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Remote Sens. 2014, 6(12), 12138-12165; doi:10.3390/rs61212138

Application of In-Segment Multiple Sampling in Object-Based Classification

1
Slovenian Centre of Excellence for Space Sciences and Technologies SPACE-SI, Aškerčeva cesta 12, 1000 Ljubljana, Slovenia
2
Research Centre of the Slovenian Academy of Sciences and Arts, Novi trg 2, 1000 Ljubljana, Slovenia
*
Author to whom correspondence should be addressed.
Received: 14 July 2014 / Revised: 11 November 2014 / Accepted: 27 November 2014 / Published: 5 December 2014
(This article belongs to the Special Issue Advances in Geographic Object-Based Image Analysis (GEOBIA))
View Full-Text   |   Download PDF [3793 KB, uploaded 5 December 2014]   |  

Abstract

When object-based analysis is applied to very high-resolution imagery, pixels within the segments reveal large spectral inhomogeneity; their distribution can be considered complex rather than normal. When normality is violated, the classification methods that rely on the assumption of normally distributed data are not as successful or accurate. It is hard to detect normality violations in small samples. The segmentation process produces segments that vary highly in size; samples can be very big or very small. This paper investigates whether the complexity within the segment can be addressed using multiple random sampling of segment pixels and multiple calculations of similarity measures. In order to analyze the effect sampling has on classification results, statistics and probability value equations of non-parametric two-sample Kolmogorov-Smirnov test and parametric Student’s t-test are selected as similarity measures in the classification process. The performance of both classifiers was assessed on a WorldView-2 image for four land cover classes (roads, buildings, grass and trees) and compared to two commonly used object-based classifiers—k-Nearest Neighbor (k-NN) and Support Vector Machine (SVM). Both proposed classifiers showed a slight improvement in the overall classification accuracies and produced more accurate classification maps when compared to the ground truth image. View Full-Text
Keywords: GEOBIA; in-segment analysis; sampling; urban land cover classification; Kolmogorov-Smirnov test statistics; Student’s t-test statistics; probability value GEOBIA; in-segment analysis; sampling; urban land cover classification; Kolmogorov-Smirnov test statistics; Student’s t-test statistics; probability value
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Đurić, N.; Pehani, P.; Oštir, K. Application of In-Segment Multiple Sampling in Object-Based Classification. Remote Sens. 2014, 6, 12138-12165.

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