CF2PN: A Cross-Scale Feature Fusion Pyramid Network Based Remote Sensing Target Detection

作者：Wei Huang, Guanyi Li, Qiqiang Chen, Ming Ju, Jiantao Qu · 发表于：Remote Sensing · 年份：2021 · DOI：10.3390/rs13050847 · 被引用次数：106 · 研究领域：Remote-Sensing Image Classification、Advanced Neural Network Applications、Advanced Image Fusion Techniques

In the wake of developments in remote sensing, the application of target detection of remote sensing is of increasing interest. Unfortunately, unlike natural image processing, remote sensing image processing involves dealing with large variations in object size, which poses a great challenge to researchers. Although traditional multi-scale detection networks have been successful in solving problems with such large variations, they still have certain limitations: (1) The traditional multi-scale detection methods note the scale of features but ignore the correlation between feature levels. Each feature map is represented by a single layer of the backbone network, and the extracted features are not comprehensive enough. For example, the SSD network uses the features extracted from the backbone network at different scales directly for detection, resulting in the loss of a large amount of contextual information. (2) These methods combine with inherent backbone classification networks to perform detection tasks. RetinaNet is just a combination of the ResNet-101 classification network and FPN network to perform the detection tasks; however, there are differences in object classification and detection tasks. To address these issues, a cross-scale feature fusion pyramid network (CF2PN) is proposed. First and foremost, a cross-scale fusion module (CSFM) is introduced to extract sufficiently comprehensive semantic information from features for performing multi-scale fusion. Moreover, a ...