Volume XLI-B7
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLI-B7, 431-435, 2016
https://doi.org/10.5194/isprs-archives-XLI-B7-431-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLI-B7, 431-435, 2016
https://doi.org/10.5194/isprs-archives-XLI-B7-431-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.

  21 Jun 2016

21 Jun 2016

A DATA FIELD METHOD FOR URBAN REMOTELY SENSED IMAGERY CLASSIFICATION CONSIDERING SPATIAL CORRELATION

Y. Zhang1, K. Qin1,2, C. Zeng1, E. B. Zhang1, M. X. Yue1, and X. Tong1 Y. Zhang et al.
  • 1School of Remote Sensing and Information Engineering, Wuhan University, Luoyu Road, Wuhan, China
  • 2Collaborative Innovation Center for Geospatial Technology, 129 Luoyu Road, Wuhan, China

Keywords: Data Field, Spatial Correlation, High-Spatial-Resolution, Urban Remotely Sensed Imagery, Classification

Abstract. Spatial correlation between pixels is important information for remotely sensed imagery classification. Data field method and spatial autocorrelation statistics have been utilized to describe and model spatial information of local pixels. The original data field method can represent the spatial interactions of neighbourhood pixels effectively. However, its focus on measuring the grey level change between the central pixel and the neighbourhood pixels results in exaggerating the contribution of the central pixel to the whole local window. Besides, Geary’s C has also been proven to well characterise and qualify the spatial correlation between each pixel and its neighbourhood pixels. But the extracted object is badly delineated with the distracting salt-and-pepper effect of isolated misclassified pixels. To correct this defect, we introduce the data field method for filtering and noise limitation. Moreover, the original data field method is enhanced by considering each pixel in the window as the central pixel to compute statistical characteristics between it and its neighbourhood pixels. The last step employs a support vector machine (SVM) for the classification of multi-features (e.g. the spectral feature and spatial correlation feature). In order to validate the effectiveness of the developed method, experiments are conducted on different remotely sensed images containing multiple complex object classes inside. The results show that the developed method outperforms the traditional method in terms of classification accuracies.