Skip to main content

Advertisement

SpringerLink
Log in

Land-use classification with biologically inspired color descriptor and sparse coding spatial pyramid matching

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

Abstract

Land-use classification using remote sensing images plays a key role in many applications such as urban mapping and geospatial object detection. With the rapid development of satellite sensors, high-resolution images which exhibit more detailed textures now can be acquired. How to effectively represent these images and recognize the categories of land-use/land-cover scenes has become a challenging task. In this paper, we propose a novel biologically inspired descriptor combined with the sparse coding spatial pyramid matching (ScSPM) for land-use classification. A color processing pipeline is first presented to simulate the opponent responses of human visual system. By extending the scale invariant feature transform (SIFT) on processed color channels, a descriptor that is able to jointly extract color and shape information for land-use images is proposed. Then the ScSPM model is employed to incorporate the local descriptors of an image, followed by a linear kernel support vector machine (SVM) for image classification. Performance evaluation on the publicly available LULC data set demonstrates that the proposed method achieves better classification accuracy than other reference methods.

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

Similar content being viewed by others

References

  1. Alahi A, Ortiz R, Vandergheynst P (2012) FREAK: Fast retina keypoint. In: 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), IEEE, pp 510–517

  2. Avramović A, Risojević V (2016) Block-based semantic classification of high-resolution multispectral aerial images. SIViP 10(1):75–84

    Article  Google Scholar 

  3. Bay H, Tuytelaars T, Van Gool L (2006) SURF: Speeded up robust features. In: Computer Vision–ECCV 2006. Springer, pp 404–417

  4. Bosch A, Zisserman A, Muoz X (2008) Scene classification using a hybrid generative/discriminative approach. IEEE Trans Pattern Anal Mach Intell 30(4):712–727

    Article  Google Scholar 

  5. Burghouts GJ, Geusebroek JM (2009) Performance evaluation of local colour invariants. Comput Vis Image Underst 113(1):48–62

    Article  Google Scholar 

  6. Chang CC, Lin CJ (2011) LIBSVM: a library for support vector machines. ACM Trans Intell Syst Technol (TIST) 2(3):27

    Google Scholar 

  7. Chen S, Tian Y (2015) Pyramid of spatial relatons for scene-level land use classification. IEEE Trans Geosci Remote Sens 53(4):1947–1957

    Article  Google Scholar 

  8. Cheng G, Han J, Guo L, Liu Z, Bu S, Ren J (2015) Effective and efficient midlevel visual elements-oriented land-use classification using vhr remote sensing images. IEEE Trans Geosci Remote Sens 53(8):4238–4249

    Article  Google Scholar 

  9. Conway BR, Chatterjee S, Field GD, Horwitz GD, Johnson EN, Koida K, Mancuso K (2010) Advances in color science: from retina to behavior. J Neurosci 30(45):14,955–14,963

    Article  Google Scholar 

  10. Csurka G, Dance C, Fan L, Willamowski J, Bray C (2004) Visual categorization with bags of keypoints. In: Workshop on statistical learning in computer vision, ECCV, Prague, vol 1, pp 1–2

  11. Dalal N, Triggs B (2005) Histograms of oriented gradients for human detection. In: IEEE Computer society conference on computer vision and pattern recognition (CVPR 2005), vol 1, pp 886–893

  12. Fei-Fei L, Perona P (2005) A bayesian hierarchical model for learning natural scene categories. In: IEEE Computer society conference on computer vision and pattern recognition (CVPR 2005), vol 2, pp 524–531

  13. Fei-Fei L, Fergus R, Perona P (2006) One-shot learning of object categories. IEEE Trans Pattern Anal Mach Intell 28(4):594–611

    Article  Google Scholar 

  14. Fei-Fei L, Fergus R, Perona P (2007) Learning generative visual models from few training examples: an incremental bayesian approach tested on 101 object categories. Comput Vis Image Underst 106(1):59–70

    Article  Google Scholar 

  15. Gao S, Yang K, Li C, Li Y (2013) A color constancy model with double-opponency mechanisms. In: Proceedings of the IEEE International Conference on Computer Vision, pp 929–936

  16. Gao S, Yang K, Li C, Li Y (2015) Color constancy using double-opponency. IEEE Trans Pattern Anal Mach Intell 37(10):1973–1985

    Article  Google Scholar 

  17. Griffin G, Holub A, Perona P (2007) Caltech-256 object category dataset

  18. Hurlbert AC (1989) The computation of color. Tech. rep., DTIC Document

  19. Ke Y, Sukthankar R (2004) PCA-SIFT: A more distinctive representation for local image descriptors. In: Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2004), vol 2, pp II–506

  20. Lazebnik S, Schmid C, Ponce J (2006) Beyond bags of features: Spatial pyramid matching for recognizing natural scene categories. In: 2006 IEEE Computer society conference on computer vision and pattern recognition, vol 2, pp 2169–2178

  21. Lennie P, Krauskopf J, Sclar G (1990) Chromatic mechanisms in striate cortex of macaque. J Neurosci 10(2):649–669

    Google Scholar 

  22. Leutenegger S, Chli M, Siegwart RY (2011) BRISK: Binary robust invariant scalable keypoints. In: 2011 IEEE International Conference on Computer Vision (ICCV), pp 2548–2555

  23. Li Y, Tao C, Tan Y, Shang K, Tian J (2016) Unsupervised multilayer feature learning for satellite image scene classification. IEEE Geosci Remote Sens Lett 13(2):157–161

    Article  Google Scholar 

  24. Liu L, Wang L, Liu X (2011) In defense of soft-assignment coding. In: 2011 IEEE International Conference on Computer Vision (ICCV), pp 2486–2493

  25. Liu P, Choo KKR, Wang L, Huang F (2016) SVM or deep learning? a comparative study on remote sensing image classification. Soft Computing. doi:10.1007/s00500-016-2247-2

  26. Lowe DG (1999) Object recognition from local scale-invariant features. In: The proceedings of the seventh IEEE international conference on computer vision, vol 2, pp 1150–1157

  27. Ma J, Zhou H, Zhao J, Gao Y, Jiang J, Tian J (2015a) Robust feature matching for remote sensing image registration via locally linear transforming. IEEE Trans Geosci Remote Sens 53(12):6469–6481

  28. Ma J, Zhao J, Yuille AL (2016) Non-rigid point set registration by preserving global and local structures. IEEE Trans Image Process 25(1):53–64

    Article  MathSciNet  Google Scholar 

  29. Ma Y, Wu H, Wang L, Huang B, Ranjan R, Zomaya A, Jie W (2015b) Remote sensing big data computing: challenges and opportunities. Futur Gener Comput Syst 51:47–60

  30. Mekhalfi ML, Melgani F, Bazi Y, Alajlan N (2015) Land-use classification with compressive sensing multifeature fusion. IEEE Geosci Remote Sens Lett 12(10):2155–2159

  31. Mikolajczyk K, Schmid C (2005) A performance evaluation of local descriptors. IEEE Trans Pattern Anal Mach Intell 27(10):1615–1630

    Article  Google Scholar 

  32. Oliva A, Torralba A (2001) Modeling the shape of the scene: a holistic representation of the spatial envelope. Int J Comput Vis 42(3):145–175

    Article  MATH  Google Scholar 

  33. Savarese S, Winn J, Criminisi A (2006) Discriminative object class models of appearance and shape by correlatons. In: 2006 IEEE Computer society conference on computer vision and pattern recognition, vol 2, pp 2033–2040

  34. Shapley R, Hawken MJ (2011) Color in the cortex: single-and double-opponent cells. Vis Res 51(7):701–717

    Article  Google Scholar 

  35. Sivic J, Zisserman A (2003) Video google: A text retrieval approach to object matching in videos. In: Proceedings of Ninth IEEE International Conference on Computer Vision, pp 1470–1477

  36. Van De Sande KE, Gevers T, Snoek CG (2010) Evaluating color descriptors for object and scene recognition. IEEE Trans Pattern Anal Mach Intell 32(9):1582–1596

  37. Van De Weijer J, Schmid C (2006) Coloring local feature extraction. In: Computer Vision–ECCV 2006. Springer, pp 334–348

  38. Wang L, Song W, Liu P (2016a) Link the remote sensing big data to the image features via wavelet transformation. Clust Comput 19(2):793–810

  39. Wang L, Yan J, Ma Y, Zomaya A (2016b) Pipscloud: High performance cloud computing for remote sensing big data management and processing. Future Generation Computer Systems. doi:10.1016/j.future.2016.06.009

  40. Wang L, Zhang J, Liu P, Choo KKR, Huang F (2016c) Spectral–spatial multi-feature-based deep learning for hyperspectral remote sensing image classification. Soft Computing pp 1–9

  41. Yang J, Yu K, Gong Y, Huang T (2009) Linear spatial pyramid matching using sparse coding for image classification. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2009), pp 1794–1801

  42. Yang K, Gao S, Li C, Li Y (2013) Efficient color boundary detection with color-opponent mechanisms. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp 2810– 2817

  43. Yang K, Gao S, Guo C, Li C, Li Y (2015) Boundary detection using double-opponency and spatial sparseness constraint. IEEE Trans Image Process 24(8):2565–2578

  44. Yang Y, Newsam S (2010) Bag-of-visual-words and spatial extensions for land-use classification. In: Proceedings of the 18th SIGSPATIAL International Conference on Advances in Geographic Information Systems, pp 270–279

  45. Yang Y, Newsam S (2011) Spatial pyramid co-occurrence for image classification. In: 2011 IEEE International Conference on Computer Vision (ICCV), pp 1465–1472

  46. Yu K, Zhang T, Gong Y (2009) Nonlinear learning using local coordinate coding. In: Advances in neural information processing systems, pp 2223–2231

  47. Zhang F, Du B, Zhang L (2015) Saliency-guided unsupervised feature learning for scene classification. IEEE Trans Geosci Remote Sens 53(4):2175–2184

    Article  Google Scholar 

  48. Zhang J, Barhomi Y, Serre T (2012) A new biologically inspired color image descriptor. In: Computer vision–ECCV 2012. Springer, pp 312–324

  49. Zhong Y, Zhu Q, Zhang L (2015) Scene classification based on the multifeature fusion probabilistic topic model for high spatial resolution remote sensing imagery. IEEE Trans Geosci Remote Sens 53(11):6207–6222

    Article  Google Scholar 

Download references

Acknowledgments

This work is supported by the Natural Foundation of Hubei Province under Grant 2016CFB278, China Postdoctoral Science Foundation under Grant 2016M602390, Open Research Project of the Hubei Key Laboratory of Intelligent Geo-Information Processing under Grant KLIGIP1608, and the Fundamental Research Funds for the Central Universities, China University of Geosciences (Wuhan).

Author information

Authors and Affiliations

  1. Hubei Key Laboratory of Intelligent Geo-Information Processing, College of Computer Science, China University of Geosciences, No.388 Lumo Road, Wuhan, 430074, China

    Tian Tian & Hengjian Tong

  2. Beijing Electro-Mechanical Engineering Institute, Beijing, 100074, China

    Yun Zhang

  3. School of Automation, Huazhong University of Science and Technology, Wuhan, 430074, China

    Hao Dou

Authors
  1. Tian Tian
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yun Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Hao Dou
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Hengjian Tong
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Hengjian Tong.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Tian, T., Zhang, Y., Dou, H. et al. Land-use classification with biologically inspired color descriptor and sparse coding spatial pyramid matching. Multimed Tools Appl 76, 22943–22958 (2017). https://doi.org/10.1007/s11042-016-4167-7

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-016-4167-7

Keywords

Search

Navigation