Global-Local Attention Network for Aerial Scene Classification

Guo, Yiyou; Ji, Jinsheng; Lu, Xiankai; Hu, Hong; Fang, Tao; Li, Deren

doi:10.1109/access.2019.2918732

Cited by 63 publications

(50 citation statements)

References 49 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The overall accuracy of BiMobileNet is 92.06% and 94.08% when the training ratios are 10% and 20%, respectively; this is higher than all but one other methods. When the training ratio is 10%, BiMobileNet accuracy is 2.1%,1.0% and 0.3% higher than SF-CNN [44], GLANet [46] and DML [49], respectively, and is similar to DDRL-AM [41]. SF-CNN, GLANet, and DML adopt deep CNN VGGNet; DDRL-AM adopts deep CNN ResNet18.…”

Section: Classification Of the Nwpu-resisc45 Datasetmentioning

confidence: 99%

An Efficient and Lightweight Convolutional Neural Network for Remote Sensing Image Scene Classification

Guo

et al. 2020

Sensors

View full text Add to dashboard Cite

Classifying remote sensing images is vital for interpreting image content. Presently, remote sensing image scene classification methods using convolutional neural networks have drawbacks, including excessive parameters and heavy calculation costs. More efficient and lightweight CNNs have fewer parameters and calculations, but their classification performance is generally weaker. We propose a more efficient and lightweight convolutional neural network method to improve classification accuracy with a small training dataset. Inspired by fine-grained visual recognition, this study introduces a bilinear convolutional neural network model for scene classification. First, the lightweight convolutional neural network, MobileNetv2, is used to extract deep and abstract image features. Each feature is then transformed into two features with two different convolutional layers. The transformed features are subjected to Hadamard product operation to obtain an enhanced bilinear feature. Finally, the bilinear feature after pooling and normalization is used for classification. Experiments are performed on three widely used datasets: UC Merced, AID, and NWPU-RESISC45. Compared with other state-of-art methods, the proposed method has fewer parameters and calculations, while achieving higher accuracy. By including feature fusion with bilinear pooling, performance and accuracy for remote scene classification can greatly improve. This could be applied to any remote sensing image classification task.

show abstract

Section: Classification Of the Nwpu-resisc45 Datasetmentioning

confidence: 99%

An Efficient and Lightweight Convolutional Neural Network for Remote Sensing Image Scene Classification

Guo

et al. 2020

Sensors

View full text Add to dashboard Cite

show abstract

“…Chen et al [62] incorporated spatial attention (SA) and channel-wise attention (CA) into CNN to obtain more discriminative features. For remote sensing images, Guo et al [63] utilized attention mechanisms to learn global and local semantic information for aerial scene classification. Channel-wise, attention usually considers useful information from different channels.…”

Section: Related Workmentioning

confidence: 99%

“…It has been proved that by utilizing attention mechanisms, more discriminative features are learned [64] for land-use scene classification, which not only accelerates the network and but reduces the computation time significantly [65]. Therefore, according to the properties of image scenes and successful motivation of attention mechanisms [44]- [47], [62], [63]- [65] it is concluded that it can produce better discriminative features for scene classification. Fig.…”

Section: Related Workmentioning

confidence: 99%

Diverse Capsules Network Combining Multiconvolutional Layers for Remote Sensing Image Scene Classification

Raza

Sirajuddin

et al. 2020

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

Self Cite

View full text Add to dashboard Cite

Remote sensing image scene classification has drawn significant attention for its potential applications in the economy and livelihoods. Unlike the traditional handcrafted features, the convolutional neural networks (CNNs) provides an excellent avenue in obtaining powerful discriminative features. Although tremendous efforts have been made so far in this domain, there are still many open challenges in scene classification due to the scene complexity with higher within-class diversity and between-class similarity. To solve the above-mentioned problems, D-CapsNet is proposed to learn the richer and more robust features for scene classification. It is an end to end network with four types of layers and incorporates visual attention mechanisms. Its diverse capsules encode different properties of complex image scenes, including deep high-level features, spatial attention based on the fusion of multilayers features, both spatial and channel attention based on high-level features, and their fusion. Experiments on three image scene datasets demonstrate that D-CapsNet outperforms other baselines and state-of-the-art methods with a significant improvement in both classification accuracy and speed.

show abstract

“…Scene classification of RSI, i.e. automatically extracting valuable information from each scene image and categorizing them into different classes based on their semantic information, has become a research hotspot in RSI interpretation [1], [4], [5]. Scene classification of RSI has a wide range of applications, including urban planning, natural disaster detection, landcover/land-use classification, environment monitoring and so on [6], [7].…”

Section: Introductionmentioning

confidence: 99%

“…Over the past decades, considerable efforts have been made to solve this problem and numerous approaches have been proposed. Existing scene classification methods are usually divided into two categories according to the used features: (a) handcrafted-based feature methods; and (b) learned-based methods, especially deep learning-based methods [5]. In recent years, with the fast development of Convolutional Neural Network (CNN), a variety of CNN-based methods have been dominating the field of scene classification mainly due to its capacity to learn hierarchical representation to describe the image scenes [5], [8], [9].…”

Section: Introductionmentioning

confidence: 99%

Scene Classification of Remote Sensing Images Based on Saliency Dual Attention Residual Network

2020

View full text Add to dashboard Cite

Scene classification of high-resolution Remote Sensing Images (RSI) is one of basic challenges in RSI interpretation. Existing scene classification methods based on deep learning have achieved impressive performances. However, since RSI commonly contain various types of ground objects and complex backgrounds, most of methods cannot focus on saliency features of scene, which limits the classification performances. To address this issue, we propose a novel Saliency Dual Attention Residual Network (SDAResNet) to extract both cross-channel and spatial saliency information for scene classification of RSI. More specifically, the proposed SDAResNet consists of spatial attention and channel attention, in which spatial attention is embedded in low-level feature to emphasize saliency location information and suppress background information, and channel attention is integrated to high-level features to extract saliency meaningful information. Additionally, several image classification tricks are used to further improve classification accuracy. Finally, Extensive experiments on two challenging benchmark RSI datasets are presented to demonstrate that our methods outperform most of state-of-the-art approaches significantly.

show abstract

Global-Local Attention Network for Aerial Scene Classification

Cited by 63 publications

References 49 publications

An Efficient and Lightweight Convolutional Neural Network for Remote Sensing Image Scene Classification

An Efficient and Lightweight Convolutional Neural Network for Remote Sensing Image Scene Classification

Diverse Capsules Network Combining Multiconvolutional Layers for Remote Sensing Image Scene Classification

Scene Classification of Remote Sensing Images Based on Saliency Dual Attention Residual Network

Contact Info

Product

Resources

About