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.
Highlights
Cost and time reduction can be achieved in remote sensing systems by automation (Serwa, 2016)
Some researchers tended to explore the effect of using digital image pyramids in remote sensing classification such as Serwa (2020b) who made a complete investigation of using Gaussian pyramids in classification
The reason of the enhancement in classification accuracy can be explained by increasing the performance of the classifier concerning with the boundary pixel by making a true decision for
Summary
Cost and time reduction can be achieved in remote sensing systems by automation (Serwa, 2016). Digital image pyramids is expressed as an abstraction tools that can increase automation accompanied by a change in size, resolution and grey levels (Serwa, 2020b). Some researchers tended to explore the effect of using digital image pyramids in remote sensing classification such as Serwa (2020b) who made a complete investigation of using Gaussian pyramids in classification. Serwa (2012) made a compression for MS satellites’ images producing a single band pyramid produced for remote sensing classification without the to use the full bands images. While pre-trained Alex-Net architecture having pyramid pooling and supervision were used by Han et al (2017) for high spatial resolution remote sensing classification. In this research the classification accuracy is used as measurement of performance of using Wavelet/QMF pyramid in classification
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