Geometrical Uncertainty of Objects and Its Influence in the Object-Oriented Multi-Source Remote Sensing Imagery Processing

Zhaocong Wu, Lina Yi and Guifeng Zhang
School of remote sensing and information engineering, Wuhan University, Wuhan 430079, China

Abstract. In the object oriented classification, object  information extracted from multi-source remote sensing imagery can be integrated to improve the accuracy and reliability of acquired remote sensing information. Before extraction information from multi-source image, the objects should be geometrically located on them. The geometrical location of objects mainly consists  of three steps: the extraction and representation of objects, the pre-processing of different spatial resolution images and the transferring of objects. Each of these steps may lead to uncertainties. This paper investigates and analyzes the geometrical uncertainty of object and its influence on the extracted features and  the classification accuracy. Different transferring methods may cause different uncertainty and the uncertainty of boundary pixels is the main source of the geometrical uncertainty. In this paper, two transferring methods of the objects are used: transferring of the raster objects and transferring of the vector objects. To analyze its influence on the extracted features, spectral features, NDVI (normalized difference vegetation index) and energy are calculated in two feature extraction methods for comparison. One method is based on all the object pixels and the other is based on the inside pixels without the boundary pixels. To analyze its influence on the classification accuracy, the extracted features are used to classify the objects with Bayes maximum likehood classifier. The results show that the influence of the geometrical uncertainty is small and inconstant. To avoid the instability caused by the geometrical uncertainty, the method of transferring the raster object without considering the influence of the boundary pixels may be more suitable in practice.

Keywords: object-oriented, classification, feature, uncertainty, geometrical location 

In: Wan, Y. et al. (eds) Proceeding of the 8th international symposium on spatial accuracy assessment in natural resources and environmental sciences, World Academic Union (Press).

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