Segmentation is crucial for the quality of the final classification results. Therefore it is very important to evaluate the segmentation results using quantitative methods and to know what kind of input data is needed to obtain the best possible segmentation results. The impact of the segmentation algorithm, the parameter settings, as well as the spatial and spectral resolution of the data is investigated in the paper. Paper has been published in the publication with impact factor 0.892 (2013).
COBISS.SI-ID: 36528685
This paper investigated the performance of two proposed classifiers and analysed the complexity of pixel distribution within the segment which was addressed using multiple random sampling of segment pixels and multiple calculations of similarity measures – statistics of non-parametric Kolmogorov-Smirnov test and parametric Student’s t-test. The performance of both classifiers was assessed on a WorldView-2 image. Both proposed classifiers showed an improvement in the overall classification accuracies and produced more accurate classification maps. Paper has been published in the publication with impact factor 2.623 (2013, first quartile).
COBISS.SI-ID: 37833517
The paper in one of the important journals (IF 0.892) dealing with application of remote sensing examines whether satellite images can be used to see a green wave in the small but geographically diverse territory of Slovenia. We used the phenological products of the MODIS satellite system to analyze and calculate the correlations between the onset, decrease, and duration of greenness on one hand, and the elevation and distance from the sea on the other. A statistically reliable significant correlation was determined between onset of greenness increase (onset of the vegetation period) and elevation. The other correlations did not attain such a high significance.
COBISS.SI-ID: 36822829