Feature Balance for Fine-Grained Object Classification in Aerial Images

Zhao, Wenda; Tong, Tingting; Yao, Libo; Liu, Yu; Xu, Congan; He, You; Lu, Huchuan

doi:10.1109/tgrs.2022.3161433

Cited by 14 publications

(2 citation statements)

References 93 publications

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“…T2FTS [38] solves the long-tail recognition problem in remote sensing images using a hierarchical distillation framework. FBNet [39] uses a feature-balancing strategy to strengthen the representation of weak details and refine local features through an iterative interaction mechanism, addressing the problem of fuzzy or missing target details. PMG [36] and LGFFE [40] have been proposed for fine-grained classification tasks under weak supervision, providing more discriminant features through multi-level feature learning.…”

Section: Comparisons With Other Methodsmentioning

confidence: 99%

Contrastive Learning Network Based on Causal Attention for Fine-Grained Ship Classification in Remote Sensing Scenarios

Pan

et al. 2023

Remote Sensing

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Fine-grained classification of ship targets is an important task in remote sensing, having numerous applications in military reconnaissance and sea surveillance. Due to the influence of various imaging factors, ship targets in remote sensing images have considerable inter-class similarity and intra-class difference, which brings significant challenges to fine-grained classification. In response, we developed a contrastive learning network based on causal attention (C2Net) to improve the model’s fine-grained identification ability from local details. The asynchronous feature learning mode of “decoupling + aggregation” is adopted to reduce the mutual influence between local features and improve the quality of local features. In the decoupling stage, the feature vectors of each part of the ship targets are de-correlated using a decoupling function to prevent feature adhesion. Considering the possibility of false associations between results and features, the decoupled part is designed based on the counterfactual causal attention network to enhance the model’s predictive logic. In the aggregation stage, the local attention weight learned in the decoupling stage is used to carry out feature fusion on the trunk feature weight. Then, the proposed feature re-association module is used to re-associate and integrate the target local information contained in the fusion feature to obtain the target feature vector. Finally, the aggregation function is used to complete the clustering process of the target feature vectors and fine-grained classification is realized. Using two large-scale datasets, the experimental results show that the proposed C2Net method had better fine-grained classification than other methods.

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Section: Comparisons With Other Methodsmentioning

confidence: 99%

Contrastive Learning Network Based on Causal Attention for Fine-Grained Ship Classification in Remote Sensing Scenarios

Pan

et al. 2023

Remote Sensing

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“…Liu et al [11] improved the inceptionV3 [12] network, and the network achieved better results in classifying obscured ship targets. Zhao et al [13] proposed a feature balancing strategy and, at the same time, using an iterative interaction mechanism that could effectively enhance the model classification effect generation by generation. Zhang et al [14] proposed the AMEFRN model and designed an attribute feature learning branch supervised by attribute information to enhance the model learning ability.…”

Section: Background and Motivationmentioning

confidence: 99%

Fine-Grained Classification of Remote Sensing Ship Images Based on Improved VAN

Zhou,

Huang,

Sun

2023

CMC

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The remote sensing ships' fine-grained classification technology makes it possible to identify certain ship types in remote sensing images, and it has broad application prospects in civil and military fields. However, the current model does not examine the properties of ship targets in remote sensing images with mixed multi-granularity features and a complicated backdrop. There is still an opportunity for future enhancement of the classification impact. To solve the challenges brought by the above characteristics, this paper proposes a Metaformer and Residual fusion network based on Visual Attention Network (VAN-MR) for fine-grained classification tasks. For the complex background of remote sensing images, the VAN-MR model adopts the parallel structure of large kernel attention and spatial attention to enhance the model's feature extraction ability of interest targets and improve the classification performance of remote sensing ship targets. For the problem of multi-grained feature mixing in remote sensing images, the VAN-MR model uses a Metaformer structure and a parallel network of residual modules to extract ship features. The parallel network has different depths, considering both high-level and lowlevel semantic information. The model achieves better classification performance in remote sensing ship images with multi-granularity mixing. Finally, the model achieves 88.73% and 94.56% accuracy on the public fine-grained ship collection-23 (FGSC-23) and FGSCR-42 datasets, respectively, while the parameter size is only 53.47 M, the floating point operations is 9.9 G. The experimental results show that the classification effect of VAN-MR is superior to that of traditional CNNs model and visual model with Transformer structure under the same parameter quantity.

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