Invariant Structure Representation for Remote Sensing Object Detection Based on Graph Modeling
Zicong Zhu, Xian Sun, Wenhui Diao, Kaiqiang Chen, Guangluan Xu, Kun Fu
Abstract
Due to the characteristics of vertical orthophoto imaging, the apparent structural features of the object in the remote sensing image are relatively stable, such as the cross-shaped structure of the aircraft, the rectangular structure of the vehicle, etc. Compared with the traditional visual features, using these features is conducive to improving the accuracy of object detection. However, there are few studies on such characteristics. In this paper, we systematically study the invariant structural features of remote sensing objects and propose a Graph Focusing Aggregation Network (GFA-Net) to represent the structural features of remote sensing objects. Among them, in view of the problem that traditional convolutional neural networks (CNNs) are sensitive to the changes in rotation, scale, and other factors, which makes it difficult to extract structural features, we propose the Graph Focusing Process (GFP) based on the idea of graph convolution. Analysis and experiments show that graph structure has significant advantages over Euclidean feature space under CNN in expressing such structural features. In order to realize the end-to-end efficient training of the above model, we design Graph Aggregation Network (GAN) to update the weight of nodes. We verify the effectiveness of our method on the proposed multi-task datasets ACSD and large-scale fine-grained remote sensing dataset FAIR1M. Experiments conducted on the object detection data sets of DOTA and HRSC2016 prove that the proposed method is superior to the current state-of-the-art method.