A DSM-Based Co-Occurrence Matrix for Semantic Classification

Traditional 2D textures cannot reflect objects’ real textures in 3D world, since they only consider spectral distribution in a 2D region which is a projection of 3D objects at a certain angle of view. The existing researches of 3D textures can only process volumetric data (VD) like multi/hyper-spectral images which are not real 3D geometric data. In this letter, we proposed a digital surface model (DSM)-based co-occurrence matrix (DSMB-CM) which extended 2D co-occurrence matrix (2D-CM) to 3D space for multispectral images with DSM. DSMB-CM is the first 3D feature in remote sensing areas considering the spectral distribution over 3D surface to represent real textures of objects in 3D space. Besides, a dimension reduction method was proposed to avoid curse of dimensionality. Experiments compared classification accuracies of different feature combinations of two data sets from ISPRS Benchmark of Semantic Labeling Contest. The results proved that DSMB-CM had better performance than traditional textures in identification of all categories.