Comparative Analysis of Feature Extraction and Pixel-based Classification of High-Resolution Satellite Images Using Geospatial Techniques

Alaa Adnan Musleh; Hussein Sabah Jaber

doi:10.1051/e3sconf/202131804007

All issues

Volume 318 (2021)

E3S Web Conf., 318 (2021) 04007

References

Open Access

Issue		E3S Web Conf. Volume 318, 2021 Second International Conference on Geotechnical Engineering – Iraq (ICGE 2021)


Article Number		04007
Number of page(s)		12
Section		Remote Sensing and Environmental Engineering
DOI		https://doi.org/10.1051/e3sconf/202131804007
Published online		08 November 2021

Abburu, S. and Golla, S.B., 2015. Satellite image classification methods and techniques: A review. International journal of computer applications, 119(8). [Google Scholar]
Trock, C. F. S., 2018. Land cover classification of urban areas : A comparison of object-based and pixel-based approaches. M.Sc. Thesis, Aalborg University Copenhagen. [Google Scholar]
Bukheet, Y.C., Al-Abudi, B.Q. and Mahdi, M.S., 2016. Land Cover Change Detection of Baghdad City Using Multi-Spectral Remote Sensing Imagery. Iraqi Journal of Science, pp.195–214. [Google Scholar]
Aggarwal, N., Srivastava, M. and Dutta, M., 2016. Comparative analysis of pixel-based and object-based classification of high resolution remote sensing images—A review. International Journal of Engineering Trends and Technology, 38(1), pp.5–11. [Google Scholar]
Mather, P. and Tso, B., 2016. Classification methods for remotely sensed data. CRC press. [Google Scholar]
Hay, G.J. and Castilla, G., 2006, July. Object-based image analysis: strengths, weaknesses, opportunities and threats (SWOT). In Proc. 1st Int. Conf. OBIA (pp. 4–5). [Google Scholar]
Abbas, Z. and Jaber, H.S., 2020, March. Accuracy assessment of supervised classification methods for extraction land use maps using remote sensing and GIS techniques. In IOP Conference Series: Materials Science and Engineering (Vol. 745, No. 1, p. 012166). IOP Publishing. [Google Scholar]
Taati, A., Sarmadian, F., Mousavi, A., Pour, C.T.H. and Shahir, A.H.E., 2015. Land use classification using support vector machine and maximum likelihood algorithms by Landsat 5 TM images. Walailak Journal of Science and Technology (WJST), 12(8), pp.681–687. [Google Scholar]
Merzah, Z.F. and Jaber, H.S., 2020, March. Assessment of Atmospheric Correction Methods for Hyperspectral Remote Sensing Imagery Using Geospatial Techniques. In IOP Conference Series: Materials Science and Engineering (Vol. 745, No. 1, p. 012123). IOP Publishing. [Google Scholar]
Changhui, Y., Shaohong, S., Jun, H. and Yaohua, Y., 2010, April. An object-based change detection approach using high-resolution remote sensing image and GIS data. In 2010 International Conference on Image Analysis and Signal Processing (pp. 565–569). IEEE. [Google Scholar]
Miranda, E., Mutiara, A.B. and Wibowo, W.C., 2018, September. Classification of land cover from Sentinel-2 imagery using supervised classification technique (preliminary study). In 2018 International Conference on Information Management and Technology (ICIMTech) (pp. 69–74). IEEE. [Google Scholar]
Oommen, T., Misra, D., Twarakavi, N.K., Prakash, A., Sahoo, B. and Bandopadhyay, S., 2008. An objective analysis of support vector machine based classification for remote sensing. Mathematical Geosciences, 40(4), pp.409–424. [Google Scholar]
Blaschke, T., 2010. Object based image analysis for remote sensing. ISPRS journal of photogrammetry and remote sensing, 65(1), pp.2–16. [Google Scholar]
Li, M., Zang, S., Zhang, B., Li, S. and Wu, C., 2014. A review of remote sensing image classification techniques: The role of spatio-contextual information. European Journal of Remote Sensing, 47(1), pp.389–411. [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.

[1] Abburu, S. and Golla, S.B., 2015. Satellite image classification methods and techniques: A review. International journal of computer applications, 119(8). [Google Scholar]

[2] Trock, C. F. S., 2018. Land cover classification of urban areas : A comparison of object-based and pixel-based approaches. M.Sc. Thesis, Aalborg University Copenhagen. [Google Scholar]

[3] Bukheet, Y.C., Al-Abudi, B.Q. and Mahdi, M.S., 2016. Land Cover Change Detection of Baghdad City Using Multi-Spectral Remote Sensing Imagery. Iraqi Journal of Science, pp.195–214. [Google Scholar]

[4] Aggarwal, N., Srivastava, M. and Dutta, M., 2016. Comparative analysis of pixel-based and object-based classification of high resolution remote sensing images—A review. International Journal of Engineering Trends and Technology, 38(1), pp.5–11. [Google Scholar]

[5] Mather, P. and Tso, B., 2016. Classification methods for remotely sensed data. CRC press. [Google Scholar]

[6] Hay, G.J. and Castilla, G., 2006, July. Object-based image analysis: strengths, weaknesses, opportunities and threats (SWOT). In Proc. 1st Int. Conf. OBIA (pp. 4–5). [Google Scholar]

[7] Abbas, Z. and Jaber, H.S., 2020, March. Accuracy assessment of supervised classification methods for extraction land use maps using remote sensing and GIS techniques. In IOP Conference Series: Materials Science and Engineering (Vol. 745, No. 1, p. 012166). IOP Publishing. [Google Scholar]

[8] Taati, A., Sarmadian, F., Mousavi, A., Pour, C.T.H. and Shahir, A.H.E., 2015. Land use classification using support vector machine and maximum likelihood algorithms by Landsat 5 TM images. Walailak Journal of Science and Technology (WJST), 12(8), pp.681–687. [Google Scholar]

[9] Merzah, Z.F. and Jaber, H.S., 2020, March. Assessment of Atmospheric Correction Methods for Hyperspectral Remote Sensing Imagery Using Geospatial Techniques. In IOP Conference Series: Materials Science and Engineering (Vol. 745, No. 1, p. 012123). IOP Publishing. [Google Scholar]

[10] Changhui, Y., Shaohong, S., Jun, H. and Yaohua, Y., 2010, April. An object-based change detection approach using high-resolution remote sensing image and GIS data. In 2010 International Conference on Image Analysis and Signal Processing (pp. 565–569). IEEE. [Google Scholar]

[11] Miranda, E., Mutiara, A.B. and Wibowo, W.C., 2018, September. Classification of land cover from Sentinel-2 imagery using supervised classification technique (preliminary study). In 2018 International Conference on Information Management and Technology (ICIMTech) (pp. 69–74). IEEE. [Google Scholar]

[12] Oommen, T., Misra, D., Twarakavi, N.K., Prakash, A., Sahoo, B. and Bandopadhyay, S., 2008. An objective analysis of support vector machine based classification for remote sensing. Mathematical Geosciences, 40(4), pp.409–424. [Google Scholar]

[13] Blaschke, T., 2010. Object based image analysis for remote sensing. ISPRS journal of photogrammetry and remote sensing, 65(1), pp.2–16. [Google Scholar]

[14] Li, M., Zang, S., Zhang, B., Li, S. and Wu, C., 2014. A review of remote sensing image classification techniques: The role of spatio-contextual information. European Journal of Remote Sensing, 47(1), pp.389–411. [Google Scholar]