Multi-scale attentive region adaptive aggregation learning for remote sensing scene classification

Lv, Guangrui; Dong, Lili; Zhang, Wenwen; Xu, Wenhai

doi:10.1080/01431161.2021.1963878

Cited by 4 publications

(2 citation statements)

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“…To improve feature discrimination capacity, Li et al [45] developed a feature fusion framework for integrating multi-scale features in which a multi-scale Fisher kernel coding method is designed to extract middle-level feature representations. Lv et al [46] proposed a multi-scale attentive region adaptive aggregation learning method, which can boost the semantic representation by combining cross-scale spatial semantic features.…”

Section: Deep Learning Feature-based Asr Methodsmentioning

confidence: 99%

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Scale‐wise interaction fusion and knowledge distillation network for aerial scene recognition

Ning

Lei

et al. 2023

CAAI Trans on Intel Tech

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Aerial scene recognition (ASR) has attracted great attention due to its increasingly essential applications. Most of the ASR methods adopt the multi‐scale architecture because both global and local features play great roles in ASR. However, the existing multi‐scale methods neglect the effective interactions among different scales and various spatial locations when fusing global and local features, leading to a limited ability to deal with challenges of large‐scale variation and complex background in aerial scene images. In addition, existing methods may suffer from poor generalisations due to millions of to‐be‐learnt parameters and inconsistent predictions between global and local features. To tackle these problems, this study proposes a scale‐wise interaction fusion and knowledge distillation (SIF‐KD) network for learning robust and discriminative features with scale‐invariance and background‐independent information. The main highlights of this study include two aspects. On the one hand, a global‐local features collaborative learning scheme is devised for extracting scale‐invariance features so as to tackle the large‐scale variation problem in aerial scene images. Specifically, a plug‐and‐play multi‐scale context attention fusion module is proposed for collaboratively fusing the context information between global and local features. On the other hand, a scale‐wise knowledge distillation scheme is proposed to produce more consistent predictions by distilling the predictive distribution between different scales during training. Comprehensive experimental results show the proposed SIF‐KD network achieves the best overall accuracy with 99.68%, 98.74% and 95.47% on the UCM, AID and NWPU‐RESISC45 datasets, respectively, compared with state of the arts.

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Section: Deep Learning Feature-based Asr Methodsmentioning

confidence: 99%

“…Lv et al. [46] proposed a multi‐scale attentive region adaptive aggregation learning method, which can boost the semantic representation by combining cross‐scale spatial semantic features.…”

Section: Related Workmentioning

confidence: 99%

Scale‐wise interaction fusion and knowledge distillation network for aerial scene recognition

Ning

Lei

et al. 2023

CAAI Trans on Intel Tech

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Mining Hierarchical Information of CNNs for Scene Classification of VHR Remote Sensing Images

Deng

Hong

2023

IEEE Trans. Big Data

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EFCOMFF-Net: A Multiscale Feature Fusion Architecture With Enhanced Feature Correlation for Remote Sensing Image Scene Classification

Chen

et al. 2023

IEEE Trans. Geosci. Remote Sensing

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Multi-scale attentive region adaptive aggregation learning for remote sensing scene classification

Cited by 4 publications

References 59 publications

Scale‐wise interaction fusion and knowledge distillation network for aerial scene recognition

Scale‐wise interaction fusion and knowledge distillation network for aerial scene recognition

Mining Hierarchical Information of CNNs for Scene Classification of VHR Remote Sensing Images

EFCOMFF-Net: A Multiscale Feature Fusion Architecture With Enhanced Feature Correlation for Remote Sensing Image Scene Classification

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