A Lightweight Convolutional Neural Network Based on Hierarchical-Wise Convolution Fusion for Remote-Sensing Scene Image Classification

Shi, Cuiping; Zhang, Xinlei; Wang, Tianyi; Wang, Liguo

doi:10.3390/rs14133184

Cited by 10 publications

(2 citation statements)

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“…First, researchers proposed different fusion strategies for features extracted from selfdesigned modules. E.g., Shi et al (2022b) proposed a CNN method using a hierarchical-wised module for feature fusion. Bai et al (2022) proposed a CNN method using a so-called octave convolution module to extract multi-frequency features for fusion.…”

Section: Related Workmentioning

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%

“…First, researchers proposed different fusion strategies for features extracted from self-designed modules. E.g., Shi et al. (2022b) proposed a CNN method using a hierarchical-wised module for feature fusion.…”

Section: Related Workmentioning

confidence: 99%

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

Song

2023

IJICC

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Convolution neural network and deep learning

Mustapha,

Ozsahin,

Ozsahin

2024

Artificial Intelligence and Image Processing in Medical Imaging

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HDTFF-Net: Hierarchical Deep Texture Features Fusion Network for High-Resolution Remote Sensing Scene Classification

Song

Cong

Zhang

et al. 2023

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Fusing features from different feature descriptors or different convolutional layers can improve the understanding of scene and enhance the classification accuracy. In this article, we propose a hierarchical deep texture feature fusion network, abbreviated as HDTFF-Net, aiming to improve the classification accuracy of high-resolution remote sensing scene classification. The proposed HDTFF-Net can effectively combine the shallow texture information from manual features and the deep texture information by convolutional neural networks (CNNs). First, for deeply excavating the multiscale and multidirectional shallow texture features in images, an improved Wavelet feature extraction module and a Gabor feature extraction module are designed by fully fusing the structural features into the backbone neural network. Then, to make the output texture features more discriminative and interpretative, we incorporate the above texture feature extraction modules into traditional CNNs (Tra-CNNs), and design two improved deep networks, namely Wave-CNN and Gabor-CNN. Finally, according to the Dempster-Shafer evidence theory, the designed Wave-CNN and Gabor-CNN are fused with the Tra-CNN by a decision-level fusion strategy, which can effectively capture the deep texture features by different feature descriptors and improve the classification performance. Experiments on high-resolution remote sensing images demonstrate the effectiveness of the proposed HDTFF-Net, and verify that it can greatly improve the classification performance.

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A Lightweight Convolutional Neural Network Based on Hierarchical-Wise Convolution Fusion for Remote-Sensing Scene Image Classification

Cited by 10 publications

References 62 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

Convolution neural network and deep learning

HDTFF-Net: Hierarchical Deep Texture Features Fusion Network for High-Resolution Remote Sensing Scene Classification

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