Efficient recurrent attention network for remote sensing scene classification

Liang, Le; Wang, Guoli

doi:10.1049/ipr2.12139

Cited by 9 publications

(4 citation statements)

References 40 publications

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“…This is an expected result since CNN is the most frequently used DL method in general computer vision and image processing. Recurrent neural networks (RNN), such as long-short term memories (LSTM) methods, were the second most frequently used DL method supported by attention mechanism for RS image processing with 18 papers [121][122][123], this algorithm is also the first DL method that was improved with attention mechanism [20]. In addition, it was observed that most of the RNN methods were used in combination with CNN methods [76,78,124].…”

Section: Overview Of the Reviewed Papersmentioning

confidence: 99%

Effect of Attention Mechanism in Deep Learning-Based Remote Sensing Image Processing: A Systematic Literature Review

et al. 2021

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Machine learning, particularly deep learning (DL), has become a central and state-of-the-art method for several computer vision applications and remote sensing (RS) image processing. Researchers are continually trying to improve the performance of the DL methods by developing new architectural designs of the networks and/or developing new techniques, such as attention mechanisms. Since the attention mechanism has been proposed, regardless of its type, it has been increasingly used for diverse RS applications to improve the performances of the existing DL methods. However, these methods are scattered over different studies impeding the selection and application of the feasible approaches. This study provides an overview of the developed attention mechanisms and how to integrate them with different deep learning neural network architectures. In addition, it aims to investigate the effect of the attention mechanism on deep learning-based RS image processing. We identified and analyzed the advances in the corresponding attention mechanism-based deep learning (At-DL) methods. A systematic literature review was performed to identify the trends in publications, publishers, improved DL methods, data types used, attention types used, overall accuracies achieved using At-DL methods, and extracted the current research directions, weaknesses, and open problems to provide insights and recommendations for future studies. For this, five main research questions were formulated to extract the required data and information from the literature. Furthermore, we categorized the papers regarding the addressed RS image processing tasks (e.g., image classification, object detection, and change detection) and discussed the results within each group. In total, 270 papers were retrieved, of which 176 papers were selected according to the defined exclusion criteria for further analysis and detailed review. The results reveal that most of the papers reported an increase in overall accuracy when using the attention mechanism within the DL methods for image classification, image segmentation, change detection, and object detection using remote sensing images.

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Section: Overview Of the Reviewed Papersmentioning

confidence: 99%

Effect of Attention Mechanism in Deep Learning-Based Remote Sensing Image Processing: A Systematic Literature Review

et al. 2021

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“…Over the years, the application of the attention mechanism to Convolutional Neural Networks (CNNs) has given rise to a series of attention models [22][23][24][25][26][27][28]. These models have demonstrated the effectiveness of attention mechanisms.…”

Section: Introductionmentioning

confidence: 99%

A Dense Residual Correlation Attention Multi-scale Network for Remote Sensing Scene Classification

Dai,

Shi,

Wang

et al. 2024

Preprint

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Remote sensing scene classification is a crucial task in the field of remote sensing technology, as it helps identify and categorize various geographical features on the Earth's surface. Recently, convolutional neural networks (CNNs) have been widely employed for scene classification in remote sensing images, leading to significant performance improvements. However, current CNN methods have certain limitations, primarily manifested in their tendency to overly focus on extracting high-level features, often overlooking essential information at different levels within the images. Furthermore, some approaches attempt to directly merge features from different levels, potentially resulting in issues of information redundancy or mutual exclusion. To address these challenges, this paper proposes an efficient method for remote sensing scene classification. Our approach involves three main contributions. First, we introduce a feature extraction module called multi-stream feature extraction, which effectively utilizes features at various scales and extracts features at different levels and depths. Second, we propose a dense residual connection feature fusion technique that enables comprehensive feature interactions and improves overall accuracy. Additionally, we introduce a correlated attention network to learn powerful feature representations at multiple levels, further improving classification performance. The method outperforms existing algorithms in terms of effectiveness and accuracy, achieving state-of-the-art results on four widely used remote sensing scene classification benchmarks.

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“…The emergence of these new models has also driven the development of the field of remote sensing scene classification. Many CNNbased methods for remote sensing scene classification have achieved high accuracy [18][19][20].…”

Section: Introductionmentioning

confidence: 99%

LPNet: A remote sensing scene classification method based on large kernel convolution and parameter fusion

Wang,

Shi,

Wang

et al. 2024

IET Image Processing

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Remote sensing scene images contain numerous feature targets with unrelated semantic information, so how to extract to the local key information and semantic features of the image becomes the key to achieving accurate classification. Existing Convolutional Neural Networks (CNNs) mostly concentrate on the global representation of an image and lose the shallow features. To overcome these issues, this paper proposes LPNet for remote sensing scene image classification. First, LPNet employs LKConv to extract the semantic features in the image, while using standard convolution to extract local key information in the image. Additionally, the LPNet applies a shortcut residual concatenation branch to reuse features. Then, parameter fusion combines parameters from previous branches, improving the capacity of the model to obtain a more comprehensive and rich feature representation of the image. Finally, considering the relationship between the classification ability of the model and the depth of feature extraction, the Feature Mixture (FM) Block is used to deepen the model for feature extraction. Comparative experiments on four publicly available datasets show that LPNet provides comparable results to other state‐of‐the‐art methods. The effectiveness of LPNet is further demonstrated by visualizing the effective receptive fields (ERFs).

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Efficient recurrent attention network for remote sensing scene classification

Cited by 9 publications

References 40 publications

Effect of Attention Mechanism in Deep Learning-Based Remote Sensing Image Processing: A Systematic Literature Review

Effect of Attention Mechanism in Deep Learning-Based Remote Sensing Image Processing: A Systematic Literature Review

A Dense Residual Correlation Attention Multi-scale Network for Remote Sensing Scene Classification

LPNet: A remote sensing scene classification method based on large kernel convolution and parameter fusion

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