Extracting impervious surfaces from full polarimetric SAR images in different urban areas

Attarchi, Sara

doi:10.1080/01431161.2020.1723178

Cited by 22 publications

(9 citation statements)

References 39 publications

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“…Although still showing satisfactory overall classification accuracy and Kappa indices (Figure 13c; M1 to M6), the additional use of SAR derived texture applied over the intensity backscattering coefficients would indeed render an increase of the classification accuracy scores. This initiative was reported recently by Attarchi [63] when studying different urban environments with a single date acquisition of the quad-polarization ALOS/PALSAR. Interestingly would be the use of seasonal SAR quad-polarization in the classification scheme.…”

Section: Classification Of Land Cover Classes In Different Data Input...mentioning

confidence: 96%

Benefits of Combining ALOS/PALSAR-2 and Sentinel-2A Data in the Classification of Land Cover Classes in the Santa Catarina Southern Plateau

et al. 2021

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The Santa Catarina Southern Plateau is located in Southern Brazil and is a region that has gained considerable attention due to the rapid conversion of the typical landscape of natural grasslands and wetlands into agriculture, reforestation, pasture, and more recently, wind farms. This study’s main goal was to characterize the polarimetric attributes of the experimental quad-polarization acquisition mode of the Advanced Land Observing Satellite/Phased Array type L-band Synthetic Aperture Radar (ALOS/PALSAR-2) for mapping seven land cover classes. The polarimetric attributes were evaluated alone and combined with SENTINEL-2A using a supervised classification method based on the Support Vector Machine (SVM) algorithm. The results showed that the intensity backscattering alone reached an overall classification accuracy of 37.48% and a Kappa index of 0.26. Interestingly, the addition of polarimetric features increased to 71.35% and 0.66, respectively. It shows that the use of polarimetric decomposition features was relatively efficient in discriminating land cover classes. SENTINEL-2A data alone performed better and achieved a weighted overall accuracy and Kappa index of 85.56% and 0.82. This increase was also significant for the Z-test. However, the addition of ALOS/PALSAR-2 derived features to SENTINEL-2A slightly improved accuracy and was marginally significant at a 95% confidence level only when all features were considered. Possible implications for that performance are the accumulated precipitation prior to SAR data acquisition, which coincides with the rainy season period. The experimental quad-polarization mode of ALOS/PALSAR- 2 shall be evaluated in the near future over different seasonal conditions to confirm results. Alternatively, further studies are then suggested by focusing on additional features derived from SAR data such as texture and interferometric coherence to increase classification accuracy. These measures would be an interesting data source for monitoring specific land cover classes such as the threatened grasslands and wetlands during periods of frequent cloud coverage. Future investigations could also address multitemporal approaches employing either single or multifrequency SAR.

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Section: Classification Of Land Cover Classes In Different Data Input...mentioning

confidence: 96%

Benefits of Combining ALOS/PALSAR-2 and Sentinel-2A Data in the Classification of Land Cover Classes in the Santa Catarina Southern Plateau

et al. 2021

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“…Imperviousness represents the percentage of soil sealing (the covering of land by an impermeable material). Imperviousness is a key indicator of urbanization which provides an estimation of population distribution (Attarchi, 2020). The degree of imperviousness (0-100%) was downloaded as a GeoTIFF raster file from the Copernicus Land Monitoring Service (Langanke, 2018).…”

Section: Meteorological Variablesmentioning

confidence: 99%

Spatio-temporal modelling of $\text{PM}_{10}$ daily concentrations in Italy using the SPDE approach

Fioravanti,

Martino,

Cameletti

et al. 2020

Preprint

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This paper illustrates the main results of a spatio-temporal interpolation process of PM 10 concentrations at daily resolution using a set of 410 monitoring sites, distributed throughout the Italian territory, for the year 2015. The interpolation process is based on a Bayesian hierarchical model where the spatial-component is represented through the Stochastic Partial Differential Equation (SPDE) approach with a lag-1 temporal autoregressive component (AR1). Inference is performed through the Integrated Nested Laplace Approximation (INLA). Our model includes 11 spatial and spatio-temporal predictors, including meteorological variables and Aerosol Optical Depth. As the predictors' impact varies across months, the regression is based on 12 monthly models with the same set of covariates. The predictive model performance has been analyzed using a cross-validation study. Our results show that the predicted and the observed values are well in accordance (correlation range: 0.79 0.91; bias: 0.22 1.07 µg/m 3 ; RMSE: 4.9 13.9 µg/m 3 ). The model final output is a set of 365 gridded (1km × 1km) daily PM 10 maps over Italy equipped with an uncertainty measure.The spatial prediction performance shows that the interpolation procedure is able to reproduce the large scale data features without unrealistic artifacts in the generated PM 10 surfaces. The paper presents also two illustrative examples

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“…The classification of PolSAR images is usually challenging (Han et al, 2020;Parikh et al, 2020;Shang et al, 2020;Gopal Singh et al, 2021). To overcome these challenges, many prominent machine learning algorithms like Artificial Neural Network (ANN), Support Vector Machine (SVM), K Means (KM), K Nearest Neighbour (KNN), Gaussian Mixture Model (GMM), Ensemble Learning (EL), Linear Discriminative Laplacian Eigenmaps (LDLE) have been applied to PolSAR image classification (Attarchi, 2020;Parikh et al, 2020;Wang et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

Deep support vector machine for PolSAR image classification

Okwuashi

Ndehedehe

Olayinka

et al. 2021

International Journal of Remote Sensing

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The main problem posed by Polarimetric Synthetic Aperture Radar (PolSAR) image classification in remote sensing is the ability to develop classifiers that can substantially discern the different classes inherent in natural and man-made targets. Emphasis has shifted from the use of conventional classifiers to modern non-parametric classifiers such as the Artificial Neural Network (ANN) and Support Vector Machine (SVM); and most recently the hybrid Deep Neural Network (DNN) which is a fusion of Deep Learning (DL) and ANN. This research therefore presents the novel application of Deep Support Vector Machine (DSVM) which is a fusion of DL and SVM to PolSAR image classification. Two PolSAR images of Flevoland region in Netherlands, and Winnipeg in Canada are used as test beds for the experiment. The Lee filter is used to filter the images to suppress the speckle noise in the images. The Pauli decomposition is applied to decompose the images into |𝑆𝑆 𝐻𝐻𝐻𝐻 + 𝑆𝑆 𝑉𝑉𝑉𝑉 |, |𝑆𝑆 𝐻𝐻𝐻𝐻 − 𝑆𝑆 𝑉𝑉𝑉𝑉 |, |𝑆𝑆 𝐻𝐻𝑉𝑉 | polarimetric channels. Then the Gray Level Co-occurrence Matrix (GLCM) texture feature for |𝑆𝑆 𝐻𝐻𝐻𝐻 + 𝑆𝑆 𝑉𝑉𝑉𝑉 |, |𝑆𝑆 𝐻𝐻𝐻𝐻 − 𝑆𝑆 𝑉𝑉𝑉𝑉 |, |𝑆𝑆 𝐻𝐻𝑉𝑉 | are extracted based on correlation, contrast, energy, and homogeneity statistics, using GLCM directions 0 0 , 45 0 , 90 0 , and 135 0 with an offset distance of 60. To enhance the efficiency of the model 8, 16, 32, 64, 128, and 256 quantization levels are explored. The DSVM classifier is implemented with four kernel function: Exponential Radial Basis Function (ERBF), Gaussian Radial Basis Function (GRBF), neural, and polynomial. The first set of results is a comparison of the DSVM and SVM.

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Extracting impervious surfaces from full polarimetric SAR images in different urban areas

Cited by 22 publications

References 39 publications

Benefits of Combining ALOS/PALSAR-2 and Sentinel-2A Data in the Classification of Land Cover Classes in the Santa Catarina Southern Plateau

Benefits of Combining ALOS/PALSAR-2 and Sentinel-2A Data in the Classification of Land Cover Classes in the Santa Catarina Southern Plateau

Spatio-temporal modelling of $\text{PM}_{10}$ daily concentrations in Italy using the SPDE approach

Deep support vector machine for PolSAR image classification

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