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Potentiality of using digital Wavelet/QMF pyramids in remotely sensed satellites’ images classification

    Ahmed Serwa   Affiliation

Abstract

Wavelet or quadrature mirror filter (QMF) satellites’ images are not commonly used in classification because of the modification in spectral responses that may confuse any classifier. Boundary pixels are hardly classified correctly in pixel-based classification especially in medium and coarse resolution. In such case, the sudden change in landcover is not measurable by the classifiers because the pixel may contain mor than one class. This research work is a trial to investigate the proper enhancement in accuracy that may occur by using wavelet/QMF bands’ pyramids are in classification instead of the original image bands. The reference map is prepared traditionally to measure the performance of the new system. The Wavelet/QMF image is constructed for each band of the satellite image. Then the classification is carried out for both the Wavelet/QMF image pyramid and the original satellite image using competitive learning neural networks (CLNN) method. The evaluation is carried out by comparing the classified Wavelet/QMF image with the classified original image. A statistical test is carried out to study the significance of using the classified Wavelet/ QMF image in classification.

Keyword : remote sensing, classification, Wavelet/QMF pyramid, accuracy assessment

How to Cite
Serwa, A. (2020). Potentiality of using digital Wavelet/QMF pyramids in remotely sensed satellites’ images classification. Geodesy and Cartography, 46(4), 163-169. https://doi.org/10.3846/gac.2020.11415
Published in Issue
Dec 21, 2020
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This work is licensed under a Creative Commons Attribution 4.0 International License.

References

Erdem, E. (n.d.). BBM 413: fundamentals of image processing. https://web.cs.hacettepe.edu.tr/~erkut/bbm413.f16/index.html

Gonzalez, R. C., Woods, R. E., & Masters, B. R. (2009). Digital image processing, third edition. Journal of Biomedical Optics, 14(2), 029901. https://doi.org/10.1117/1.3115362

Han, X., Zhong, Y., Cao, L., & Zhang, L. (2017). Pre-trained alexnet architecture with pyramid pooling and supervision for high spatial resolution remote sensing image scene classification. Remote Sensing, 9(8), 848. https://doi.org/10.3390/rs9080848

Serwa, A. (2009). Automatic extraction of topographic features from digital images [PhD thesis]. Azhar University.

Serwa, A. (2012). New method for feature reduction of mss satellite bands to produce single equivalent band. Al-Azhar University Engineering Journal, 7(1), 519–526.

Serwa, A. (2016). Development of soft computational simulator for aerial imagery project planning. Surveying and Land Information Science, 75(2), 65–75.

Serwa, A. (2020a). Correction to: studying the potentiality of using digital gaussian pyramids in multi-spectral satellites images classification. Journal of the Indian Society of Remote Sensing. https://doi.org/10.1007/s12524-020-01210-8

Serwa, A. (2020b). Studying the potentiality of using digital gaussian pyramids in multi-spectral satellites images classification. Journal of the Indian Society of Remote Sensing. https://doi.org/10.1007/s12524-020-01210-8

Serwa, A., Ali, M. E.-N. O., & Dief-Allah, M. A. M. (2010). Potential of fusion of fuzzy based and neural network classifiers for unsupervised classification. Al-Azhar University Engineering Journal, 5(1), 713–726.

Serwa, A., & El-Semary, H. H. (2020). Semi-automatic general approach to achieve the practical number of clusters for classification of remote sensing MS satellite images. Spatial Information Research, 28, 203–213.
https://doi.org/10.1007/s41324-019-00283-z

Yang, X., Sun, H., Fu, K., Yang, J., Sun, X., Yan, M., & Guo, Z. (2018). Automatic ship detection in remote sensing images from google earth of complex scenes based on multiscale rotation dense feature pyramid networks. Remote Sensing, 10(1), 132. https://doi.org/10.3390/rs10010132

Yue, J., Mao, S., & Li, M. (2016). A deep learning framework for hyperspectral image classification using spatial pyramid pooling. Remote Sensing Letters, 7(9), 875–884.
https://doi.org/10.1080/2150704X.2016.1193793

Zhang, L., & Yang, K. (2013). Region-of-interest extraction based on frequency domain analysis and salient region detection for remote sensing image. IEEE Geoscience and Remote Sensing Letters, 11(5), 916–920.
https://doi.org/10.1109/LGRS.2013.2281827