Skip to main content

Advertisement

SpringerLink
Log in

Image classification based on the linear unmixing and GEOBIA

  • Published:
Environmental Monitoring and Assessment Aims and scope Submit manuscript

Abstract

Geographic Object-Based Image Analysis and linear unmixing are common methods in image classification. The purpose of this study is to analyze the classification efficiency by integrating these two methods in the mountain area. This research selected Jiangle County, Fujian, as a study area. Two Landsat8 OLI images, which covered the county, were used. Linear spectral mixture model, multi-scale segmentation, and decision tree were applied in the classification. After image preprocessing, linear spectral mixture model was used to unmix the image into three fraction images—vegetation, shade, and soil. The principal component analysis and tasseled cap transformation were used to derived three principal components and the brightness, wetness, and greenness. Multi-scale segmentation is applied by eCognition. Under scale 40, the image was divided into vegetation and non-vegetation area, then under scale 20, the vegetation area was divided into different types by integrating the fraction with different methods. The accuracy assessment of the classification map was done using the forestry resource survey and the high-resolution image of Google Earth. This study indicated that the unmixed bands could improve the classification accuracy. The overall classification accuracy was 92.40% with a Kappa coefficient of 0.9032. Therefore, there is a conclusion that this approach is an efficient way to classify different plantation.

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

Similar content being viewed by others

References

  • Adams, J. B., Smith, M. O., & Johnson, P. E. (1986). Spectral mixture modeling: a new analysis of rock and soil types at the Viking Lander 1 site. Journal of Geophysical Research, Solid Earth, 91(B8), 8098–8112.

    Article  Google Scholar 

  • Basaeed, E., Bhaskar, H., & Al-Mualla, M. (2016a). Supervised remote sensing image segmentation using boosted convolutional neural networks. Knowledge-Based Systems, 99, 19–27.

    Article  Google Scholar 

  • Basaeed, E., Bhaskar, H., Hill, P., Al-Mualla, M., & Bull, D. (2016b). A supervised hierarchical segmentation of remote-sensing images using a committee of multi-scale convolutional neural networks. International Journal of Remote Sensing, 37(7), 1671–1691.

    Article  Google Scholar 

  • Bing, H., & Zhao, Y. (2013). Wavelet-domain HMT model based on iterative context fusion for remote sensing image segmentation. Acta Geodaetica Et Cartographica Sinica, 42(2), 233–238.

    Google Scholar 

  • Blaschke, T., Hay, G. J., Kelly, M., Lang, S., Hofmann, P., Addink, E., et al. (2014). Geographic object-based image analysis - towards a new paradigm. Isprs Journal of Photogrammetry & Remote Sensing, 87(100), 180–191.

    Article  Google Scholar 

  • Chang, C. I., & Wu, C. C. (2015). Design and development of iterative pixel purity index. IEEE Journal of Selected Topics in Applied Earth Observations & Remote Sensing, 8(6), 1–20.

    Article  Google Scholar 

  • Chen, L., & Sun, Y. (2018). Comparison of object⁃oriented remote sensing image classification based on different decision trees in forest area. Chinese Journal of Applied Ecology, 29(12), 3995–4003.

    Google Scholar 

  • Chen, Y., Dai, J., & Li, J. (2008). CART-based decision tree classifier using multi-feature of image and its application. Geography and Geo-Information Science, 24(2), 33–36.

    CAS  Google Scholar 

  • Chen, L., Lin, H., Sun, H., Yan, E., & Wang, J. (2013a). Leaf area index and vegetation index Correlation of Cunninghamia based on the WorldView 2 images. Chinese Agricultural Science Bulletin, 29(01), 29–36.

    CAS  Google Scholar 

  • Chen, L., Zhang, X., & Jiao, Z. (2013b). Reversion of leaf area index in forest based on linear mixture model. Transactions of the Chinese Society of Agricultural Engineering, 29(13), 124–129.

    CAS  Google Scholar 

  • Chen, J., Chen, T., Mei, X., Shao, Q., & Deng, M. (2014). Hilly farmland extraction from high resolution remote sensing imagery based on optimal scale selection. Transactions of the Chinese Society of Agricultural Engineering, 30(5), 99–107.

    CAS  Google Scholar 

  • Chen, C., Knyazikhin, Y., Park, T., Yan, K., Lyapustin, A., Wang, Y., et al. (2017a). Prototyping of LAI and FPAR retrievals from MODIS multi-angle implementation of atmospheric correction (MAIAC) data.

    Book  Google Scholar 

  • Chen, Q., Li, L., Xu, Q., Yang, S., Shi, X., & Liu, X. (2017b). Multi-feature segmentation for high-resolution polarimetric SAR data based on fractal net evolution approach. Remote Sensing, 9(570), 1–26.

    Google Scholar 

  • Chen, L., Sun, Y., & Saeed, S. (2018). Monitoring and predicting land use and land cover changes using remote sensing and GIS techniques—a case study of a hilly area, Jiangle, China. PLoS One, 13(7), e0200493.

    Article  CAS  Google Scholar 

  • Clark, B. J. F., & Pellikka, P. K. E. (2009). Landscape analysis using multi-scale segmentation and object-oriented classification. In A. Röder, & J. Hill (Eds.), Recent Advances in Remote Sensing and Geoinformation Processing for Land Degradation Assessment. 323–341. CRC Press.

  • Felzenszwalb, P. F., & Huttenlocher, D. P. (2004). Efficient graph-based image segmentation. International Journal of Computer Vision, 59(2), 167–181.

    Article  Google Scholar 

  • Gu, H., Li, H., Yan, L., Liu, Z., Blaschke, T., & Soergel, U. (2017). An Object-Based Semantic Classification Method for High Resolution Remote Sensing Imagery Using Ontology. Remote Sensing, 9(4), 329–350.

    Article  Google Scholar 

  • Han, T., Xi, X., Wang, C., Bao, Y., & Luo, S. (2014). Study on Forest Classification in Yunnan based on decision tree algorithm. Remote Sensing Technology & Application, 29(5), 744–751.

    Google Scholar 

  • Hao, S., Chen, Y., Liu, H., Zhu, X., Zhaxi, D., & Li, W. (2017). Object-oriented forest classification of Linzhi County based on CART decision tree with texture information. Remote Sensing Technology & Application, 32(2), 386–194.

    Google Scholar 

  • He, M., Zhang, W., & Wang, W. (2009). Optimal segmentation scale model based on object-oriented analysis method. Journal of Geodesy & Geodynamics, 29(1), 106–109.

    Google Scholar 

  • Hoseini, F., & Shahbahrami, A. An efficient implementation of Fuzzy C-Means and watershed algorithms for MRI segmentation. In International Symposium on Telecommunications, 2017 (pp. 178-184). https://doi.org/10.1109/ISTEL.2016.7881806.

  • Hu, W., Zhao, P., & Dong, Z. (2010). An improved calculation model of object-oriented for the optimal segmentation-scale of remote sensing image. Geography and Geo-Information Science, 26(6), 15–18.

    Google Scholar 

  • Huang, Z., Zhang, J., Li, X., & Zhang, H. (2014). Remote sensing image segmentation based on dynamic statistical region merging. Optik - International Journal for Light and Electron Optics, 125(2), 870–875.

    Article  Google Scholar 

  • Ichoku, C., & Karnieli, A. (1996). A review of mixture modeling techniques for sub-pixel land cover estimation. Remote Sensing Reviews, 13(3), 161–186.

    Article  Google Scholar 

  • Johnson, B. A. (2013). High-resolution urban land-cover classification using a competitive multi-scale object-based approach. Remote Sensing Letters, 4(2), 131–140.

    Article  Google Scholar 

  • Johnson, B., & Xie, Z. (2011). Unsupervised image segmentation evaluation and refinement using a multi-scale approach. Isprs Journal of Photogrammetry & Remote Sensing, 66(4), 473–483.

    Article  Google Scholar 

  • Kang, J., Hou, X., Niu, Z., Gao, S., & Jia, K. (2014). Decision tree classification based on fitted phenology parameters from remotely sensed vegetation data. Transactions of the Chinese Society of Agricultural Engineering, 30(9), 148–156.

    Google Scholar 

  • Li, X., & Strahler, A. H. (1985). Geometric-optical modeling of a conifer forest canopy. Geoscience and Remote Sensing, IEEE Transactions on, 5, 705–721.

    Article  Google Scholar 

  • Li, X., & Strahler, A. H. (1986). Geometric-optical bidirectional reflectance modeling of a conifer forest canopy. Geoscience and Remote Sensing, IEEE Transactions on, 6, 906–919.

    Article  Google Scholar 

  • Li, L., Lu, D., & Kuang, W. (2016). Examining urban impervious surface distribution and its dynamic change in Hangzhou Metropolis. Remote Sensing, 8(3), 1–20. https://doi.org/10.3390/rs8030265.

    Article  Google Scholar 

  • Liu, L., Xu, M., Wang, S., Zhang, Q., Wang, N., Pan, H., et al. (2013). Effect of different Cunninghamia lanceolata plantation soil qualities on soil microbial community structure. Acta Ecologica Sinica, 33(15), 4692–4706.

    Article  CAS  Google Scholar 

  • Liu, Z., Xinhui, L. I., Shen, R., Zhu, F., Zhang, K., Wang, T., et al. (2014). Selection of the best segmentation scale in high-resolution image segmentation. Computer Engineering and Applications, 50(6), 144–147.

    Google Scholar 

  • Liu, J., Wang, L., Yang, L., Teng, F., Shao, J., Yang, F., et al. (2015). GF-1 satellite image atmospheric correction based on 6S model and its effect. Transactions of the Chinese Society of Agricultural Engineering, 31(19), 159–168.

  • López, S., Pablito, M., Corral, R., José, J., Díaz, V., Ramón, A., Álvarez, G., Juan, G., López, S., & Carlos, A. (2016). Evaluation of radiometric and atmospheric correction algorithms for aboveground forest biomass estimation using Landsat 5 TM data. Remote Sensing, 8(369), 1–19.

    Google Scholar 

  • Lu, D., Moran, E., & Batistella, M. (2003). Linear mixture model applied to Amazonian vegetation classification. Remote Sensing of Environment, 87(4), 456–469.

    Article  Google Scholar 

  • Lv, C. C., Wang, Z. W., & Qian, S. M. (2003). A review of pixel unmixing models. Remote Sensing Information, 3, 55–58.

    Google Scholar 

  • Mitra, P., Shankar, B. U., & Pal, S. K. (2004). Segmentation of multispectral remote sensing images using active support vector machines. Pattern Recognition Letters, 25(9), 1067–1074.

    Article  Google Scholar 

  • Pal, M., & Mather, P. M. (2003). An assessment of the effectiveness of decision tree methods for land cover classification. Remote Sensing of Environment, 86(4), 554–565.

    Article  Google Scholar 

  • Pipaud, I., & Lehmkuhl, F. (2017). Object-based delineation and classification of alluvial fans by application of mean-shift segmentation and support vector machines. Geomorphology, 293, 178–200.

    Article  Google Scholar 

  • Ren, J., Li, D. D., Wu, S. R., & Liu, J. (2015). Research on multispectral images sub-pixel mapping in agricultural region. Transactions of the Chinese Society for Agricultural Machinery, 46(10), 311–320.

    Google Scholar 

  • Saeed, S., Yujun, S., Beckline, M., & Liping, C. (2019). Forest edge effect on biomass carbon along altitudinal gradients in Chinese Fir (Cunninghamia lanceolata): A study from Southeastern China. Carbon Management, 10(1), 11–22.

    Article  CAS  Google Scholar 

  • Salehi, B., Zhang, Y., Zhong, M., & Dey, V. (2012). Object-based classification of urban areas using VHR imagery and height points ancillary data. Remote Sensing, 4(8), 2256–2276.

    Article  Google Scholar 

  • Shi, T., Zhang, L., & Yang, Y. (2015). Study on extraction of winter wheat area based on Landsat-8 and LSMM in case of northern Anhui. Journal of Triticeae Crops, 35(12), 1727–1732.

    Google Scholar 

  • Smith, M. O., Ustin, S. L., Adams, J. B., & Gillespie, A. R. (1990). Vegetation in deserts: I. A regional measure of abundance from multispectral images. Remote Sensing of Environment, 31(1), 1–26.

    Article  Google Scholar 

  • Su, W., Jiang, F., Zhu, D., Zhan, J., Ma, H., & Zhang, X. (2015). Extraction of Maize Planting Area Based on Decision Tree and Mixed-pixel Unmixing Methods. Transactions of the Chinese Society for Agricultural Machinery, 46(9), 289–295.

    Google Scholar 

  • Tian, H., Wang, L., Niu, Z., & Qin, Y. (2015a). Winter Wheat Planting Area Extraction Based on New Remote Sensing Data at County Level. Chinese Agricultural Science Bulletin, 31(5), 220–227.

    Google Scholar 

  • Tian, T., Fan, W., Lu, W. e., & Xiao, X. (2015b). An object-based information extraction technology for dominant tree species group types. Ying Yong Sheng Tai Xue Bao, 26(6), 1665–1672.

    Google Scholar 

  • Wang, H., Jin, H., Wang, J., & Jiang, W. (2015). Optimization approach for multi-scale segmentation of remotely sensed imagery under k-means clustering guidance. Acta Geodaetica Et Cartographica Sinica, 44(5), 526–532.

    Google Scholar 

  • Wang, L. X., Xu, S. N., Li, Q., Xue, H. X., & Wu, J. S. (2016a). Extraction of winter wheat planted area in Jiangsu province using decision tree and mixed-pixel methods. Transactions of the Chinese Society of Agricultural Engineering (Transactions of the CSAE), 32(5), 182–187.

    CAS  Google Scholar 

  • Wang, R., Wang, Z., Liu, X., & University, T. N. (2016b). ZY-3 linear features information extraction based on the multi-scales and multi-criterion. Science of Surveying & Mapping, 41(11), 146–150.

    Google Scholar 

  • Xi, Z., Liu, L., Lu, D., Ge, H., & Chen, Y. (2015). Mapping of Carya cathayensis spatial distribution with linear spectral mixture model. Scientia Silvae Sinica, 51(10), 43–52.

    Google Scholar 

  • Xi, Z. Y., Lu, D. S., Liu, L. J., & Ge, H. L. (2016). Detection of drought-induced hickory disturbances in western Lin An County , China, using multitemporal Landsat imagery. Remote Sensing, 8(345), 1–20.

    Google Scholar 

  • Xu, H. (2005). A study on information extraction of water body with the modified normalized difference water index (MNDWI). Journal of Remote Sensing, 9(5), 589–595.

    Google Scholar 

  • Xuan, C. Z., & Bo, W. W. (2008). A review on endmember extraction algorithms based on the linear mixing model. Science of Surveying & Mapping, 33(S), 49–51.

    Google Scholar 

  • Xue, Q., Kuang, G. Y., & Zhi-Yong, L. I. (2004). Endmember extraction algorithms from hyperspectral image based on the linear mixing model: an overview. Remote Sensing Technology & Application, 19(3), 197–201.

    Google Scholar 

  • Yang, H., & Ming, D. (2016). Optimal scales based segmentation of high spatial resolution remote sensing data. Journal of Geo-Information Science, 18(5), 632–638.

    Google Scholar 

  • Zhang, Z. J., Li, A. N., Lei, G. B., Bian, J. H., & Wu, B. F. (2014). Change detection of remote sensing images based on multiscale segmentation and decision tree algorithm over mountainous area: a case study in Panxi region, Sichuan Province. Acta Ecologica Sinica. https://doi.org/10.5846/stxb201310112439.

  • Zhang, N., Wu, Y., & Zhang, Q. (2015). Detection of sea ice in sediment laden water using MODIS in the Bohai Sea: a CART decision tree method. International Journal of Remote Sensing, 36(6), 1661–1674.

    Article  Google Scholar 

Download references

Funding

This study was supported by the project of the National Natural Science Foundation of China, “study on crown models for Larix olgensis based on tree growth” (No. 31870620).

Author information

Authors and Affiliations

  1. State Forestry Administration Key Laboratory of Forest Resources & Environmental Management, Beijing Forestry University, Beijing, 100083, China

    Chen Liping, Sajjad Saeed & Sun Yujun

Authors
  1. Chen Liping
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Sajjad Saeed
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Sun Yujun
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Sun Yujun.

Ethics declarations

Conflict of interest

This manuscript is original work of the authors. Neither the entire manuscript nor any part of its content has been published elsewhere.

Additional information

Publisher’s note

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

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Liping, C., Saeed, S. & Yujun, S. Image classification based on the linear unmixing and GEOBIA. Environ Monit Assess 191, 649 (2019). https://doi.org/10.1007/s10661-019-7837-x

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s10661-019-7837-x

Keywords

Search

Navigation