Hybrid Dilated Convolution Guided Feature Filtering and Enhancement Strategy for Hyperspectral Image Classification

Liu, Runmin; Cai, Weiwei; Li, Guangjun; Ning, Xin; Jiang, Yizhang

doi:10.1109/lgrs.2021.3100407

Cited by 36 publications

(16 citation statements)

References 20 publications

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“…The attention mechanism is proposed by the imitation of human brain's special vision signals processing mechanism. When human is to observe and identify objects, there will be a targeted focus on target, while ignoring some significant part of the background and global information, the mechanism of selective attention in fine-grained picture recognition task rely on consistent discriminant characteristics of parts [ 14 ]. Therefore, in order to further extract judicious part characteristics, a hybrid attention mechanism is introduced in the network-using the CBAM (convolutional block attention module) algorithm to extract attention weight maps in both channel and spatial dimensions in the two characteristic functions of the backbone network, and to distribute the weights distributed in the original characteristic maps for characteristic fusion, and the fused channel attention and spatial attention modules are added between the convolutional blocks of the first network conv4 and conv5 and the second network conv2 and conv3, respectively, to obtain attention characteristics with different dimensions and more richness.…”

Section: Methodsmentioning

confidence: 99%

Aircraft Image Recognition Network Based on Hybrid Attention Mechanism

Wang

Chen

Liu

2022

Computational Intelligence and Neuroscience

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With the deepening of deep learning research, progress has been made in artificial intelligence. In the process of aircraft classification, the precision rate of aircraft picture recognition based on traditional methods is low due to various types of aircraft, large similarities between different models, and serious texture interference. In this article, the hybrid attention network model (BA-CNN) to implement an aircraft recognition algorithm is proposed to solve the above problems. Using two-channel ResNet-34 as a characteristic extraction function, the depth of network is increased to improve fine-grained characteristic extraction capability without increasing the output characteristic dimension. In the network to introduce a hybrid attention mechanism, respectively, between the residual units of two ResNet-34 channels, channel attention and spatial attention modules are added, more abundant mixed characteristics of attention are obtained, space and characteristics of the local characteristics of the channel response are focused, the characteristics of redundancy are reduced, and the fine-grained characteristics of learning ability are further enhanced. Trained and tested on FGVC-aircraft, a public fine-grained pictures dataset, the recognition precision rate of the BA-CNN networks model reached 89.2%. It can be seen from the experimental results, the recognition precision rate of the original model is improved effectively by using this method, and the recognition precision rate is higher than most of the existing mainstream aircraft recognition ways.

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Section: Methodsmentioning

confidence: 99%

Aircraft Image Recognition Network Based on Hybrid Attention Mechanism

Wang

Chen

Liu

2022

Computational Intelligence and Neuroscience

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“…Dilated Convolution In deep networks, to ensure a lower computational effort while expanding the perceptual field, we adopt dilated convolution [55] . The filter can be applied to regions larger than the length of the filter itself by skipping some of the inputs (interval sampling).…”

Section: Methodologiesmentioning

confidence: 99%

A deep spatio-temporal meta-learning model for urban traffic revitalization index prediction in the COVID-19 pandemic

Wang

Sheng

et al. 2022

Advanced Engineering Informatics

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“…A low spatial resolution can cause small objects to be either over-segmented or missed. The authors in [41] show how dilated convolution layers can reduce the loss of spatial information while still managing to gather distant features (increase in spatial resolution). The architecture D2A U-Net [42] uses dilated convolutions in the model's decoder to improve the receptive field and refine the decoding process.…”

Section: Receptive Field and Spatial Resolutionmentioning

confidence: 99%

Semantic Segmentation and Edge Detection—Approach to Road Detection in Very High Resolution Satellite Images

Ghandorh

Boulila

Masood³

et al. 2022

Remote Sensing

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Road detection technology plays an essential role in a variety of applications, such as urban planning, map updating, traffic monitoring and automatic vehicle navigation. Recently, there has been much development in detecting roads in high-resolution (HR) satellite images based on semantic segmentation. However, the objects being segmented in such images are of small size, and not all the information in the images is equally important when making a decision. This paper proposes a novel approach to road detection based on semantic segmentation and edge detection. Our approach aims to combine these two techniques to improve road detection, and it produces sharp-pixel segmentation maps, using the segmented masks to generate road edges. In addition, some well-known architectures, such as SegNet, used multi-scale features without refinement; thus, using attention blocks in the encoder to predict fine segmentation masks resulted in finer edges. A combination of weighted cross-entropy loss and the focal Tversky loss as the loss function is also used to deal with the highly imbalanced dataset. We conducted various experiments on two datasets describing real-world datasets covering the three largest regions in Saudi Arabia and Massachusetts. The results demonstrated that the proposed method of encoding HR feature maps effectively predicts sharp segmentation masks to facilitate accurate edge detection, even against a harsh and complicated background.

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Hybrid Dilated Convolution Guided Feature Filtering and Enhancement Strategy for Hyperspectral Image Classification

Cited by 36 publications

References 20 publications

Aircraft Image Recognition Network Based on Hybrid Attention Mechanism

Aircraft Image Recognition Network Based on Hybrid Attention Mechanism

A deep spatio-temporal meta-learning model for urban traffic revitalization index prediction in the COVID-19 pandemic

Semantic Segmentation and Edge Detection—Approach to Road Detection in Very High Resolution Satellite Images

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