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Analysis of Built-Up Classes in Urbanised Zones Using Radar Images

Joanna Pluto-Kossakowska

Joanna Pluto-Kossakowska

Department of Photogrammetry, Remote Sensing and GIS, Warsaw University of TechnologyWarsaw, Poland
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Pluto-Kossakowska, Joanna
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Joanna Giczan

Joanna Giczan

Department of Photogrammetry, Remote Sensing and GIS, Warsaw University of TechnologyWarsaw, Poland
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Giczan, Joanna
  
07 sept. 2023
Analysis of Built-Up Classes in Urbanised Zones Using Radar Images's Cover Image
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Publié en ligne: 07 sept. 2023
Pages: 195 - 211
Reçu: 03 janv. 2023
DOI: https://doi.org/10.14746/quageo-2023-0032
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© 2023 Joanna Pluto-Kossakowska et al., published by Sciendo
This work is licensed under the Creative Commons Attribution 4.0 International License.

Fig. 1.

The extent of Gdynia’s boundary (purple lines) on the VV ICEYE image (right) and the visualisation of the UA database in the study area according to the UA legend (left). UA, Urban Atlas.
The extent of Gdynia’s boundary (purple lines) on the VV ICEYE image (right) and the visualisation of the UA database in the study area according to the UA legend (left). UA, Urban Atlas.

Fig. 2.

Sentinel-1 variables selected for classification. The same representative example for the Continuous urban fabric CUF class shown on orthophotomap in Fig. 5.
Sentinel-1 variables selected for classification. The same representative example for the Continuous urban fabric CUF class shown on orthophotomap in Fig. 5.

Fig. 3.

Results of supervised classification by RF (upper) and MD (lower) for Sentinel-1 with UA class outlines. The fragment shows the representation and variety of different classes in the area. The same legend is applicable as was mentioned in Table 2.
Results of supervised classification by RF (upper) and MD (lower) for Sentinel-1 with UA class outlines. The fragment shows the representation and variety of different classes in the area. The same legend is applicable as was mentioned in Table 2.

Fig. 4.

RF (100 trees) classification result on Sentinel-1 before (upper) and after aggregation to four classes (lower). Example of Port area (PA) class differentiation. The same legend is applicable as was mentioned in Table 2.
RF (100 trees) classification result on Sentinel-1 before (upper) and after aggregation to four classes (lower). Example of Port area (PA) class differentiation. The same legend is applicable as was mentioned in Table 2.

Fig. 5.

ICEYE variables selected for classification and contours of the Continuous urban fabric CUF class (as a representative example). The right-bottom orthophoto shows the scale and shape of features.
ICEYE variables selected for classification and contours of the Continuous urban fabric CUF class (as a representative example). The right-bottom orthophoto shows the scale and shape of features.

Fig. 6.

Results of classification using RF (upper) and MD (lower) on ICEYE image with UA class borders. The same legend is applicable as was mentioned in Table 2. This representative example shows the diversity of classes.
Results of classification using RF (upper) and MD (lower) on ICEYE image with UA class borders. The same legend is applicable as was mentioned in Table 2. This representative example shows the diversity of classes.

Fig. 8.

Comparison of Sentinel-1 (upper) and ICEYE (lower) results based on MD classifier, after class aggregation. The same legend is applicable as was mentioned in Table 2. This representative example shows the diversity of classes.
Comparison of Sentinel-1 (upper) and ICEYE (lower) results based on MD classifier, after class aggregation. The same legend is applicable as was mentioned in Table 2. This representative example shows the diversity of classes.

Fig. 7.

Overall classification accuracy (total and kappa) based on RF and MD classifiers for Sentinel-1 (S1, in green colours) and ICEYE (brown-orange colours).
Overall classification accuracy (total and kappa) based on RF and MD classifiers for Sentinel-1 (S1, in green colours) and ICEYE (brown-orange colours).

Urban Atlas classes selected for the study_ These code names and colours have been used in the forth-coming presentation of results_

Class name Sealed Level (SL) Codename and colour on images Name and colour of aggregated classes
Continuous urban fabric >80% CUF Dense urban area
Discontinuous dense urban fabric 50–80% DDUF
Discontinuous medium density urban fabric 30–50% DMDUF
Discontinuous low density urban fabric 10–30% DLDUF Low density urban area
Discontinuous very low density urban fabric <10% DVLDUF
Isolated structures IS
Port areas PA Industrial area
Industrial, commercial, public, military and private units ICPMAPU
Arable land (annual crops) AL Vegetation
Forests F
Pastures P
UA class borders

Specification of the SAR data used in the study_

Sensor Date Band Polarisation Orbit Mode Spatial resolution after corrections and resampling
ICEYE 19.04.2019 X (3 cm) VV Ascending SM 2 m
Sentinel-1 27.12.2018 C (5 cm) VH + VV Descending IW 10 m

Comparison of classification results in different images and different algorithms for discontinuous low and very low density urban fabric, both in low density urban area class; these representative examples visualise a general pattern_

Discontinuous low density urban fabric
orthophotomap Dense urban areaLow density urban areaIndustrial areaVegetationUrban Atlas feature
Sentinel-1 ICEYE
Random Forests
Minimum Distance
Discontinuous very low density urban fabric
orthophotomap
Sentinel-1 ICEYE
Random Forests
Minimum Distance

Sentinel-1 image classification accuracy by RF (top) and MD (bottom) algorithms – both results after aggregation_

Class value Vegetation Dense urban Low dens. urban Industrial Total U_Accuracy Kappa
RF classification
Vegetation 685 58 21 70 834 0.821 0
Dense urban 29 140 5 162 336 0.417 0
Low dens. urban 196 66 17 98 377 0.045 0
Industrial 28 103 2 321 454 0.707 0
Total 938 367 45 651 2001 0 0
P_Accuracy 0.730 0.381 0.378 0.493 0 0.581 0
Kappa 0 0 0 0 0 0 0.398
MD classification
Vegetation 747 63 21 57 888 0.841 0
Dense urban 32 144 3 118 297 0.485 0
Low dens. urban 141 56 21 114 332 0.063 0
Industrial 18 104 0 362 484 0.748 0
Total 938 367 45 651 2001 0 0
P_Accuracy 0.796 0.392 0.467 0.556 0 0.637 0
Kappa 0 0 0 0 0 0 0.468

Comparison of classification results in different images and different algorithms for Continuous urban fabric class and discontinuous dense urban fabric, both in one dense urban area class; these representative examples visualise a general pattern_

Continuous urban fabric
orthophotomap Dense urban areaLow density urban areaIndustrial areaVegetationUrban Atlas feature
Sentinel-1 ICEYE
Random Forests
Minimum Distance
Discontinuous dense urban fabric
orthophotomap
Sentinel-1 ICEYE
Random Forests
Minimum Distance
Langue:
Anglais
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Sujets de la revue:
Géosciences, Géographie
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