<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>When talking about land cover, we need to find a proper way to extract information from aerial or satellite images. In the field of photogrammetry, aerial images are generally acquired by optical sensors that deliver images in four bands (red, green, blue and near-infrared). Recent researches in this field demonstrated that for the image classification process is still place for improvement. From satellites are obtained multispectral images with more bands (e.g. Landsat 7/8 has 36 spectral bands). This paper will present the differences between these two types of images and the classification results using support-vector machine and maximum likelihood classifier. For the aerial and the satellite images we used different sets of classification classes and the two methods mentioned above to highlight the importance of choosing the classes and the classification method.</p>
</abstract>

When talking about land cover, we need to find a proper way to extract information from aerial or satellite images. In the field of photogrammetry, aerial images are generally acquired by optical sensors that deliver images in four bands (red, green, blue and near-infrared). Recent researches in this field demonstrated that for the image classification process is still place for improvement. From satellites are obtained multispectral images with more bands (e.g. Landsat 7/8 has 36 spectral bands). This paper will present the differences between these two types of images and the classification results using support-vector machine and maximum likelihood classifier. For the aerial and the satellite images we used different sets of classification classes and the two methods mentioned above to highlight the importance of choosing the classes and the classification method.

Urban Classification from Aerial and Satellite Images

Technical University of Civil Engineering of Bucharest

Faculty of Geodesy, Topography and Cadaster Department

Journal of Applied Engineering Sciences

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

{"article-title":"Urban Classification from Aerial and Satellite Images"}

When talking about land cover, we need to find a proper way to extract information from aerial or satellite images. In the field of photogrammetry, aerial...

Urban Classification from Aerial and Satellite Images

Online veröffentlicht: 11. Nov. 2020

Seitenbereich: 163 - 172

Eingereicht: 30. Mai 2020

Akzeptiert: 10. Juli 2020

DOI: https://doi.org/10.2478/jaes-2020-0024

Schlüsselwörter
photogrammetry, multispectral images, support-vector machine, maximum likelihood classifier

© 2020 Iuliana Maria Pârvu et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Urban Classification from Aerial and Satellite Images

Online veröffentlicht: 11. Nov. 2020

Seitenbereich: 163 - 172

Eingereicht: 30. Mai 2020

Akzeptiert: 10. Juli 2020

DOI: https://doi.org/10.2478/jaes-2020-0024

Schlüsselwörterphotogrammetry, multispectral images, support-vector machine, maximum likelihood classifier

© 2020 Iuliana Maria Pârvu et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Schlüsselwörter
photogrammetry, multispectral images, support-vector machine, maximum likelihood classifier