ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., II-3/W1, 35-40, 2013
© Author(s) 2013. This work is distributed
under the Creative Commons Attribution 3.0 License.
16 May 2013
Institute of Geodesy and Photogrammetry, ETH Zürich, Zurich, Switzerland
Keywords: Classification, land cover, feature extraction, pattern recognition Abstract. A basic problem of image classification in remote sensing is to select suitable image features. However, modern classifiers such as AdaBoost allow for feature selection driven by the training data. This capability brings up the question whether hand-crafted features are required or whether it would not be enough to extract the same quasi-exhaustive feature set for different classification problems and let the classifier choose a suitable subset for the specific image statistics of the given problem. To be able to efficiently extract a large quasi-exhaustive set of multi-scale texture and intensity features we suggest to approximate standard derivative filters via integral images. We compare our quasi-exhaustive features to several standard feature sets on four very high-resolution (VHR) aerial and satellite datasets of urban areas. We show that in combination with a boosting classifier the proposed quasi-exhaustive features outperform standard baselines.
Received: 08 Mar 2013 – Revised: 03 May 2013 – Accepted: 04 May 2013 – Published: 16 May 2013