based and object based classifications over images with various spatial resolutions. Online J. Earth Sci. 2, 27–35 (2008)

    Google Scholar 

  36. Kaplan, G., Avdan, U.: Object-based water body extraction model using sentinel-2 satellite imagery. Eur. J. Remote Sens. 50, 137–143 (2017)

    Article  Google Scholar 

  37. Toure, S.I., Stow, D.A., Shih, H.C., Weeks, J., Lopez-Carr, D.: Land cover and land use change analysis using multi-spatial resolution data and object-based image analysis. Remote Sens. Environ. 210, 259–268 (2018)

    Article  Google Scholar 

  38. Lang, S., Baraldi, A., Tiede, D., Hay, G., Blaschke, T.: Towards a GEOBIA 2.0 manifesto ? Achievements and open challenges in information & knowledge extraction from big earth data. In: GEOBIA 2018 - From Pixels to Ecosystem Global Sustainability, pp. 18–22 (2018)

    Google Scholar 

  39. Khiali, L., Ienco, D., Teisseire, M.: Object-oriented satellite image time series analysis using a graph-based representation. Ecol. Inform. 43, 52–64 (2018)

    Article  Google Scholar 

  40. Manoharan, D.S., Sathish.: Population based meta heuristics algorithm for performance improvement of feed forward neural network. J. Soft Comput. Paradig. 2, 36–46 (2020)

    Google Scholar 

  41. Syms, S., Chen, Z.J.I., Shakya, S.: Survey on neural network architectures with deep learning. J. Soft Comput. Paradig. 2, 186–194 (2020)

    Article  Google Scholar 

  42. USGS EarthExplorer, 21 December 2019. https://earthexplorer.usgs.gov/

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Authors and Affiliations

  1. K. J. Somaiya College of Engineering, Mumbai, India

    Stuti Naresh Ahuja & Sonali Atulkumar Patil

  2. SIES Graduate School of Technology, Navi Mumbai, India

    Stuti Naresh Ahuja

Authors
  1. Stuti Naresh Ahuja
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  2. Sonali Atulkumar Patil
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Corresponding author

Correspondence to Stuti Naresh Ahuja .

Editor information

Editors and Affiliations

  1. Department of Electrical Engineering, Da-Yeh University, Changhua, Taiwan

    Joy Iong-Zong Chen

  2. Departamento de Engenharia Mecanica, Universidade do Porto, Porto, Portugal

    João Manuel R. S. Tavares

  3. College of Engineering, Prince Sattan Bin Abdulaziz University, Al-Kharj, Saudi Arabia

    Abdullah M. Iliyasu

  4. Department of Electrical and Computer Engineering, Concordia University, Montreal, QC, Canada

    Ke-Lin Du

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Ahuja, S.N., Patil, S.A. (2022). Geospatial Object Detection for Scene Understanding Using Remote Sensing Images. In: Chen, J.IZ., Tavares, J.M.R.S., Iliyasu, A.M., Du, KL. (eds) Second International Conference on Image Processing and Capsule Networks. ICIPCN 2021. Lecture Notes in Networks and Systems, vol 300. Springer, Cham. https://doi.org/10.1007/978-3-030-84760-9_11

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