Ms. Katarzyna K. Bernat Profile

Katarzyna K. Bernat

PhD Student at AGH Univ of Science and Technology

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Publications (4)

Proceedings Article | 15 October 2015 Paper

A comparative study of Landsat TM and RapidEye imagery for two-stage impervious surface coverage estimation

Katarzyna Bernat, Wojciech Drzewiecki

Proceedings Volume 9643, 96432B (2015) https://doi.org/10.1117/12.2194880

KEYWORDS: Landsat, Image resolution, Image classification, Error analysis, Satellites, Earth observing sensors, Satellite imaging, Data modeling, Remote sensing, Machine learning

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Proceedings Article | 15 October 2015 Paper

A study of selected textural features usefulness for impervious surface coverage estimation using Landsat images

Katarzyna Bernat, Wojciech Drzewiecki

Proceedings Volume 9643, 964327 (2015) https://doi.org/10.1117/12.2194820

KEYWORDS: Earth observing sensors, Landsat, Data modeling, Error analysis, Image classification, Binary data, Image analysis, Remote sensing, Analytical research, Satellites

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The aim of our research was to evaluate the applicability of textural measures for sub-pixel impervious surfaces estimation using Landsat TM images based on machine learning algorithms. We put the particular focus on determining usefulness of five textural features groups in respect to pixel- and sub-pixel level. However, the two-stage approach to impervious surfaces coverage estimation was also tested. We compared the accuracy of impervious surfaces estimation using spectral bands only with results of imperviousness index estimation based on extended classification features sets (spectral band values supplemented with measures derived from various textural characteristics groups).

Impervious surfaces coverage estimation was done using decision and regression trees based on C5.0 and Cubist algorithms. At the stage of classification the research area was divided into two categories: i) completely permeable (imperviousness index less than 1%) and ii) fully or partially impervious areas. At the stage of sub-pixel classification evaluation of percentage impervious surfaces coverage within single pixel was done. Based on the results of cross-validation, we selected the approaches guaranteeing the lowest means errors in terms of training set. Accuracy of the imperviousness index estimation was checked based on validation data set. The average error of hard classification using spectral features only was 6.5% and about 4.4% for spectral features combining with absolute gradient-based characteristics. The root mean square error (RMSE) of determination of the percentage impervious surfaces coverage within a single pixel was equal to 9.46% for the best tested classification features sets. The two-stage procedure was utilized for the primary approach involving spectral bands as the classification features set and for the approach guaranteeing the best accuracy for classification and regression stage.

The results have shown that inclusion of textural measures into classification features can improve the estimation of imperviousness based on Landsat imagery. However, it seems that in our study this is mainly due higher accuracy of hard classification used for masking out the completely permeable pixels.

Proceedings Article | 23 October 2014 Paper

Two-stage subpixel impervious surface coverage estimation: comparing classification and regression trees and artificial neural networks

Katarzyna Bernat, Wojciech Drzewiecki

Proceedings Volume 9244, 92441I (2014) https://doi.org/10.1117/12.2067308

KEYWORDS: Artificial neural networks, Evolutionary algorithms, Landsat, Neural networks, Satellite imaging, Satellites, Image resolution, Earth observing sensors, Error analysis, Data modeling

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Proceedings Article | 13 October 2014 Paper

The usefulness of wavelet-based features as global descriptors of VHR satellite images

Krystian Pyka, Wojciech Drzewiecki, Katarzyna Bernat, Anna Wawrzaszek, Michal Krupiński

Proceedings Volume 9244, 92441D (2014) https://doi.org/10.1117/12.2067323

KEYWORDS: Wavelets, Satellites, Satellite imaging, Earth observing sensors, Wavelet transforms, Image classification, Discrete wavelet transforms, Linear filtering, Image analysis, Image resolution

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