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This paper presents a novel approach to detect the buildings by automization of the training area collecting stage for supervisedclassification. The method based on the fact that a 3d building structure should cast a shadow under suitable imaging conditions.Therefore, the methodology begins with the detection and masking out the shadow areas using luminance component of the LABcolor space, which indicates the lightness of the image, and a novel double thresholding technique. Further, the training areas forsupervised classification are selected by automatically determining a buffer zone on each building whose shadow is detected by usingthe shadow shape and the sun illumination direction. Thereafter, by calculating the statistic values of each buffer zone which iscollected from the building areas the Improved Parallelepiped Supervised Classification is executed to detect the buildings. Standarddeviation thresholding applied to the Parallelepiped classification method to improve its accuracy. Finally, simple morphologicaloperations conducted for releasing the noises and increasing the accuracy of the results. The experiments were performed on set ofhigh resolution Google Earth images. The performance of the proposed approach was assessed by comparing the results of theproposed approach with the reference data by using well-known quality measurements (Precision, Recall and F1-score) to evaluatethe pixel-based and object-based performances of the proposed approach. Evaluation of the results illustrates that buildings detectedfrom dense and suburban districts with divers characteristics and color combinations using our proposed method have 88.4 % and853 % overall pixel-based and object-based precision performances, respectively

the international archives of the photogrammetry, remote sensing and spatial information sciences/international archives of the photogrammetry, remote sensing and spatial information sciences

Automatic Building Detection based on Supervised Classification using High Resolution Google Earth Images