Efficient Convolutional Neural Architecture Search for Remote Sensing Image Scene Classification

2020 ◽  
pp. 1-14
Author(s):  
Cheng Peng ◽  
Yangyang Li ◽  
Licheng Jiao ◽  
Ronghua Shang
Keyword(s):  
Remote Sensing ◽  
Remote Sensing Image ◽  
Scene Classification ◽  
Neural Architecture

AutoRSISC: Automatic design of neural architecture for remote sensing image scene classification

2020 ◽  
Vol 140 ◽  
pp. 186-192
Author(s):  
Weipeng Jing ◽  
Quanlin Ren ◽  
Jun Zhou ◽  
Houbing Song
Keyword(s):  
Remote Sensing ◽  
Remote Sensing Image ◽  
Automatic Design ◽  
Scene Classification ◽  
Neural Architecture

Marginal Center Loss for Deep Remote Sensing Image Scene Classification

2020 ◽  
Vol 17 (6) ◽  
pp. 968-972 ◽  
Author(s):  
Tianyu Wei ◽  
Jue Wang ◽  
Wenchao Liu ◽  
He Chen ◽  
Hao Shi
Keyword(s):  
Remote Sensing ◽  
Remote Sensing Image ◽  
Scene Classification ◽  
Center Loss

scholarly journals A Multi-Branch Feature Fusion Strategy Based on an Attention Mechanism for Remote Sensing Image Scene Classification

2021 ◽  
Vol 13 (10) ◽  
pp. 1950
Author(s):  
Cuiping Shi ◽  
Xin Zhao ◽  
Liguo Wang
Keyword(s):  
Remote Sensing ◽  
Feature Extraction ◽  
Classification Accuracy ◽  
Feature Fusion ◽  
State Of The Art ◽  
Rapid Development ◽  
Remote Sensing Image ◽  
Classification Performance ◽  
Attention Mechanism ◽  
Scene Classification

In recent years, with the rapid development of computer vision, increasing attention has been paid to remote sensing image scene classification. To improve the classification performance, many studies have increased the depth of convolutional neural networks (CNNs) and expanded the width of the network to extract more deep features, thereby increasing the complexity of the model. To solve this problem, in this paper, we propose a lightweight convolutional neural network based on attention-oriented multi-branch feature fusion (AMB-CNN) for remote sensing image scene classification. Firstly, we propose two convolution combination modules for feature extraction, through which the deep features of images can be fully extracted with multi convolution cooperation. Then, the weights of the feature are calculated, and the extracted deep features are sent to the attention mechanism for further feature extraction. Next, all of the extracted features are fused by multiple branches. Finally, depth separable convolution and asymmetric convolution are implemented to greatly reduce the number of parameters. The experimental results show that, compared with some state-of-the-art methods, the proposed method still has a great advantage in classification accuracy with very few parameters.

scholarly journals Ensemble model with cascade attention mechanism for high-resolution remote sensing image scene classification

2020 ◽  
Vol 28 (15) ◽  
pp. 22358
Author(s):  
Fengpeng Li ◽  
Ruyi Feng ◽  
Wei Han ◽  
Lizhe Wang
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Remote Sensing Image ◽  
Attention Mechanism ◽  
Ensemble Model ◽  
Scene Classification
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