Remote Sensing Scene Classification Based on Attention-Enabled Progressively Searching

Shen, Junge; Cao, Bin; Zhang, Chi; Wang, Ruxin; Wang, Qi

doi:10.1109/tgrs.2022.3186588

Cited by 9 publications

(8 citation statements)

References 41 publications

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“…LGRINet (Xu et al, 2022a(Xu et al, , 2022b(Xu et al, , 2022c(Xu et al, , 2022d 4 (Shen et al, 2022a) 3.8 95.32 (TR-60%) TST-Net (Chen et al, 2018) KD 1.0 80.00(TR-60%) ESD-MBENet (Zhao et al, 2022) 23.9 93.05 6 0.18 95.36 6 0.14 ET-GSNet (Xu et al, 2022a(Xu et al, , 2022b(Xu et al, , 2022c(Xu et al, , 2022d (Bi et al, 2022) Feature refining >12.5 93.04 6 0.35 94.91 6 0.17 MGS-Net (Guo et al, 2022) 244.2 91.92 6 0.12 94.33 6 0.08 GSCCTL-Net (Song and Yang, 2022) None 91.96 None ViT-Huge (Bazi et al, 2021) Single ViT 86 93.83 6 0.46 None ViT-AEv2 (Wang et al, 2023) 18.8 94.41 6 0.11 95.60 6 0.06 SC-ViT (Lv et al, 2022) 40.1 92.72 6 0.04 94.66 6 0.10 DFAGC-Net (Xu et al, 2022a(Xu et al, , 2022b(Xu et al, , 2022c(Xu et al, , 2022d Multiple models None None 89.29 6 0.28 GRMA-Net (Li et al, 2022) 54.1 93.67 6 0.21 95.32 6 0.28 ACNet (Tang et al, 2021) >276.6 91.09 6 0.13 92.42 6 0.16 T-CNN (Wang et al, 2022b) 15.9 90.25 6 0.14 93.05 6 0.12 GLDBS-Net (Xu et al, 2022a(Xu et al, , 2022b(Xu et al, , 2022c…”

Section: Efficient Knowledge Distillationmentioning

confidence: 99%

“…Therefore, applying NAS to RSI data sets could yield more efficient deep models that leverage general RSI features. However, existing NAS studies (Ao et al , 2023; Broni-Bediako et al , 2022; Shen et al , 2022a) have only validated their models on RSI data sets significantly smaller than ImageNet-1K. Consequently, these NAS-based models have demonstrated smaller volumes without achieving competitive accuracy.…”

Section: Related Workmentioning

confidence: 99%

“…Alternatively, custom-built CNN architectures developed through neural architecture search (NAS) on RSI data sets may offer improved solutions. For instance, Ao et al (2023), Broni-Bediako et al (2022) and Shen et al (2022a) proposed their NAS-based CNNs and demonstrated much smaller volumes than preexisting models. However, these NAS-based methods still achieved significantly lower accuracy because the NAS was only performed on RSI data sets with several tens of thousands of data points.…”

Section: Introductionmentioning

confidence: 99%

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Efficient knowledge distillation for remote sensing image classification: a CNN-based approach

Song,

Wei,

Yong

2023

IJWIS

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Purpose The paper aims to tackle the classification of Remote Sensing Images (RSIs), which presents a significant challenge for computer algorithms due to the inherent characteristics of clustered ground objects and noisy backgrounds. Recent research typically leverages larger volume models to achieve advanced performance. However, the operating environments of remote sensing commonly cannot provide unconstrained computational and storage resources. It requires lightweight algorithms with exceptional generalization capabilities. Design/methodology/approach This study introduces an efficient knowledge distillation (KD) method to build a lightweight yet precise convolutional neural network (CNN) classifier. This method also aims to substantially decrease the training time expenses commonly linked with traditional KD techniques. This approach entails extensive alterations to both the model training framework and the distillation process, each tailored to the unique characteristics of RSIs. In particular, this study establishes a robust ensemble teacher by independently training two CNN models using a customized, efficient training algorithm. Following this, this study modifies a KD loss function to mitigate the suppression of non-target category predictions, which are essential for capturing the inter- and intra-similarity of RSIs. Findings This study validated the student model, termed KD-enhanced network (KDE-Net), obtained through the KD process on three benchmark RSI data sets. The KDE-Net surpasses 42 other state-of-the-art methods in the literature published from 2020 to 2023. Compared to the top-ranked method’s performance on the challenging NWPU45 data set, KDE-Net demonstrated a noticeable 0.4% increase in overall accuracy with a significant 88% reduction in parameters. Meanwhile, this study’s reformed KD framework significantly enhances the knowledge transfer speed by at least three times. Originality/value This study illustrates that the logit-based KD technique can effectively develop lightweight CNN classifiers for RSI classification without substantial sacrifices in computation and storage costs. Compared to neural architecture search or other methods aiming to provide lightweight solutions, this study’s KDE-Net, based on the inherent characteristics of RSIs, is currently more efficient in constructing accurate yet lightweight classifiers for RSI classification.

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Section: Efficient Knowledge Distillationmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Efficient knowledge distillation for remote sensing image classification: a CNN-based approach

Song,

Wei,

Yong

2023

IJWIS

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“…Among these deep learning based methods, CNNs are the most commonly-utilized [2], [18]- [21], [44] as the convolutional filters are effective to extract multi-level features from the image. In the past two years, CNN based methods (e.g., DSENet [45], MS2AP [46], MSDFF [47], CADNet [48], LSENet [5], GBNet [49], MBLANet [50], MG-CAP [51], Contourlet CNN [52], STHP [53], SAGM [54], DARTS [55], LML [56], GCSANet [57]) still remain heated for aerial scene classification. On the other hand, recurrent neural network (RNN) based [25], auto-encoder based [58], [59] and generative adversarial network (GAN) based [60], [61] approaches have also been reported effective for aerial scene classification.…”

Section: A Aerial Scene Classificationmentioning

confidence: 99%

“…We compare the performance of our AGOS with three handcrafted features (PLSA, BOW, LDA) [17], [87], three typical CNN models (AlexNet, VGG, GoogLeNet) [17], [87], twentytwo latest CNN-based state-of-the-art approaches (MIDCNet [2], RANet [29], APNet [88], SPPNet [20], DCNN [28], TEXNet [89], MSCP [18], VGG+FV [21], DSENet [45], MS2AP [46], MSDFF [47], CADNet [48], LSENet [5], GBNet [49], MBLANet [50], MG-CAP [51], Contourlet CNN [52], STHP [53], SAGM [54], DARTS [55], LML [56], GCSANet [57]), one RNN-based approach (ARCNet [25]), two autoencoder based approaches (SGUFL [59], PARTLETS [58]) and two GAN-based approaches (MARTA [60], AGAN [61]) respectively. The performance under the backbone of ResNet-50, ResNet-101 and DenseNet-121 is all reported for fair evaluation as some latest methods [47], [48] use much deeper networks as backbone.…”

Section: Comparison With State-of-the-art Approachesmentioning

confidence: 99%

All Grains, One Scheme (AGOS): Learning Multigrain Instance Representation for Aerial Scene Classification

Zhou

Qin

et al. 2022

IEEE Trans. Geosci. Remote Sensing

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Aerial scene classification remains challenging as: 1) the size of key objects in determining the scene scheme varies greatly; 2) many objects irrelevant to the scene scheme are often flooded in the image. Hence, how to effectively perceive the region of interests (RoIs) from a variety of sizes and build more discriminative representation from such complicated object distribution is vital to understand an aerial scene. In this paper, we propose a novel all grains, one scheme (AGOS) framework to tackle these challenges. To the best of our knowledge, it is the first work to extend the classic multiple instance learning into multi-grain formulation. Specially, it consists of a multigrain perception module (MGP), a multi-branch multi-instance representation module (MBMIR) and a self-aligned semantic fusion (SSF) module. Firstly, our MGP preserves the differential dilated convolutional features from the backbone, which magnifies the discriminative information from multi-grains. Then, our MBMIR highlights the key instances in the multi-grain representation under the MIL formulation. Finally, our SSF allows our framework to learn the same scene scheme from multi-grain instance representations and fuses them, so that the entire framework is optimized as a whole. Notably, our AGOS is flexible and can be easily adapted to existing CNNs in a plug-andplay manner. Extensive experiments on UCM, AID and NWPU benchmarks demonstrate that our AGOS achieves a comparable performance against the state-of-the-art methods.

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An evidential combination method with multi-color spaces for remote sensing image scene classification

Huang

Zhao

Liew³

et al. 2023

Information Fusion

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Remote Sensing Scene Classification Based on Attention-Enabled Progressively Searching

Cited by 9 publications

References 41 publications

Efficient knowledge distillation for remote sensing image classification: a CNN-based approach

Efficient knowledge distillation for remote sensing image classification: a CNN-based approach

All Grains, One Scheme (AGOS): Learning Multigrain Instance Representation for Aerial Scene Classification

An evidential combination method with multi-color spaces for remote sensing image scene classification

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