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2 2017

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Precision of sub-pixel image displacement measurements

Lutowski Z., Marciniak B., Marciniak T., Bujnowski S.

Journal of Machine Construction and Maintenance - Problemy Eksploatacji

2017

no. 4

21--26

Digital image correlation (DIC) and Optical flow method (Flow) are among the widely used methods in displacement detection applications. Both methods allow the use of sub-pixel information, leading to increase accuracy. The authors decided to verify the accuracy of the displacement detection of these methods by comparing the results with real displacement. The tests were performed on a special system for fatigue properties testing of microobjects (MFS). It allows setting very small movement, smaller than the value corresponding to the pixel size in the image. This allowed estimating measuring errors for both methods.

Cyfrowa korelacja obrazów (DIC) i metoda przepływów (Optical flow) należą do szeroko wykorzystywanych metod w aplikacjach pozwalających na wykrywanie przemieszczeń. Obie metody pozwalają na wykorzystanie informacji subpikselowych, co prowadzi do zwiększenia dokładności. Autorzy artykułu postanowili sprawdzić i zweryfikować dokładności wykrywania przesunięcia przez te metody poprzez porównanie wyników z rzeczywistymi przesunięciami. Badania wykonano na specjalnym stanowisku badawczym MFS. Pozwala ono na zadawanie przesunięć w bardzo małym zakresie, tzn. o rząd mniejszych niż wartości odpowiadające rozmiarowi piksela w obrazie. Przeprowadzone badania pozwoliły na wyznaczenie błędu pomiaru obu metod.

Thorough statistical comparison of machine learning regression models and their ensembles for sub-pixel imperviousness and imperviousness change mapping

Drzewiecki W.

Geodesy and Cartography

2017

Vol. 66, no. 2

171--209

We evaluated the performance of nine machine learning regression algorithms and their ensembles for sub-pixel estimation of impervious areas coverages from Landsat imagery. The accuracy of imperviousness mapping in individual time points was assessed based on RMSE, MAE and R2. These measures were also used for the assessment of imperviousness change intensity estimations. The applicability for detection of relevant changes in impervious areas coverages at sub-pixel level was evaluated using overall accuracy, F-measure and ROC Area Under Curve. The results proved that Cubist algorithm may be advised for Landsat-based mapping of imperviousness for single dates. Stochastic gradient boosting of regression trees (GBM) may be also considered for this purpose. However, Random Forest algorithm is endorsed for both imperviousness change detection and mapping of its intensity. In all applications the heterogeneous model ensembles performed at least as well as the best individual models or better. They may be recommended for improving the quality of sub-pixel imperviousness and imperviousness change mapping. The study revealed also limitations of the investigated methodology for detection of subtle changes of imperviousness inside the pixel. None of the tested approaches was able to reliably classify changed and non-changed pixels if the relevant change threshold was set as one or three percent. Also for fi ve percent change threshold most of algorithms did not ensure that the accuracy of change map is higher than the accuracy of random classifi er. For the threshold of relevant change set as ten percent all approaches performed satisfactory.