Abstract
Abstract. To deal with the problem of spectral variability in high resolution satellite images, this paper focuses on the analysis and modelling of spatial autocorrelation feature. The semivariograms are used to model spatial variability of typical object classes while Getis statistic is used for the analysis of local spatial autocorrelation within the neighbourhood window determined by the range information of the semivariograms. Two segmentation experiments are conducted via the Fuzzy C-Means (FCM) algorithm which incorporates both spatial autocorrelation features and spectral features, and the experimental results show that spatial autocorrelation features can effectively improve the segmentation quality of high resolution satellite images.
Highlights
High spatial resolution remote sensing imagery obtained from satellite (IKNOS, Quickbird, GeoEye-1, WorldView-2, etc) and airborne sensors have become increasingly available in recent years (Johnson & Xie, 2011)
This paper focuses on the analysis and modelling of spatial autocorrelation features for improving the segmentation quality of high resolution satellite images
The semivariograms are used to model spatial variability of typical object classes while Getis statistic is used to calculate the degree of local spatial autocorrelation
Summary
High spatial resolution remote sensing imagery obtained from satellite (IKNOS, Quickbird, GeoEye-1, WorldView-2, etc) and airborne sensors have become increasingly available in recent years (Johnson & Xie, 2011). These data provide amazing details of the Earth’s surface, but for information extraction from complex scene such as urban environment, it is difficult to obtain satisfactory results using only spectral information (Byun et al, 2011). Spatial autocorrelation provides us the structural information between spectral values of pixels, which is usually more stable and robust to noise than individual pixel This information may be used to improve the segmentation quality or classification accuracy for spectrally heterogeneous classes and overcome the current spectral limitations of very high spatial resolution satellite images
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