Object-based approaches for land use-land cover classification using high resolution quick bird satellite imagery (a case study: Kerbela, Iraq)

    Hussein Sabah Jaber Affiliation
    ; Muntadher Aidi Shareef Affiliation
    ; Zainab Fahkri Merzah Affiliation


Land Use / Land Cover (LULC) classification is considered one of the basic tasks that decision makers and map makers rely on to evaluate the infrastructure, using different types of satellite data, despite the large spectral difference or overlap in the spectra in the same land cover in addition to the problem of aberration and the degree of inclination of the images that may be negatively affect rating performance. The main objective of this study is to develop a working method for classifying the land cover using high-resolution satellite images using object based  method. Maximum likelihood pixel based supervised as well as  object approaches were examined on QuickBird satellite image in Karbala, Iraq. This study illustrated that use of textural data during the  object image classification approach can considerably enhance land use classification performance. Moreover, the results showed higher overall accuracy (86.02%) in the o object based method than pixel based (79.06%)  in urban extractions. The  object based performed much more capabilities than pixel based.

Keyword : object-based approaches, maximum likelihood, quick bird, satellite imagery, LULC, processing

How to Cite
Jaber, H. S., Shareef, M. A., & Merzah, Z. F. (2022). Object-based approaches for land use-land cover classification using high resolution quick bird satellite imagery (a case study: Kerbela, Iraq). Geodesy and Cartography, 48(2), 85–91.
Published in Issue
Jun 29, 2022
Abstract Views
PDF Downloads
Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.


Chen, Y., Su, W., Li, J., & Sun, Z. (2009). Hierarchical object oriented classification using very high resolution imagery and LIDAR data over urban areas. Advances in Space Research, 43(7), 1101–1110.

Cihlar, J. (2000). Land cover mapping of large areas from satellites: status and research priorities. International Journal of Remote Sensing, 21(6–7), 1093–1114.

Degerickx, J., Okujeni, A., Iordache, M.-D., Hermy, M., Van der Linden, S., & Somers, B. (2017). A novel spectral library pruning technique for spectral unmixing of urban land cover. Remote Sensing, 9(6), 565.

Ding, L. L., Hongyi Hu, C., Zhang, W., & Wang, S. (2018). Alexnet feature extraction and multi-kernel learning for object-oriented classification. International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, 42, 277–281.

Elatawneh, A., Kalaitzidis, C., Petropoulos, G. P., & Schneider, T. J. (2014). Evaluation of diverse classification approaches for land use/cover mapping in a Mediterranean region utilizing Hyperion data. International Journal of Digital Earth, 7(3), 194–216.

Gibril, M. B. A., Bakar, S. A., Yao, K., Idrees, M. O., & Pradhan, B. (2017). Fusion of RADARSAT-2 and multispectral optical remote sensing data for LULC extraction in a tropical agricultural area. Geocarto International, 32(7), 735–748.

Hasan, S. F., Shareef, M. A., & Hassan, N. D. (2021). Speckle filtering impact on land use/land cover classification area using the combination of Sentinel-1A and Sentinel-2B (a case study of Kirkuk city, Iraq). Arabian Journal of Geosciences, 14(4), 276.

Joshi, N., Baumann, M., Ehammer, A., Fensholt, R., Grogan, K., Hostert, P., Rudbeck Jepsen, M., Kuemmerle, T., Meyfroidt, P., Mitchard, E. T. A., Reiche, J., Ryan, C. M., & Waske, B. (2016). A review of the application of optical and radar remote sensing data fusion to land use mapping and monitoring. Remote Sensing, 8(1), 70.

Kazemi, S., Lim, S., & Rizos, C. (2009). Interactive and automated segmentation and generalisation of raster data. International Journal of Geoinformatics, 5(3).

Li, C., & Xiong, H. (2017). A geometric and radiometric simultaneous correction model (GRSCM) framework for high-accuracy remotely sensed image preprocessing. Photogrammetric Engineering & Remote Sensing, 83(9), 621–632.

Liu, D., & Xia, F. (2010). Assessing object-based classification: Advantages and limitations. Remote Sensing Letters, 1(4), 187–194.

Pu, R., & Bell, S. (2017). Mapping seagrass coverage and spatial patterns with high spatial resolution IKONOS imagery. International Journal of Applied Earth Observation and Geoinformation, 54, 145–158.

Shareef, M. A., Ameen, M. H., & Ajaj, Q. M. (2020). Change detection and GIS-based fuzzy AHP to evaluate the degradation and reclamation land of Tikrit City, Iraq. Geodesy and Cartography, 46(4), 194–203.

Shareef, M. A., & Hasan, S. F. (2020). Characterization and estimation of dates palm trees in an urban area using GIS-based least-squares model and minimum noise fraction images. Journal of Ecological Engineering, 21(6), 78–85.

Shareef, M. A., Hassan, N. D., Hasan, S. F., & Khenchaf, A. (2020). Integration of Sentinel-1A and Sentinel-2B data for land use and land cover mapping of the Kirkuk Governorate, Iraq. International Journal of Geoinformatics, 16(3).

Snehmani, Gore, A., Ganju, A., Kumar, S., Srivastava, P. K., & R P, H. R. (2017). A comparative analysis of pansharpening techniques on QuickBird and WorldView-3 images. Geocarto International, 32(11), 1268–1284.

Steinhausen, M. J., Wagner, P. D., Narasimhan, B., & Waske, B. (2018). Combining Sentinel-1 and Sentinel-2 data for improved land use and land cover mapping of monsoon regions. International Journal of Applied Earth Observation and Geoinformation, 73, 595–604.

Sturari, M., Frontoni, E., Pierdicca, R., Mancini, A., Malinverni, E. S., Tassetti, A. N., & Zingaretti, P. (2017). Integrating elevation data and multispectral high-resolution images for an improved hybrid Land Use/Land Cover mapping. European Journal of Remote Sensing, 50(1), 1–17.

Walter, V. (2004). Object-based classification of remote sensing data for change detection. ISPRS Journal of Photogrammetry and Remote Sensing, 58(3–4), 225–238.