Angular difference feature extraction for urban scene classification using ZY 3 multi angle high resolution satellite imagery
Abstract Spaceborne multi angle images with a high resolution are capable of simultaneously providing spatial details and three dimensional (3D) information to support detailed and accurate classification of complex urban scenes. In recent years, satellite derived digital surface models (DSMs) have been increasingly utilized to provide height information to complement spectral properties for urban classification. However, in such a way, the multi angle information is not effectively exploited, which is mainly due to the errors and difficulties of the multi view image matching and the inaccuracy of the generated DSM over complex and dense urban scenes. Therefore, it is still a challenging task to effectively exploit the available angular information from high resolution multi angle images. In this paper, we investigate the potential for classifying urban scenes based on local angular properties characterized from high resolution ZY 3 multi view images. Specifically, three categories of angular difference features (ADFs) are proposed to describe the angular information at three levels (i.e. pixel, feature, and label levels) (1) ADF pixel: the angular information is directly extrapolated by pixel comparison between the multi angle images; (2) ADF feature: the angular differences are described in the feature domains by comparing the differences between the multi angle spatial features (e.g. morphological attribute profiles (APs) (3) ADF label: label level angular features are proposed based on a group of urban primitives (e.g. buildings and shadows) in order to describe the specific angular information related to the types of primitive classes. In addition, we utilize spatial contextual information to refine the multi level ADF features using superpixel segmentation, for the purpose of alleviating the effects of salt and pepper noise and representing the main angular characteristics within a local area. The experiments on ZY 3 multi angle images confirm that the proposed ADF features can effectively improve the accuracy of urban scene classification, with a significant increase in overall accuracy (3.8 11.7% compared to using the spectral bands alone. Furthermore, the results indicated the superiority of the proposed ADFs in distinguishing between the spectrally similar and complex man made classes, including roads and various types of buildings (e.g. high buildings, urban villages, and residential apartments)