An Assessment of Geometric Activity Features for Per-pixel Classification of Urban Man-made Objects using Very High Resolution Satellite Imagery

Abstract

In this paper, the authors 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. The 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.