Improved Harris feature point set for orientation sensitive urban area detection in aerial images

As manual administration is time consuming and unfeasible, researchers have to focus on automated processing techniques, which can handle various image characteristics and huge amount of data.
The applied method extracts feature points in the first step [Fig. (a)], which is followed by the construction of a voting map to represent urban areas [Fig. (b)]. Finally, an adaptive decision-making is performed to find urban areas [Figs. (c) and (d)]. We present methodological contributions in two key issues to the algorithm: (1) An automatically extracted, Harris based feature point set is introduced for the first step, which is able to represent urban areas more precisely [Fig. (a)]. (2) An improved, orientation-sensitive voting technique is proposed, exploiting the orientation information calculated in the local neighborhood of points. Evaluation results show that the proposed contributions increase the detection accuracy of urban areas. Fig. (c) shows detection without orientation information, Fig. (d) shows the introduced, orientation-sensitive detection.


Further details:
A. Kovacs and T. Sziranyi, "Improved Harris Feature Point Set for Orientation Sensitive Urban Area Detection in Aerial Images", IEEE Geoscience and Remote Sensing Letters, vol. 10, no. 4, pp. 796-800, 2013, IF:1.809

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