Skip to main content
SpringerLink
Log in

GFSCompNet: remote sensing image compression network based on global feature-assisted segmentation

  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

Abstract

The proliferation of remote sensing image data in recent years has posed a pressing need for efficient compression techniques due to constrained transmission bandwidth. While lossless compression preserves image fidelity, it falls short of meeting real-time demands. Conversely, conventional lossy compression methods can attain high compression ratios for real-time applications, but often introduce issues like block artifacts, blurring, and distortions in the decompressed images. Hence, we propose the Global Feature-Assisted Segmentation Compression Network (GFSCompNet) as a solution for high compression ratio lossy compression. Initially, we design a segmentation network utilizing a dual-branch global feature-assisted segmentation approach to precisely detect small targets in remote sensing images. On the compression side of the network, we leverage an attention mechanism and code rate allocation technique to seamlessly merge the segmented small target information with the original image, thereby allocating a higher compression code rate to the small target region. Furthermore, a joint hyper-priority decoding and entropy coding estimation network is proposed to further remove the redundancy in the potential representation and improve the compression ratio. Experimental results conducted under conditions of high compression ratios and comparable bit rates demonstrate that our approach yields higher-quality reconstructed images compared to the JPEG algorithm and outperforms other deep learning-based image compression methods. Additionally, it effectively preserves small target information, thereby enhancing the interpretability of machine learning models.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

Similar content being viewed by others

Data Availability

This study uses the publicly available iSAID dataset which can be accessed at https://captain-whu.github.io/iSAID/dataset.html.

References

  1. Linde Y, Buzo A, Gray R (1980) An algorithm for vector quantizer design. IEEE Trans Commun 28(1):84–95. https://doi.org/10.1109/TCOM.1980.1094577

    Article  Google Scholar 

  2. Ahalt SC, Krishnamurthy AK, Chen P et al (1990) Competitive learning algorithms for vector quantization. Neural Netw 3(3):277–290. https://doi.org/10.1016/0893-6080(90)90071-R

    Article  Google Scholar 

  3. Amrani N, Serra-Sagristá J, Laparra V et al (2016) Regression wavelet analysis for lossless coding of remote-sensing data. IEEE Trans Geosci Remote Sens 54(9):5616–5627. https://doi.org/10.1109/TGRS.2016.2569485

    Article  Google Scholar 

  4. Cheng X, Li Z (2021) Predicting the lossless compression ratio of remote sensing images with configurational entropy. IEEE J Sel Top Appl Earth Obs Remote Sens 14:11936–11953. https://doi.org/10.1109/JSTARS.2021.3123650

    Article  Google Scholar 

  5. Selwin Mich Priyadharson A, Thilipkumar C, Reddy LMK (2023) Sentinel-2 satellite image enhancement and compression based on dwt and vector quantization. Artificial intelligence and machine learning in satellite data processing and services. Springer Nature Singapore, Singapore, pp 1–7

    Google Scholar 

  6. Zhou S, Deng C, Zhao B, et al (2015) Remote sensing image compression: A review. In: 2015 IEEE International conference on multimedia big data, pp 406–410, https://doi.org/10.1109/BigMM.2015.16

  7. Xiang S, Liang Q (2023) Remote sensing image compression with long-range convolution and improved non-local attention model. Signal Process 209:109005

    Article  Google Scholar 

  8. Fu C, Du B, Zhang L (2023) Sar image compression based on multi-resblock and global context. EEE Geosci Remote Sens Lett 20:1–5

    Google Scholar 

  9. Gao J, Teng Q, He X, et al (2023) Mixed entropy model enhanced residual attention network for remote sensing image compression. Neural Processing Letters pp 1–13

  10. Fu H, Liang F (2023) Learned image compression with generalized octave convolution and cross-resolution parameter estimation. Signal Process 202:108778

    Article  Google Scholar 

  11. Wallace GK (1992) The jpeg still picture compression standard. IEEE Trans Consum Electron 38(1):xviii–xxxiv

  12. Ballé J, Minnen D, Singh S, et al (2018) Variational image compression with a scale hyperprior. In: International conference on learning representations

  13. Minnen D, Ballé J, Toderici GD (2018) Joint autoregressive and hierarchical priors for learned image compression. Advances in Neural Information Processing Systems 31

  14. Cheng Z, Sun H, Takeuchi M, et al (2020) Learned image compression with discretized gaussian mixture likelihoods and attention modules. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp 7939–7948

  15. Aggarwal K, Mijwil MM, Sonia, et al. (2022) Has the future started? the current growth of artificial intelligence, machine learning, and deep learning. Iraqi J Comput Sci Math 3(1):115–123. https://doi.org/10.52866/ijcsm.2022.01.01.013

  16. Lijun Y, Mengbo L, Tongxin W et al (2023) Geo-information mapping improves canny edge detection method. IET Image Process 17(6):1893–1904

    Article  Google Scholar 

  17. Majhi A, Sethy KM, Panda M (2022) Machine learning approach for change detection of chandaka wildlife sanctuary with the help of remote sensing data. Intell Syst Proc ICMIB 2021:523–535

    Google Scholar 

  18. Zhang W, Fu C, Chang X et al (2022) A more compact object detector head network with feature enhancement and relational reasoning. Neurocomputing 499:23–34

    Article  Google Scholar 

  19. Zhang W, Fu C, Xie H et al (2021) Global context aware rcnn for object detection. Neural Comput Appl 33:11627–11639

    Article  Google Scholar 

  20. Zhang W, Fu C, Cao L et al (2022) Codh++: Macro-semantic differences oriented instance segmentation network. Expert Syst Appl 202:117198

    Article  Google Scholar 

  21. Zhang W, Fu C, Zheng Y et al (2022) Hsnet: A hybrid semantic network for polyp segmentation. Comput Biol Med 150:106173

    Article  Google Scholar 

  22. Minaee S, Boykov Y, Porikli F et al (2022) Image segmentation using deep learning: A survey. IEEE Trans Pattern Anal Mach Intell 44(7):3523–3542. https://doi.org/10.1109/TPAMI.2021.3059968

    Article  Google Scholar 

  23. Gao H, Xiao J, Yin Y, et al (2022) A mutually supervised graph attention network for few-shot segmentation: The perspective of fully utilizing limited samples. IEEE Transactions on Neural Networks and Learning Systems pp 1–13. https://doi.org/10.1109/TNNLS.2022.3155486

  24. Hamaguchi R, Fujita A, Nemoto K, et al (2018) Effective use of dilated convolutions for segmenting small object instances in remote sensing imagery. In: 2018 IEEE winter conference on applications of computer vision (WACV), IEEE, pp 1442–1450

  25. Kampffmeyer M, Salberg AB, Jenssen R (2016) Semantic segmentation of small objects and modeling of uncertainty in urban remote sensing images using deep convolutional neural networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition workshops, pp 1–9

  26. Wang H, Zhou L, Wang L (2019) Miss detection vs. false alarm: Adversarial learning for small object segmentation in infrared images. In: Proceedings of the IEEE/CVF international conference on computer vision, pp 8509–8518

  27. Guo D, Zhu L, Lu Y et al (2018) Small object sensitive segmentation of urban street scene with spatial adjacency between object classes. IEEE Trans Image Process 28(6):2643–2653

    Article  MathSciNet  Google Scholar 

  28. Dong R, Pan X, Li F (2019) Denseu-net-based semantic segmentation of small objects in urban remote sensing images. IEEE Access 7:65347–65356

    Article  Google Scholar 

  29. Zheng Z, Zhong Y, Wang J, et al (2020) Foreground-aware relation network for geospatial object segmentation in high spatial resolution remote sensing imagery. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp 4096–4105

  30. Cheng S, Chen H, Yao P et al (2023) Mlae: A pretraining method for automatic identification of urban public space. IEEE Trans Geosci Remote Sens Lett 20:1–5. https://doi.org/10.1109/LGRS.2023.3315687

    Article  Google Scholar 

  31. Rahebi J (2022) Vector quantization using whale optimization algorithm for digital image compression. Multimed Tools Appl 81(14):20077–20103. https://doi.org/10.1007/s11042-022-11952-x

    Article  Google Scholar 

  32. Yuen M, Wu H (1998) A survey of hybrid mc/dpcm/dct video coding distortions. Signal Processing 70(3):247–278. https://doi.org/10.1016/S0165-1684(98)00128-5

    Article  Google Scholar 

  33. Bajpai S (2023) Low complexity image coding technique for hyperspectral image sensors. Multimedia Tools and Appl 82(20):31233–31258. https://doi.org/10.1007/s11042-023-14738-x

    Article  Google Scholar 

  34. Toderici G, O’Malley SM, Hwang SJ, et al (2015) Variable rate image compression with recurrent neural networks. arXiv preprint arXiv:1511.06085

  35. Toderici G, Vincent D, Johnston N, et al (2017) Full resolution image compression with recurrent neural networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 5306–5314

  36. Agustsson E, Tschannen M, Mentzer F, et al (2019) Generative adversarial networks for extreme learned image compression. In: Proceedings of the IEEE/CVF international conference on computer vision, pp 221–231

  37. Zhao S, Yang S, Gu J et al (2021) Symmetrical lattice generative adversarial network for remote sensing images compression. ISPRS J Photogramm Remote Sens 176:169–181

    Article  Google Scholar 

  38. Ballé J, Laparra V, Simoncelli EP (2017) End-to-end optimized image compression. In: Int’l conf on learning representations (ICLR), Toulon, France

  39. Ballé J, Laparra V, Simoncelli EP (2015) Density modeling of images using a generalized normalization transformation. arXiv preprint arXiv:1511.06281

  40. Jamil S, Piran MJ, Rahman M et al (2023) Learning-driven lossy image compression: A comprehensive survey. Eng Appl Artif Intell 123:106361

    Article  Google Scholar 

  41. Li M, Zuo W, Gu S, et al (2018) Learning convolutional networks for content-weighted image compression. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 3214–3223

  42. Xia Q, Liu H, Ma Z (2020) Object-based image coding: A learning-driven revisit. In: 2020 IEEE international conference on multimedia and expo (ICME), IEEE, pp 1–6

  43. Ding L, Lin D, Lin S et al (2022) Looking outside the window: Wide-context transformer for the semantic segmentation of high-resolution remote sensing images. IEEE Trans Geosci Remote Sens 60:1–13. https://doi.org/10.1109/TGRS.2022.3168697

    Article  Google Scholar 

  44. Hu J, Shen L, Sun G (2018) Squeeze-and-excitation networks. In: 2018 IEEE/CVF conference on computer vision and pattern recognition, pp 7132–7141, https://doi.org/10.1109/CVPR.2018.00745

  45. He X, Zhou Y, Zhao J et al (2022) Swin transformer embedding unet for remote sensing image semantic segmentation. IEEE Trans Geosci Remote Sens 60:1–15

    Article  Google Scholar 

  46. Zan Z, Liu C, Sun H et al (2021) Learned image compression with separate hyperprior decoders. IEEE Open J Circuits Syst 2:627–632. https://doi.org/10.1109/OJCAS.2021.3125354

    Article  Google Scholar 

  47. Chen T, Liu H, Ma Z et al (2021) End-to-end learnt image compression via non-local attention optimization and improved context modeling. IEEE Transactions on Image Processing 30:3179–3191. https://doi.org/10.1109/TIP.2021.3058615

    Article  Google Scholar 

  48. Waqas Zamir S, Arora A, Gupta A, et al (2019) isaid: A large-scale dataset for instance segmentation in aerial images. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition workshops, pp 28–37

  49. Xia GS, Bai X, Ding J, et al (2018) Dota: A large-scale dataset for object detection in aerial images. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 3974–3983

  50. Chen LC, Papandreou G, Schroff F, et al (2017) Rethinking atrous convolution for semantic image segmentation. arXiv:1706.05587

  51. Chen LC, Zhu Y, Papandreou G, et al (2018) Encoder-decoder with atrous separable convolution for semantic image segmentation. In: Proceedings of the European conference on computer vision (ECCV), pp 801–818

  52. Zhao H, Shi J, Qi X, et al (2017) Pyramid scene parsing network. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 2881–2890

  53. Fu J, Liu J, Tian H, et al (2019) Dual attention network for scene segmentation. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp 3146–3154

  54. Sun K, Xiao B, Liu D, et al (2019) Deep high-resolution representation learning for human pose estimation. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp 5693–5703

Download references

Acknowledgements

This work was supported by the Fundamental Research Funds for the Central Universities of China (No. N2216010), the ‘Jie Bang Gua Shuai’ Science and Technology Major Project of Liaoning Province in 2022 (No.2022JH1/10400025) and the National Key Research and Development Program of China (No. 2018YFB1702000).

Author information

Authors and Affiliations

  1. School of Computer Science and Engineering, Northeastern University, Wenhua Road, Shenyang, 110819, Liaoning, China

    Wenhui Ye, Weimin Lei, Wei Zhang, Tingting Yu & Xiang Feng

Authors
  1. Wenhui Ye
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Weimin Lei
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Wei Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Tingting Yu
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Xiang Feng
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Weimin Lei.

Ethics declarations

Competing of interest

The authors have no competing interests to declare that are relevant to the content of this article.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Ye, W., Lei, W., Zhang, W. et al. GFSCompNet: remote sensing image compression network based on global feature-assisted segmentation. Multimed Tools Appl (2024). https://doi.org/10.1007/s11042-024-18260-6

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s11042-024-18260-6

Keywords

Search

Navigation