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An assessment of geometric activities features for classification of urban man-made objects using meter resolution imagery This publication appears in: Photogrammetric Engineering and Remote Sensing Authors: J. C-W Chan, R. Bellens, F. Canters and S. Gautama Volume: 75 Issue: 4 Pages: 397-412 Publication Date: Apr. 2009
Abstract: In this paper we propose the use of Geometric Activity (GA) features for detecting man-made objects in urban areas using VHR satellite imagery. These features describe the geometric context of a pixel without the necessity of segmentation and can be integrated as extra bands in a per-pixel classification. Two main types of GA features were investigated: ridge features based on the well-known facet model and morphological features obtained by applying closing transforms with structuring elements of different size and shape. Our findings show a substantial increase in classification accuracy for the man-made object classes roads and buildings with dark roof after inclusion of GA features. Next to GA features, the use of object-based features derived from eCognition®, containing both geometric and textural information, was also investigated for per-pixel classification. Accuracies obtained with object-based features are comparable to the accuracies obtained with GA features. The inclusion of both GA features and object-based features further improves the overall accuracy. GA features and object-based features thus contain complementary information.
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