Consistency- and dependence-guided knowledge distillation for object detection in remote sensing images

Chen, Yixia; Lin, Mingwei; Zhu, He; Polat, Kemal; Alhudhaif, Adi; Alenezi, Fayadh

doi:10.1016/j.eswa.2023.120519

Cited by 33 publications

(8 citation statements)

References 37 publications

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“…To find effective roadmaps, different learning paradigms are being developed presently, such as self-supervised learning (Wang et al. , 2022c), knowledge distillation (Chen et al. , 2023; Luo et al.…”

Section: Related Workmentioning

confidence: 99%

“…Efficient training is very important for deep models. To find effective roadmaps, different learning paradigms are being developed presently, such as self-supervised learning (Wang et al, 2022c), knowledge distillation (Chen et al, 2023;Luo et al, 2023) and so on. As applications for RSI-SC, Xu et al (2022a) and Bi et al (2023) Based on all the aforementioned ideas, this work proposes the MBC-Net for RSI-SC.…”

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…Remote sensing images (RSI) are the key archives of human Earth observation, providing important survey proofs about the environment, agriculture, economy and so on. As the onboard sensor blooms, the rapid expansion of monitoring data has made a computer algorithm indispensable for RSI recognition (Chen et al, 2023;Kochupillai et al, 2022;Zhang et al, 2022a;Wijata et al, 2023). With the breakthroughs in machine learning, deep learning (DL) methods have dominated the recognition tasks of RSI, resulting in a lot of applications based on DL.…”

Section: Introductionmentioning

confidence: 99%

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MBC-Net: long-range enhanced feature fusion for classifying remote sensing images

Song

2023

IJICC

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PurposeClassification of remote sensing images (RSI) is a challenging task in computer vision. Recently, researchers have proposed a variety of creative methods for automatic recognition of RSI, and feature fusion is a research hotspot for its great potential to boost performance. However, RSI has a unique imaging condition and cluttered scenes with complicated backgrounds. This larger difference from nature images has made the previous feature fusion methods present insignificant performance improvements.Design/methodology/approachThis work proposed a two-convolutional neural network (CNN) fusion method named main and branch CNN fusion network (MBC-Net) as an improved solution for classifying RSI. In detail, the MBC-Net employs an EfficientNet-B3 as its main CNN stream and an EfficientNet-B0 as a branch, named MC-B3 and BC-B0, respectively. In particular, MBC-Net includes a long-range derivation (LRD) module, which is specially designed to learn the dependence of different features. Meanwhile, MBC-Net also uses some unique ideas to tackle the problems coming from the two-CNN fusion and the inherent nature of RSI.FindingsExtensive experiments on three RSI sets prove that MBC-Net outperforms the other 38 state-of-the-art (STOA) methods published from 2020 to 2023, with a noticeable increase in overall accuracy (OA) values. MBC-Net not only presents a 0.7% increased OA value on the most confusing NWPU set but also has 62% fewer parameters compared to the leading approach that ranks first in the literature.Originality/valueMBC-Net is a more effective and efficient feature fusion approach compared to other STOA methods in the literature. Given the visualizations of grad class activation mapping (Grad-CAM), it reveals that MBC-Net can learn the long-range dependence of features that a single CNN cannot. Based on the tendency stochastic neighbor embedding (t-SNE) results, it demonstrates that the feature representation of MBC-Net is more effective than other methods. In addition, the ablation tests indicate that MBC-Net is effective and efficient for fusing features from two CNNs.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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MBC-Net: long-range enhanced feature fusion for classifying remote sensing images

Song

2023

IJICC

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“…These advancements can be broadly categorized into two primary domains: traditional machine learning methodologies 12 and deep learning techniques, 13 both of which have made significant contributions to the successful detection of these tasks. Prior to this, these methods have also been widely used in other related fields, such as storage systems, 14 computer vision, 15 medical image processing, 16 data mining, 17 intelligent transportation systems, 18 network security, 19 etc.…”

Section: Introductionmentioning

confidence: 99%

A deep learning method for multi-task intelligent detection of oral cancer based on optical fiber Raman spectroscopy

Li,

Yu,

et al. 2024

Anal. Methods

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A deep learning integrated framework for predicting stock index price and fluctuation via singular spectrum analysis and particle swarm optimization

Wang,

Yuan,

Zeng

et al. 2024

Appl Intell

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Consistency- and dependence-guided knowledge distillation for object detection in remote sensing images

Cited by 33 publications

References 37 publications

MBC-Net: long-range enhanced feature fusion for classifying remote sensing images

MBC-Net: long-range enhanced feature fusion for classifying remote sensing images

A deep learning method for multi-task intelligent detection of oral cancer based on optical fiber Raman spectroscopy

A deep learning integrated framework for predicting stock index price and fluctuation via singular spectrum analysis and particle swarm optimization

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