MFST: A Multi-Level Fusion Network for Remote Sensing Scene Classification

Wang, Guoqing; Zhang, Ning; Liu, Wenchao; Chen, He; Xie, Yizhuang

doi:10.1109/lgrs.2022.3205417

Cited by 26 publications

(10 citation statements)

References 15 publications

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“…Bidirectional Adaptive Feature Fusion [36] 2019 93.56 Feature Aggregation CNN [37] 2019 95.45 Aggregated Deep Fisher Feature [38] 2019 95.26 Skip-connected covariance network [39] 2019 93.30 EfficientNet [40] 2020 88.35 InceptionV3 [41] 2020 95.07 Branch Feature Fusion [42] 2020 94.53 Gated Bidirectional Network with global feature [43] 2020 95.48 Deep Discriminative Representation Learning [44] 2020 94.08 Hierarchical Attention and Bilinear Fusion [45] 2020 96.75 VGG-VD16 with SAFF [46] 2021 95.98 EfficientNetB3-CNN [47] 2021 95.39 Multiscale attention network [48] 2021 96.76 Channel Multi-Group Fusion [49] 2021 97.54 Multiscale representation learning [50] 2022 96.01 Global-local dual-branch structure [51] 2022 97.01 Multilevel feature fusion networks [52] 2022 95.06 Multi-Level Fusion Network [53] 2022 97.38 MGSNet [54] 2023 97.18 BayesNet -97.57…”

Section: Methods Year Overall Accuracymentioning

confidence: 99%

“…Rotation invariant feature learning [61] 2019 91.03 Positional Context Aggregation [56] 2019 92.61 Feature Variable Significance Learning [57] 2019 89.13 Multi-Granualirty Canonical Appearance Pooling [62] 2020 91.72 EfficientNet [40] 2020 81.83 ResNet50 with transfer learning [41] 2020 88.93 MobileNet with tranfer learning [41] 2020 83.26 Branch Feature Fusion [42] 2020 91.73 Multi-Structure Deep features fusion [63] 2020 93.55 Coutourlet CNN [58] 2021 89.57 Channel Multi-Group Fusion [49] 2022 94.18 Multi-Level Fusion Network [53] 2022 94.90 MGSNet [54] 2023 94.57 BayesNet -95.44…”

Section: Methods Year Overall Accuracymentioning

confidence: 99%

See 1 more Smart Citation

BayesNet: Enhancing UAV-Based Remote Sensing Scene Understanding with Quantifiable Uncertainties

Sagar,

Tanveer,

Chen

et al. 2024

Remote Sensing

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Remote sensing stands as a fundamental technique in contemporary environmental monitoring, facilitating extensive data collection and offering invaluable insights into the dynamic nature of the Earth’s surface. The advent of deep learning, particularly convolutional neural networks (CNNs), has further revolutionized this domain by enhancing scene understanding. However, despite the advancements, traditional CNN methodologies face challenges such as overfitting in imbalanced datasets and a lack of precise uncertainty quantification, crucial for extracting meaningful insights and enhancing the precision of remote sensing techniques. Addressing these critical issues, this study introduces BayesNet, a Bayesian neural network (BNN)-driven CNN model designed to normalize and estimate uncertainties, particularly aleatoric and epistemic, in remote sensing datasets. BayesNet integrates a novel channel–spatial attention module to refine feature extraction processes in remote sensing imagery, thereby ensuring a robust analysis of complex scenes. BayesNet was trained on four widely recognized unmanned aerial vehicle (UAV)-based remote sensing datasets, UCM21, RSSCN7, AID, and NWPU, and demonstrated good performance, achieving accuracies of 99.99%, 97.30%, 97.57%, and 95.44%, respectively. Notably, it has showcased superior performance over existing models in the AID, NWPU, and UCM21 datasets, with enhancements of 0.03%, 0.54%, and 0.23%, respectively. This improvement is significant in the context of complex scene classification of remote sensing images, where even slight improvements mark substantial progress against complex and highly optimized benchmarks. Moreover, a self-prepared remote sensing testing dataset is also introduced to test BayesNet against unseen data, and it achieved an accuracy of 96.39%, which showcases the effectiveness of the BayesNet in scene classification tasks.

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Section: Methods Year Overall Accuracymentioning

confidence: 99%

Section: Methods Year Overall Accuracymentioning

confidence: 99%

BayesNet: Enhancing UAV-Based Remote Sensing Scene Understanding with Quantifiable Uncertainties

Sagar,

Tanveer,

Chen

et al. 2024

Remote Sensing

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“…As a special subfield of computer vision, remote sensing scene classification has also been pushed forward with a big step by DCNNs and many successfully works have been proposed. In [ 30 , 32 , 47 , 48 , 49 ], Zhang et al extracted a representative set of patches from the salient regions in original image data set, then the patch set is feed into a sparse autoencoder to learn a set of feature extractors for scene classification. Based on pretrained network models on ImageNet [ 50 ], many DCNNs-based networks are designed for RSSC by fine-tuning on remote sensing image datasets.…”

Section: Related Workmentioning

confidence: 99%

Adaptive Discriminative Regions Learning Network for Remote Sensing Scene Classification

Tang

Zheng

Tang

2023

Sensors

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As an auxiliary means of remote sensing (RS) intelligent interpretation, remote sensing scene classification (RSSC) attracts considerable attention and its performance has been improved significantly by the popular deep convolutional neural networks (DCNNs). However, there are still several challenges that hinder the practical applications of RSSC, such as complex composition of land cover, scale-variation of objects, and redundant and noisy areas for scene classification. In order to mitigate the impact of these issues, we propose an adaptive discriminative regions learning network for RSSC, referred as ADRL-Net briefly, which locates discriminative regions effectively for boosting the performance of RSSC by utilizing a novel self-supervision mechanism. Our proposed ADRL-Net consists of three main modules, including a discriminative region generator, a region discriminator, and a region scorer. Specifically, the discriminative region generator first generates some candidate regions which could be informative for RSSC. Then, the region discriminator evaluates the regions generated by region generator and provides feedback for the generator to update the informative regions. Finally, the region scorer makes prediction scores for the whole image by using the discriminative regions. In such a manner, the three modules of ADRL-Net can cooperate with each other and focus on the most informative regions of an image and reduce the interference of redundant regions for final classification, which is robust to the complex scene composition, object scales, and irrelevant information. In order to validate the efficacy of the proposed network, we conduct experiments on four widely used benchmark datasets, and the experimental results demonstrate that ADRL-Net consistently outperforms other state-of-the-art RSSC methods.

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“…ET-GSNet [57] employs a vision transformer as a teacher to guide small networks for ASR. (2) MG-CAP [1], KFB [41], CNN-MS2AP [43], C-CNN [37], ACR-MLFF [58], MF 2 CNet [4] and SKAL-CNN [20] adopt multi-branch networks. MG-CAP [1] network introduces a multi-granularity canonical appearance pooling strategy for capturing the latent ontological structure of aerial scene images.…”

Section: Comparison With State Of the Artsmentioning

confidence: 99%

“…The C-CNN [37] network combines the contourlet transform with CNN to learn abundant information for ASR. The ACR-MLFF [58] network adopts the multilevel feature fusion network and adaptive channel dimensionality reduction mechanism for ASR. MF 2 CNet [4] proposes a multi-scale feature fusion covariance network to learn multi-scale and multi-frequency features to classify aerial scene images.…”

Section: Comparison With State Of the Artsmentioning

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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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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MFST: A Multi-Level Fusion Network for Remote Sensing Scene Classification

Cited by 26 publications

References 15 publications

BayesNet: Enhancing UAV-Based Remote Sensing Scene Understanding with Quantifiable Uncertainties

BayesNet: Enhancing UAV-Based Remote Sensing Scene Understanding with Quantifiable Uncertainties

Adaptive Discriminative Regions Learning Network for Remote Sensing Scene Classification

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

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