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Year 2021
 
Vol 62, Issue 1, [02 - 2021]

Object-oriented classification for land cover of North Thang Long Industrial area using Worldview-2 data

DOI:10.46326/JMES.2021.62(1).02

  • Affiliations:

    1 Hanoi University of Mining and Geology, Hanoi, Vietnam
    2 Loc Ninh land registration office, Binh Phuoc, Vietnam

  • *Corresponding:
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  • Received: 25th-Sept-2020
  • Revised: 25th-Dec-2020
  • Accepted: 26th-Jan-2021
  • Online: 28th-Feb-2021
Pages: 10 - 18
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Cited by: 2

Abstract:

Land cover/land use classification using high spatial resolution remote sensing data has the biggest challenge is how to distinguish object classes from different spectral values based on structures, shapes, and spatial elements. This paper focuses on the object-oriented classification method to extract artificial surface at industrial area by Worldview-2 data with a spatial resolution of 1.8 m. Extraction of 05 types of land cover/land use using object-oriented classification method based on reflectance spectral characteristics, shape index, location of objects, brightness, NDVI index, and density objects are archive efficiency to the quality of classification results. The overall accuracy of classification result for land cover/land use of Thang Long industrial area is about 0.85 and Kappa index is about 0.81.

How to Cite
Le, H.Thu Thi, Hoang, L.Van and Nguyen, T.Van 2021. Object-oriented classification for land cover of North Thang Long Industrial area using Worldview-2 data (in Vietnamese). Journal of Mining and Earth Sciences. 62, 1 (Feb, 2021), 10-18. DOI:https://doi.org/10.46326/JMES.2021.62(1).02.
References

Aatz, M., Benz, U., Dehghani, S., Heynen, M., Höltje, A., Hofmann, P., Lingenfelder, I., Mimler, M., Sohlbach, M., Weber, M., and Willhauck, G., (2004), eCognition Professional: User guide 4.; Munich: Definiens-Imaging. 

Belward, A.S., Skøien, J.O., (2015). Who launched what, when and why; trends in global land-cover observation capacity from civilian earth observation satellites, ISPRS J. Photogram. Remote Sens., 103, pp. 115-128, 

Choodarathnakara, A.L., Ashok, K.T., Shivaprakash, K Dr., and Patil Dr.C.G., (2012). Soft Classification Techniques for RS Data, IJCSET, 2 (11), pp.1468 - 1471.

Congalton, R. G. and Green, K., (2008). Assessing the accuracy of remotely sensed data: Principies and practices. New York. Taylorand Francis Group.

Desheng Liu and Fanxia, (2010). Assessing object-based classification: advantages and limitations. Remote Sensing Letters, ISSN: 2150-704X (Print) 2150-7058 (Online).

Digitalglobal, (2009). WorldView-2 Satellite Sensor. 

Guo, Q., Kelly, M., Gong, P. and Liu, D., (2007). An object-based classification approach in mapping tree mortality using high spatial resolution imagery. GIScience and Remote Sensing, 44, pp. 24-47.

Kamal, M.; Phinn, S.; Johansen, K., (2015). Object-Based Approach for Multi-Scale Mangrove Composition Mapping Using Multi-Resolution Image Datasets. Remote Sens 7, 4753-4783.

Kavzoglu, T.; Yildiz, M., (2014). Parameter-Based Performance Analysis of Object-Based Image Analysis Using Aerial and Quikbird-2 Images. ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume II-7, pp.31-37.

Manakos, I., (2001). eCognition and Precision Farming. http://www.lrz-muenchen.de/~lnn /. eCognition Application Notes, Vol. 2, No 2, April 2001.

Mario, C., (2009). ESA Advanced Training Course on Land Remote Sensing: Image Classification, ESA

Mitri, G.H., and Gitas, I.Z.,(2002). The development of an object-oriented classification model for operational burned area mapping on the Mediterranean island of Thasos using LANDSAT TM images. Forest Fire Research and Wildland Fire Safety, Viegas (ed.) Millpress, Rotterdam, ISBN 90-77017-72-0.

Qihao Weng, (2020). Techniques and Methods in Urban Remote Sensing, IEEE Press Wiley, Printed in the United States of America.

Whiteside, T., and Ahmad, W., (2004). Object-oriented classification of ASTER imagery for landcover mapping in monsoonal northern Australia. Proceedings of 12th Australasian Remote Sensing and Photogrammetry Conference.

Willhauck, G., Schneider, T., De Kok, R., and Ammer, U., (2000). Comparison of object-oriented classification techniques and standard image analysis for the use of change detection betweeen SPOT multispectral satellite images and aerial photos. Proceedings of XIX ISPRS Congress, 16-22 July, Amsterdam.

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