Correction to: Studying the Potentiality of Using Digital Gaussian Pyramids in Multi-spectral Satellites Images Classification

Serwa, A.

doi:10.1007/s12524-020-01210-8

Cited by 1 publication

(4 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Every pyramid may be used in such application according to its attributes and size. Some applications require up sampled pyramid and others require down sampled of the original image (Serwa, 2020a). The common normal form of the digital pyramid UDC 528.852…”

Section: Digital Pyramidsmentioning

confidence: 99%

“…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). The problem of changing the original spectral reflectance of the objects in remote sensing images lead excluding the use of processed images in classification (Serwa, 2020b).…”

Section: Introductionmentioning

confidence: 99%

“…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). The problem of changing the original spectral reflectance of the objects in remote sensing images lead excluding the use of processed images in classification (Serwa, 2020b). On contrary, 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.…”

Section: Introductionmentioning

confidence: 99%

“…But it is overcomplete and it has a high frequency residual form. In this research Wavelet/QMF pyramid with average filter is used due to it is the most used in most of remote sensing applications (Serwa, 2020b).…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Potentiality of Using Digital Wavelet/QMF Pyramids in Remotely Sensed Satellites’ Images Classification

Serwa

2020

Geodesy and Cartography

Self Cite

View full text Add to dashboard Cite

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.

show abstract

Section: Digital Pyramidsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%