Non-locally Enhanced Feature Fusion Network for Aircraft Recognition in Remote Sensing Images

Abstract

Aircraft recognition has great application value, but aircraft in remote sensing images have some problems such as low resolution, poor contrasts, poor sharpness, and lack of details caused by the vertical view, which make the aircraft recognition very difficult. Especially when there are many kinds of aircraft and the differences between aircraft are subtle, the fine-grained recognition of aircraft is more challenging. In this paper, we propose a non-locally enhanced feature fusion network(NLFFNet) and attempt to make full use of the features from discriminative parts of aircraft. First, according to the long-distance self-correlation in aircraft images, we adopt non-locally enhanced operation and guide the network to pay more attention to the discriminating areas and enhance the features beneficial to classification. Second, we propose a part-level feature fusion mechanism(PFF), which crops 5 parts of the aircraft on the shared feature maps, then extracts the subtle features inside the parts through the part full connection layer(PFC) and fuses the features of these parts together through the combined full connection layer(CFC). In addition, by adopting the improved loss function, we can enhance the weight of hard examples in the loss function meanwhile reducing the weight of excessively hard examples, which improves the overall recognition ability of the network. The dataset includes 47 categories of aircraft, including many aircraft of the same family with slight differences in appearance, and our method can achieve 89.12% accuracy on the test dataset, which proves the effectiveness of our method.

RAIH-Det: An End-to-End Rotated Aircraft and Aircraft Head Detector Based on ConvNeXt and Cyclical Focal Loss in Optical Remote Sensing Images

“…In nature scenes, most existing target head detection methods also directly treat head detection as a specific form of object detection. However, there is very little theory and research on target head detection in optical remote sensing images, with only a few studies on different scene tasks [4,22,23]. For example, to perform aircraft head detection, Jia et al [4] clipped the recognized aircraft and pre-trained a head prediction module using a classification network.…”

Multiple Instance Learning Convolutional Neural Networks for Fine-Grained Aircraft Recognition

“…For example, K. Fu et al [20] proposed multiple class activation mapping to locate discriminative features and recognize aircraft types without part information. Yunsheng Xiong et al [21] proposed a non-locally enhanced feature fusion network to cultivate holistic representations and highlight part responses. The above approaches demand the complex design of locating networks to encode subcategory object features, which occupy many model parameters and consume much reasoning time.…”