Remote sensing object detection with feature-associated convolutional neural networks - Rao, Jianghao; Wu, Tao; Li, Hongyun; Zhang, Jianlin; Bao, Qiliang; Peng, Zhenming (Frontiers in Earth Science, 2024)
Neural networks have become integral to remote sensing data processing. Among these, Convolutional Neural Networks (CNNs) in deep learning offer numerous advanced algorithms for object detection in remote sensing imagery, which is pivotal in both military and civilian contexts. CNNs excel in extracting features from training samples. However, traditional CNN models often lack specific signal assumptions tailored to remote sensing data at the feature level. In this paper, we propose a novel approach aimed at effectively representing and correlating information within CNNs for remote sensing object detection. We introduce object tokens and incorporate global information features in embedding layers, facilitating the comprehensive utilization of features across multiple hierarchical levels. Thinking of feature maps from images as two-dimensional signal or matrixes image signal processing is employed to correlate features for diverse representations within the CNN framework. Moreover, hierarchical feature signals are effectively represented and associated during end-to-end network training. Experiments on various datasets demonstrate that the CNN model