Object detection in optical remote sensing images by integrating object-to-object relationships

ABSTRACT In recent years, deep-learning-based methods for remote sensing image interpretation have undergone rapid development, due to the increasing amount of image data and the advanced techniques of machine learning. The abundant spatial and contextual information within the images is helpful to improve the interpretation performance. However, the contextual information is ignored by most of the current deep-learning-based methods. In this letter, we explore the contextual information by taking advantage of the object-to-object relationship. Then, the feature representation of the individual objects can be enhanced. To be specific, we first build a knowledge database which reveals the relationship between different categories and generate a region-to-region graph that indicates the relationship between different regions of interest (RoIs). For each RoI, the features of its related regions are then combined with the original region features, and the fused features are finally used for object detection. The experiments conducted on a public ten-class object detection dataset demonstrate the validity of the proposed method.