Remote sensing image recognition based on dual-channel deep learning network

Cui, Xianping; Zou, Cui; Wang, Zesong

doi:10.1007/s11042-021-11079-5

Cited by 9 publications

(4 citation statements)

References 27 publications

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“…The second step involves the learning [34] of the channel weight distribution. This distribution determines the significance of each feature map channel.…”

Section: Learnable Attention Module (Lam)mentioning

confidence: 99%

DiffusionFR: Species Recognition of Fish in Blurry Scenarios via Diffusion and Attention

Wang,

Shi,

et al. 2024

Animals

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Blurry scenarios, such as light reflections and water ripples, often affect the clarity and signal-to-noise ratio of fish images, posing significant challenges for traditional deep learning models in accurately recognizing fish species. Firstly, deep learning models rely on a large amount of labeled data. However, it is often difficult to label data in blurry scenarios. Secondly, existing deep learning models need to be more effective for the processing of bad, blurry, and otherwise inadequate images, which is an essential reason for their low recognition rate. A method based on the diffusion model and attention mechanism for fish image recognition in blurry scenarios, DiffusionFR, is proposed to solve these problems and improve the performance of species recognition of fish images in blurry scenarios. This paper presents the selection and application of this correcting technique. In the method, DiffusionFR, a two-stage diffusion network model, TSD, is designed to deblur bad, blurry, and otherwise inadequate fish scene pictures to restore clarity, and a learnable attention module, LAM, is intended to improve the accuracy of fish recognition. In addition, a new dataset of fish images in blurry scenarios, BlurryFish, was constructed and used to validate the effectiveness of DiffusionFR, combining bad, blurry, and otherwise inadequate images from the publicly available dataset Fish4Knowledge. The experimental results demonstrate that DiffusionFR achieves outstanding performance on various datasets. On the original dataset, DiffusionFR achieved the highest training accuracy of 97.55%, as well as a Top-1 accuracy test score of 92.02% and a Top-5 accuracy test score of 95.17%. Furthermore, on nine datasets with light reflection noise, the mean values of training accuracy reached a peak at 96.50%, while the mean values of the Top-1 accuracy test and Top-5 accuracy test were at their highest at 90.96% and 94.12%, respectively. Similarly, on three datasets with water ripple noise, the mean values of training accuracy reached a peak at 95.00%, while the mean values of the Top-1 accuracy test and Top-5 accuracy test were at their highest at 89.54% and 92.73%, respectively. These results demonstrate that the method showcases superior accuracy and enhanced robustness in handling original datasets and datasets with light reflection and water ripple noise.

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“…The second step involves the learning [34] of the channel weight distribution. This distribution determines the significance of each feature map channel.…”

Section: Learnable Attention Module (Lam)mentioning

confidence: 99%

DiffusionFR: Species Recognition of Fish in Blurry Scenarios via Diffusion and Attention

Wang,

Shi,

et al. 2024

Animals

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“…Many academics, both domestically and internationally, have become interested in it recently. Cui X's team [5] developed a recognition method based on dual-channel deep learning (DL) for the limitations faced by the information-diverse image recognition from local or global features. The results showed that this method obtained higher recognition accuracy.…”

Section: Related Workmentioning

confidence: 99%

Application Simulation Research Based on Visual Image Capture Technology in Sports Injury Rehabilitation

Tang

2024

ITC

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To capture and analyze the motion state of patients in real time and improve the evaluation effect of sports injury, the research is based on image recognition in visual image capture technology. Firstly, multiscale attention mechanism was introduced into U-Net image segmentation model to improve the pre-processing of image recognition. Then, the image recognition model of convolutional neural network is optimized by gradient class weighted activation mapping. The combination of the two is applied to the sports injury image processing to verify the effect. The results show that the F1 score and Precision values of the improved segmentation model in the database reach 98.85% and 98.74%, respectively. The segmentation accuracy is obviously improved. The accuracy of the optimized image recognition method in the training set and the test set is about 96% and 98%, respectively. After the combination of the two methods, the processing accuracy of sports injury medical images is 97%, and the running time is within 4s. It has high accuracy and processing efficiency, providing a technical and methodological basis for sports injury rehabilitation training.

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“…The dual-channel feature selection method is a parallel structure that independently solves the feature redundancy problem under the two correlations so as to alleviate the feature loss problem in the single-channel. Meanwhile, the related algorithms of the dual-channel structure are noticed [17,18]. Dual-channel is extremely rare in machine learning, but it is commonly used in deep learning to form dual-channel convolutional neural networks with CNN [19].…”

Section: Introductionmentioning

confidence: 99%

Parallel dual-channel multi-label feature selection

et al. 2023

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In the process of multi-label learning, feature selection methods are often adopted to solve the high-dimensionality problem in feature spaces. Most existing multi-label feature selection algorithms focus on exploring the correlation between features and labels and then obtain the target feature subset by importance ranking. These algorithms commonly use serial structures to obtain important features, which induces the excessive reliance on the ranking results and causes the loss of important features. However, the correlation between label-specific feature and label-instance is ignored. Therefore, this paper proposes Parallel Dual-channel Multi-label Feature Selection algorithm (PDMFS). We first introduce the dual-channel concept and design the algorithm model as two independent modules obtaining different feature correlation sequences, so that the algorithm can avoid the over-reliance on single feature correlation. And then, the proposed algorithm uses the subspace model to select the feature subset with the maximum correlation and minimum redundancy for each sequence, thus obtaining feature subsets under respective correlations. Finally, the subsets are cross-merged to reduce the important feature loss caused by the serial structure processing single feature correlation. The experimental results on eight datasets and statistical hypothesis testing indicate that the proposed algorithm is effective.

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Remote sensing image recognition based on dual-channel deep learning network

Cited by 9 publications

References 27 publications

DiffusionFR: Species Recognition of Fish in Blurry Scenarios via Diffusion and Attention

DiffusionFR: Species Recognition of Fish in Blurry Scenarios via Diffusion and Attention

Application Simulation Research Based on Visual Image Capture Technology in Sports Injury Rehabilitation

Parallel dual-channel multi-label feature selection

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