Structural Feature Modeling of High-Resolution Remote Sensing Images Using Directional Spatial Correlation

In the classification of high-resolution remote sensing images, spatial correlations between pixel values are important spatial information. Traditional methods of measuring spatial correlation are inadequate for the extraction of spatial information about the shape and structure of object classes. In this letter, we propose a novel method using directional spatial correlation (DSC) to model and extract spatial information in neighborhoods of pixels. Two sets of descriptors DSC_I and DSC_C are defined to describe spatial structural features. The effectiveness of the proposed method was tested by image classification on two data sets. Results show that the DSC-based approach can drastically improve the classification, and it is found by comparison that it has better performance than some existing methods.