INTEGRATING PASSIVE AND ACTIVE REMOTE SENSING DATA WITH SPATIAL FILTERS FOR URBAN GROWTH ANALYSIS IN URMIA, IRAN

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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.

Об авторах

V. Isazade

Email: qasimi.abdul.a@gmail.com
ORCID iD: 0000-0002-6348-4028

E. Isazade

Email: qasimi.abdul.a@gmail.com

A. B. Qasimi

Email: qasimi.abdul.a@gmail.com
ORCID iD: 0000-0001-9180-831X

A. Serwa

Автор, ответственный за переписку.
Email: qasimi.abdul.a@gmail.com
ORCID iD: 0000-0002-5121-5242
ResearcherId: P-9953-2015

Список литературы

  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.
  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.
  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.
  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.
  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.
  6. Camargo, F. F., E. E. Sano, C. M. Almeida, J. C. Mura, and T. Almeida (2019), A Comparative Assessment of MachineLearning Techniques for Land Use and Land Cover Classification of the Brazilian Tropical Savanna Using ALOS2/PALSAR-2 Polarimetric Images, Remote Sensing, 11(13), 1600, https://doi.org/10.3390/rs11131600.
  7. Chang, S. G., B. Yu, and M. Vetterli (2000), Adaptive wavelet thresholding for image denoising and compression, IEEE Transactions on Image Processing, 9(9), 1532–1546, https://doi.org/10.1109/83.862633.
  8. Chen, J., Y. Chen, W. An, Y. Cui, and J. Yang (2011), Nonlocal Filtering for Polarimetric SAR Data: A Pretest Approach, IEEE Transactions on Geoscience and Remote Sensing, 49(5), 1744–1754, https://doi.org/10.1109/tgrs.2010.2087763.
  9. Dai, J., T. Zhu, Y. Wang, R. Ma, and X. Fang (2020), Road Extraction From High-Resolution Satellite Images Based on Multiple Descriptors, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 13, 227–240, https://doi.org/10.1109/jstars.2019.2955277.
  10. Dass, R., and N. Yadav (2020), Image Quality Assessment Parameters for Despeckling Filters, Procedia Computer Science, 167, 2382–2392, https://doi.org/10.1016/j.procs.2020.03.291.
  11. Fazel, N., R. Berndtsson, C. B. Uvo, K. Madani, and B. Kløve (2017), Regionalization of precipitation characteristics in Iran’s Lake Urmia basin, Theoretical and Applied Climatology, 132(1-2), 363–373, https://doi.org/10.1007/s00704-017-2090-0.
  12. Frost, V. S., J. A. Stiles, K. S. Shanmugan, and J. C. Holtzman (1982), A Model for Radar Images and Its Application to Adaptive Digital Filtering of Multiplicative Noise, IEEE Transactions on Pattern Analysis and Machine Intelligence, PAMI-4(2), 157–166, https://doi.org/10.1109/tpami.1982.4767223.
  13. Ghanbari, A., V. Isazade, and Z. Alibeigy (2022), Analysis of Spatial Justice in Dealing with Urban Facilities Using Quantitative, Spatial and Moran Correlation Models (Study Area: Isfahan), Journal Research and Urban Planning, 13(50), 240–254.
  14. Ghorbanian, A., M. Kakooei, M. Amani, S. Mahdavi, A. Mohammadzadeh, and M. Hasanlou (2020), Improved land cover map of Iran using Sentinel imagery within Google Earth Engine and a novel automatic workflow for land cover classification using migrated training samples, ISPRS Journal of Photogrammetry and Remote Sensing, 167, 276–288, https://doi.org/10.1016/j.isprsjprs.2020.07.013.
  15. Golilarz, N. A., H. Gao, and H. Demirel (2019), Satellite Image De-Noising With Harris Hawks Meta Heuristic Optimization Algorithm and Improved Adaptive Generalized Gaussian Distribution Threshold Function, IEEE Access, 7, 57,459–57,468, https://doi.org/10.1109/access.2019.2914101.
  16. Hu, T., J. Yang, X. Li, and P. Gong (2016), Mapping Urban Land Use by Using Landsat Images and Open Social Data, Remote Sensing, 8(2), 151, https://doi.org/10.3390/rs8020151.
  17. Isazade, V., A. Ghanbari, and K. Valizadeh (2021), Evaluation of spectral indices and extraction of constructed and nonconstructed urban features and its comparison with ground surface temperature using Landsat 7 and 8 satellite images (Study area, Tehran), Journal of Spatial Planning, 11(4), 23–39, https://doi.org/10.30488/GPS.2021.243118.3283.
  18. Isazade, V., A. B. Qasimi, and E. Isazade (2022), Environmental dust effect phenomenon on the sustainability of urban areas using remote sensing data in GEE, Safety in Extreme Environments, 5(1), 59–67, https://doi.org/10.1007/s42797-022-00067-z.
  19. Kaushik, K., and D. Kumar (2020), Forest Disturbance Mapping with Microwave Remote Sensing, Journal of Landscape Ecology, 13(2), 95–128, https://doi.org/10.2478/jlecol-2020-0012.
  20. Kraus, K. (2003), Photogrammetry, Remote Sensing and Spatial Information Sciences for heritage documentation, in Proceedings of the XIXth International Symposium, CIPA 2003: new perspectives to save cultural heritage, pp. 17–24, CIPA 2003 Organizing Committee, Antalya (Turkey).
  21. LaCasse, C. F., R. A. Chipman, and J. S. Tyo (2011), Band limited data reconstruction in modulated polarimeters, Optics Express, 19(16), 14,976, https://doi.org/10.1364/oe.19.014976.
  22. Lang, F., J. Yang, D. Li, L. Shi, and J. Wei (2014), Mean-Shift-Based Speckle Filtering of Polarimetric SAR Data, IEEE Transactions on Geoscience and Remote Sensing, 52(7), 4440–4454, https://doi.org/10.1109/tgrs.2013.2282036.
  23. Lang, F., J. Yang, and D. Li (2015), Adaptive-Window Polarimetric SAR Image Speckle Filtering Based on a Homogeneity Measurement, IEEE Transactions on Geoscience and Remote Sensing, 53(10), 5435–5446, https://doi.org/10.1109/tgrs.2015.2422737.
  24. Lapini, A., S. Pettinato, E. Santi, S. Paloscia, G. Fontanelli, and A. Garzelli (2020), Comparison of Machine Learning Methods Applied to SAR Images for Forest Classification in Mediterranean Areas, Remote Sensing, 12(3), 369, https://doi.org/10.3390/rs12030369.
  25. Lee, J.-S. (1986), Speckle Suppression and Analysis for Synthetic Aperture Radar Images, Optical Engineering, 25(5), 255,636, https://doi.org/10.1117/12.7973877.
  26. Lee, J.-S., M. R. Grunes, and G. de Grandi (1999), Polarimetric SAR speckle filtering and its implication for classification, IEEE Transactions on Geoscience and Remote Sensing, 37(5), 2363–2373, https://doi.org/10.1109/36.789635.
  27. Lee, J.-S., M. R. Grunes, D. L. Schuler, E. Pottier, and L. Ferro-Famil (2006), Scattering-model-based speckle filtering of polarimetric SAR data, IEEE Transactions on Geoscience and Remote Sensing, 44(1), 176–187, https://doi.org/10.1109/tgrs.2005.859338.
  28. Lee, J.-S., T. L. Ainsworth, Y. Wang, and K.-S. Chen (2015), Polarimetric SAR Speckle Filtering and the Extended Sigma Filter, IEEE Transactions on Geoscience and Remote Sensing, 53(3), 1150–1160, https://doi.org/10.1109/tgrs.2014.2335114.
  29. Leiva-Murillo, J. M., L. Gomez-Chova, and G. Camps-Valls (2013), Multitask Remote Sensing Data Classification, IEEE Transactions on Geoscience and Remote Sensing, 51(1), 151–161, https://doi.org/10.1109/TGRS.2012.2200043.
  30. Li, Y., S. Wang, Q. Zhao, and G. Wang (2020), A new SAR image filter for preserving speckle statistical distribution, Signal Processing, 176, 107,706, https://doi.org/10.1016/j.sigpro.2020.107706.
  31. Liu, N., Z. Cao, Z. Cui, Y. Pi, and S. Dang (2019), Multi-Scale Proposal Generation for Ship Detection in SAR Images, Remote Sensing, 11(5), 526, https://doi.org/10.3390/rs11050526.
  32. Mohan, M., and A. Kandya (2015), Impact of urbanization and land-use/land-cover change on diurnal temperature range: A case study of tropical urban airshed of India using remote sensing data, Science of The Total Environment, 506-507, 453–465, https://doi.org/10.1016/j.scitotenv.2014.11.006.
  33. Mun, J., Y. Jang, Y. Nam, and J. Kim (2019), Edge-enhancing bi-histogram equalisation using guided image filter, Journal of Visual Communication and Image Representation, 58, 688–700, https://doi.org/10.1016/j.jvcir.2018.12.037.
  34. NISAR (2018), NASA-ISRO SAR (NISAR) Mission Science Users’ Handbook, NASA Jet Propulsion Laboratory.
  35. Nouri, H., R. J. Mason, and N. Moradi (2017), Land suitability evaluation for changing spatial organization in Urmia County towards conservation of Urmia Lake, Applied Geography, 81, 1–12, https://doi.org/10.1016/j.apgeog.2017.02.006.
  36. Osgouei, P. E., S. Kaya, E. Sertel, and U. Alganci (2019), Separating Built-Up Areas from Bare Land in Mediterranean Cities Using Sentinel-2A Imagery, Remote Sensing, 11(3), 345, https://doi.org/10.3390/rs11030345.
  37. Podest, E. (2018), SAR for Mapping Land Cover, National Aeronautics and Space Administration.
  38. Qasimi, A. B., V. Isazade, G. Kaplan, and Z. Nadry (2022), Spatiotemporal and multi-sensor analysis of surface temperature, NDVI, and precipitation using google earth engine cloud computing platform, Russian Journal of Earth Sciences, 22(6), 1–12, https://doi.org/10.2205/2022ES000812.
  39. Rasche, C. (2018), Rapid contour detection for image classification, IET Image Processing, 12(4), 532–538, https://doi.org/10.1049/iet-ipr.2017.1066.
  40. Routray, S., P. P. Malla, S. K. Sharma, S. K. Panda, and G. Palai (2020), A new image denoising framework using bilateral filtering based non-subsampled shearlet transform, Optik, 216, 164,903, https://doi.org/10.1016/j.ijleo.2020.164903.
  41. Seo, S. (2018), Subpixel Edge Localization Based on Adaptive Weighting of Gradients, IEEE Transactions on Image Processing, 27(11), 5501–5513, https://doi.org/10.1109/tip.2018.2860241.
  42. Seo, S. (2019a), Subpixel Line Localization With Normalized Sums of Gradients and Location Linking With Straightness and Omni-Directionality, IEEE Access, 7, 155–167, https://doi.org/10.1109/access.2019.2959320.
  43. Seo, S. (2019b), Line-Detection Based on the Sum of Gradient Angle Differences, Applied Sciences, 10(1), 254, https://doi.org/10.3390/app10010254.
  44. Serwa, A. (2020), Studying the Potentiality of Using Digital Gaussian Pyramids in Multi-spectral Satellites Images Classification, Journal of the Indian Society of Remote Sensing, 48(12), 1651–1660, https://doi.org/10.1007/s12524-020-01173-w.
  45. Serwa, A., and S. Elbialy (2021), Enhancement of classification accuracy of multi-spectral satellites’ images using Laplacian pyramids, The Egyptian Journal of Remote Sensing and Space Science, 24(2), 283–291, https://doi.org/10.1016/j.ejrs.2020.12.006.
  46. Serwa, A., and M. Saleh (2021), New semi-automatic 3D registration method for terrestrial laser scanning data of bridge structures based on artificial neural networks, The Egyptian Journal of Remote Sensing and Space Science, 24(3, Part 2), 787–798, https://doi.org/10.1016/j.ejrs.2021.06.003.
  47. Seto, K. C., B. Güneralp, and L. R. Hutyra (2012), Global forecasts of urban expansion to 2030 and direct impacts on biodiversity and carbon pools, Proceedings of the National Academy of Sciences, 109(40), 16,083–16,088, https://doi.org/10.1073/pnas.1211658109.
  48. Shanthi, I., and M. L. Valarmathi (2013), SAR image despeckling using possibilistic fuzzy C-means clustering and edge detection in bandelet domain, Neural Computing and Applications, 23(S1), 279–291, https://doi.org/10.1007/s00521-013-1394-y.
  49. Shi, Y., Q. Li, and X. X. Zhu (2019), Building Footprint Generation Using Improved Generative Adversarial Networks, IEEE Geoscience and Remote Sensing Letters, 16(4), 603–607, https://doi.org/10.1109/LGRS.2018.2878486.
  50. Shokati, B., and B. Feizizadeh (2018), Sensitivity and uncertainty analysis of agro-ecological modeling for saffron plant cultivation using GIS spatial decision-making methods, Journal of Environmental Planning and Management, 62(3), 517–533, https://doi.org/10.1080/09640568.2018.1427561.
  51. Sun, H., W. Forsythe, and N. Waters (2007), Modeling Urban Land Use Change and Urban Sprawl: Calgary, Alberta, Canada, Networks and Spatial Economics, 7(4), 353–376, https://doi.org/10.1007/s11067-007-9030-y.
  52. Vasile, G., E. Trouve, J.-S. Lee, and V. Buzuloiu (2006), Intensity-driven adaptive-neighborhood technique for polarimetric and interferometric SAR parameters estimation, IEEE Transactions on Geoscience and Remote Sensing, 44(6), 1609–1621, https://doi.org/10.1109/tgrs.2005.864142.
  53. Wang, Y., J. Yang, and J. Li (2016), Similarity-intensity joint PolSAR speckle filtering, in 2016 CIE International Conference on Radar (RADAR), pp. 1–5, https://doi.org/10.1109/RADAR.2016.8059213.
  54. Wei, C., and T. Blaschke (2018), Pixel-Wise vs. Object-Based Impervious Surface Analysis from Remote Sensing: Correlations with Land Surface Temperature and Population Density, Urban Science, 2(1), https://doi.org/10.3390/urbansci2010002.
  55. Yang, C. H., B. K. Kenduiywo, and U. Soergel (2016), Change detection based on persistent scatterer interferometry – a new method of monitoring building changes, ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, III-7, 243–250, https://doi.org/10.5194/isprs-annals-III-7-243-2016.
  56. Zakeri, H., F. Yamazaki, and W. Liu (2017), Texture Analysis and Land Cover Classification of Tehran Using Polarimetric Synthetic Aperture Radar Imagery, Applied Sciences, 7(5), 452, https://doi.org/10.3390/app7050452.
  57. Zia, S., S. A. Shirazi, M. N. Bhalli, and S. Kausar (2015), The Impact Of Urbanization On Mean Annual Temperature Of Lahore Metropolitan Area, Pakistan, Pakistan Journal of Science, 67(3).

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