Integrating Passive and Active Remote Sensing Data with Spatial Filters for Urban Growth Analysis in Urmia, Iran-Reference-Cited by-同舟云学术

Integrating Passive and Active Remote Sensing Data with Spatial Filters for Urban Growth Analysis in Urmia, Iran

Published:2023-12-30 Issue: Volume: Page:1-16
ISSN:1681-1208
Container-title:Russian Journal of Earth Sciences
language:en
Short-container-title:

Author:

Isazade Vahid¹^ORCID,Isazade Esmail²,Qasimi Abdul Baser³^ORCID,Serwa Ahmed⁴^ORCID

Affiliation:

1. Department of department of geographical science

2. Department of Urban planning

3. Department of geography, Faculty of Education, Samangan university

4. Faculty of Engineering in El-Mataria, Helwan University

Abstract

Active remote sensing and related technologies are one of the new tools recently used to monitor complications and urban growth. This research aims to investigate the effect of spatial filters on urban complications. The aim of this paper is to compare Lee, Frost and Average spatial filters with Landsat 8 satellite images and radar images with HH/HV polarization to investigate and identify urban features in the west of Urmia City. The results showed that Filterelli with the kernel 3 x 3 had reduced the spike noise in Alus Palsard satellite radar images in identifying the growth of urban tolls. Also, the results of K-means classification, the Lee filter with kernel size 3 x 3 more accurately identifies the urban features of the west of Urmia City. The kappa coefficient was 0.96%, and the overall accuracy of this filter was 97.36%. Therefore, Lee’s spatial filter has successfully identified the urban features of west Urmia with high accuracy. This system can be implemented in any other field due to its generality and reliability. This system may be a step towards remote sensing automation.

Publisher

Geophysical Center of the Russian Academy of Sciences

Reference57 articles.

1. Abdollahi, A., H. R. R. Bakhtiari, and M. P. Nejad (2017), Investigation of SVM and Level Set Interactive Methods for Road Extraction from Google Earth Images, Journal of the Indian Society of Remote Sensing, 46(3), 423–430, https://doi.org/10.1007/s12524-017-0702-x., Abdollahi, A., H. R. R. Bakhtiari, and M. P. Nejad (2017), Investigation of SVM and Level Set Interactive Methods for Road Extraction from Google Earth Images, Journal of the Indian Society of Remote Sensing, 46(3), 423–430, https://doi.org/10.1007/s12524-017-0702-x.

2. Alenin, A. S., and J. S. Tyo (2014), Generalized channeled polarimetry, Journal of the Optical Society of America A, 31(5), 1013, https://doi.org/10.1364/josaa.31.001013., Alenin, A. S., and J. S. Tyo (2014), Generalized channeled polarimetry, Journal of the Optical Society of America A, 31(5), 1013, https://doi.org/10.1364/josaa.31.001013.

3. Alves, W. A. L., C. F. Gobber, D. J. Silva, A. Morimitsu, R. F. Hashimoto, and B. Marcotegui (2020), Image segmentation based on ultimate levelings: From attribute filters to machine learning strategies, Pattern Recognition Letters, 133, 264–271, https://doi.org/10.1016/j.patrec.2020.03.013., Alves, W. A. L., C. F. Gobber, D. J. Silva, A. Morimitsu, R. F. Hashimoto, and B. Marcotegui (2020), Image segmentation based on ultimate levelings: From attribute filters to machine learning strategies, Pattern Recognition Letters, 133, 264–271, https://doi.org/10.1016/j.patrec.2020.03.013.

4. Attarchi, S., M. Poorrahimi, and V. Isazade (2020), Comparison of spectral indices and object-based classification for built-up area extraction in different urban areas, Geographical Urban Planning Research (GUPR), 8(1), 23–43, https://doi.org/10.22059/jurbangeo.2020.299492.1249., Attarchi, S., M. Poorrahimi, and V. Isazade (2020), Comparison of spectral indices and object-based classification for built-up area extraction in different urban areas, Geographical Urban Planning Research (GUPR), 8(1), 23–43, https://doi.org/10.22059/jurbangeo.2020.299492.1249.

5. Betbeder, J., S. Rapinel, S. Corgne, E. Pottier, and L. Hubert-Moy (2015), TerraSAR-X dual-pol time-series for mapping of wetland vegetation, ISPRS Journal of Photogrammetry and Remote Sensing, 107, 90–98, https://doi.org/10.1016/j.isprsjprs.2015.05.001., Betbeder, J., S. Rapinel, S. Corgne, E. Pottier, and L. Hubert-Moy (2015), TerraSAR-X dual-pol time-series for mapping of wetland vegetation, ISPRS Journal of Photogrammetry and Remote Sensing, 107, 90–98, https://doi.org/10.1016/j.isprsjprs.2015.05.001.