Small Sample Classification of Hyperspectral Remote Sensing Images Based on Sequential Joint Deeping Learning Model

Wang, Zesong; Zou, Cui; Cai, Weiwei

doi:10.1109/access.2020.2986267

Cited by 56 publications

(27 citation statements)

References 31 publications

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“…e proposed approach firstly introduces the core architecture, working principle, and related ecosystem of the Hadoop platform and then introduces the basic ideas of the data mining theory of big data analysis and the common algorithms of data mining are outlined. Finally, the principle of convolutional neural network [26][27][28][29][30][31] and the two-branch algorithm proposed in this paper are explained. Figure 2 shows the overall framework of the proposed approach.…”

Section: Methodsmentioning

confidence: 99%

Injury Risk Prediction of Aerobics Athletes Based on Big Data and Computer Vision

Zhu

Zhang

Sun

et al. 2021

Scientific Programming

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In recent years, competitive aerobics has been rapidly popularized and developed, and the level of sports skills has also been greatly improved. The performance of some events has gradually approached and reached the advanced level. Therefore, it is vital to invest in the quantitative analysis and cross-disciplinary comprehensive research of aerobics performance and related factors. This paper adopts big data analysis technology and computer vision technology based on convolutional neural network, according to the related theories of sports biomechanics and computer image recognition, to establish a loss risk prediction model for aerobics athletes. The approach firstly has used technology of big data analysis for analyzing the characteristics of competitive aerobics sports data. Secondly, the approach combines the convolutional neural network to visually recognize the aerobics sports images and establish a two-branch prediction model. Finally, the output can be fused to accurately diagnose and evaluate the level of physical fitness development of aerobics athletes, the focus and goal of training content are clarified, and the scientific degree of aerobics training is improved. The study can help injury risk prediction of aerobic athletes based on applications of big data and computer vision.

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Section: Methodsmentioning

confidence: 99%

Injury Risk Prediction of Aerobics Athletes Based on Big Data and Computer Vision

Zhu

Zhang

Sun

et al. 2021

Scientific Programming

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“…Krizhevsky et al [31] proposed a large, deep convolutional neural network to classify the 1.2 million high-resolution images in ImageNet, and achieved record-breaking results. Wang et al [32] proposed a sequential joint deep learning algorithm that was found to achieve powerful performance in the prediction of hyperspectral images of small samples.…”

Section: Deep Learningmentioning

confidence: 99%

APLCNet: Automatic Pixel-Level Crack Detection Network Based on Instance Segmentation

Zhang

Chen

Wang³

et al. 2020

IEEE Access

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The accurate and automatic detection of pavement cracks is essential for pavement maintenance. However, automatic crack detection remains a challenging problem due to the inconspicuous visual features of cracks in complex pavement backgrounds, the complicated shapes and structures of cracks, and the influences of weather changes and noise. In recent years, with the development of artificial intelligence technology, crack detection methods based on classification and semantic segmentation have laid a good foundation for the automation of pavement crack detection. However, there remain shortcomings in the comprehensive acquisition of pavement crack attribute information and detection accuracy. To solve these problems, this paper proposes an instance segmentation network for pavement crack detection. The network can simultaneously obtain the crack category, position, and mask, and can realize end-to-end pixel-level crack detection. A semantic segmentation branch is first added to Mask R-CNN. This branch can extract the bottom-level detail information of the cracks and ultimately improves the accuracy of crack mask prediction. An adaptive feature fusion module is then designed. During feature fusion, this module highlights the attribute information and location information of cracks according to the channel attention mechanism and the spatial attention mechanism. Finally, these two modules are integrated to form an automatic pixel-level crack detection network, namely APLCNet. Without any embellishment, APLCNet achieves a precision of 92.21%, a recall of 94.89%, and an F1-score of 93.53% on the challenging public CFD dataset, thereby outperforming CrackForest and MFCD for pixel-wise crack detection. Moreover, APLCNet achieves a 16.5% mask AP on the self-captured GDPH dataset, thereby surpassing Mask R-CNN and PANet.

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“…They then construct the relationship between the features through a graph convolution neural network and use the NWPU-RESISC45 data and a private data set to verify the effectiveness of the attention mechanism. Zesong Wang et al [23] constructed a multiattention mechanism model. By combining the original attention feature with the average pooling attention feature and the global average pooling attention feature, more a priori knowledge is obtained, which supports later feature mining.…”

Section: Related Researchmentioning

confidence: 99%

Semantic Segmentation of Marine Remote Sensing Based on a Cross Direction Attention Mechanism

Gao

Cao

et al. 2020

IEEE Access

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With the development of remote sensing technology, the semantic segmentation and recognition of various things in the ocean have become more and more frequent. Due to the wide variety of marine things and the large differences in morphology, it has brought greater difficulties to the recognition of marine remote sensing images. In order to obtain better segmentation results of ocean remote sensing images, this paper proposes an cross attention mechanism(Horizontal and Vertical) of exponential operation combined with multi-scale convolution algorithm. Among them, the cross attention mechanism and expanded distribution weight coefficient mentioned in this paper are first proposed. First, Input the marine remote sensing image features into an cross attention mechanism algorithm of exponential operation to obtain feature weight coefficients and joint weight coefficients in multiple directions; Then, the features with weight coefficients are input into the multi-access convolutional layer and the multi-scale dilated convolutional layer respectively for deep feature mining; Then the above steps are repeated twice, and finally the semantic segmentation of marine remote sensing images is achieved by fusing multiple deeplevel features afterwards. Experiments were conducted on three public marine remote sensing data sets, and the results proved the effectiveness of our proposed cross attention mechanism of extended operation algorithm. The F values of the MAMC model on Beach, Island and Sea ice data sets have reached 99.4%, 91.25%, 87.08% respectively. Compared with other models, the effect is significantly improved, and proved the powerful performance of the algorithm in the semantic segmentation of marine remote sensing images.

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Small Sample Classification of Hyperspectral Remote Sensing Images Based on Sequential Joint Deeping Learning Model

Abstract: colleges and universities young talents introduction plan construction team project: big data and business intelligence social service innovation team.

Cited by 56 publications

References 31 publications

Injury Risk Prediction of Aerobics Athletes Based on Big Data and Computer Vision

Injury Risk Prediction of Aerobics Athletes Based on Big Data and Computer Vision

APLCNet: Automatic Pixel-Level Crack Detection Network Based on Instance Segmentation

Semantic Segmentation of Marine Remote Sensing Based on a Cross Direction Attention Mechanism

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