Automatic house detection from high-resolution satellite imagery

Yoriko Kazama; Tao Guo

doi:10.1117/12.829721

28 September 2009 Automatic house detection from high-resolution satellite imagery

Yoriko Kazama, Tao Guo

Proceedings Volume 7477, Image and Signal Processing for Remote Sensing XV; 747710 (2009) https://doi.org/10.1117/12.829721
Event: SPIE Remote Sensing, 2009, Berlin, Germany

Abstract

We have developed a house detection method based on machine learning for classification of houses and non-houses. In order to achieve precise classification, it is important to select features and to determine a dimensionality reduction method and a learning method. We first applied Gabor wavelet filters to generate the feature vectors and then developed a new method using the Adaboost algorithm to reduce the dimensionality of feature space. If a linear classifier made by one element of a feature vector is considered as a weak classifier in Adaboost, higher contribution dimensions can be selected. We used support vector machines (SVM) for the learning method. We evaluated our method by using QuickBird panchromatic images. Despite the significant variations in house shape and rooftop color, and in background clutter, our algorithm achieved high accuracy in house detection.

Citation Download Citation

Yoriko Kazama and Tao Guo "Automatic house detection from high-resolution satellite imagery", Proc. SPIE 7477, Image and Signal Processing for Remote Sensing XV, 747710 (28 September 2009); https://doi.org/10.1117/12.829721

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