Spectrum selection and deep feature fusion based hyperspectral image natural scene classification network

2021 ◽  
Author(s):  
Weilong Guo ◽  
Zifei Zhao ◽  
Longxuan Kou ◽  
Junjie Lu ◽  
Shaopan Xiong ◽  
...  
Keyword(s):  
Hyperspectral Image ◽  
Feature Fusion ◽  
Natural Scene ◽  
Scene Classification ◽  
Deep Feature ◽  
Spectrum Selection

scholarly journals Dense Connectivity Based Two-Stream Deep Feature Fusion Framework for Aerial Scene Classification

2018 ◽  
Vol 10 (7) ◽  
pp. 1158 ◽  
Author(s):  
Yunlong Yu ◽  
Fuxian Liu
Keyword(s):  
Remote Sensing ◽  
Feature Fusion ◽  
Local Binary Patterns ◽  
Aerial Image ◽  
Great Success ◽  
Scene Classification ◽  
Fusion Model ◽  
Deep Architecture ◽  
Deep Feature ◽  
Training Samples

Aerial scene classification is an active and challenging problem in high-resolution remote sensing imagery understanding. Deep learning models, especially convolutional neural networks (CNNs), have achieved prominent performance in this field. The extraction of deep features from the layers of a CNN model is widely used in these CNN-based methods. Although the CNN-based approaches have obtained great success, there is still plenty of room to further increase the classification accuracy. As a matter of fact, the fusion with other features has great potential for leading to the better performance of aerial scene classification. Therefore, we propose two effective architectures based on the idea of feature-level fusion. The first architecture, i.e., texture coded two-stream deep architecture, uses the raw RGB network stream and the mapped local binary patterns (LBP) coded network stream to extract two different sets of features and fuses them using a novel deep feature fusion model. In the second architecture, i.e., saliency coded two-stream deep architecture, we employ the saliency coded network stream as the second stream and fuse it with the raw RGB network stream using the same feature fusion model. For sake of validation and comparison, our proposed architectures are evaluated via comprehensive experiments with three publicly available remote sensing scene datasets. The classification accuracies of saliency coded two-stream architecture with our feature fusion model achieve 97.79%, 98.90%, 94.09%, 95.99%, 85.02%, and 87.01% on the UC-Merced dataset (50% and 80% training samples), the Aerial Image Dataset (AID) (20% and 50% training samples), and the NWPU-RESISC45 dataset (10% and 20% training samples), respectively, overwhelming state-of-the-art methods.

scholarly journals AttentionBased Deep Feature Fusion for the Scene Classification of HighResolution Remote Sensing Images

2019 ◽  
Vol 11 (17) ◽  
pp. 1996 ◽  
Author(s):  
Zhu ◽  
Yan ◽  
Mo ◽  
Liu
Keyword(s):  
Remote Sensing ◽  
Loss Function ◽  
Feature Fusion ◽  
Cross Entropy ◽  
Scene Classification ◽  
Remote Sensing Images ◽  
Graphic Processing Units ◽  
Entropy Loss ◽  
Deep Feature

Scene classification of highresolution remote sensing images (HRRSI) is one of the most important means of landcover classification. Deep learning techniques, especially the convolutional neural network (CNN) have been widely applied to the scene classification of HRRSI due to the advancement of graphic processing units (GPU). However, they tend to extract features from the whole images rather than discriminative regions. The visual attention mechanism can force the CNN to focus on discriminative regions, but it may suffer from the influence of intraclass diversity and repeated texture. Motivated by these problems, we propose an attention-based deep feature fusion (ADFF) framework that constitutes three parts, namely attention maps generated by Gradientweighted Class Activation Mapping (GradCAM), a multiplicative fusion of deep features and the centerbased cross-entropy loss function. First of all, we propose to make attention maps generated by GradCAM as an explicit input in order to force the network to concentrate on discriminative regions. Then, deep features derived from original images and attention maps are proposed to be fused by multiplicative fusion in order to consider both improved abilities to distinguish scenes of repeated texture and the salient regions. Finally, the centerbased cross-entropy loss function that utilizes both the cross-entropy loss and center loss function is proposed to backpropagate fused features so as to reduce the effect of intraclass diversity on feature representations. The proposed ADFF architecture is tested on three benchmark datasets to show its performance in scene classification. The experiments confirm that the proposed method outperforms most competitive scene classification methods with an average overall accuracy of 94% under different training ratios.

Scene Classification Based on Two-Stage Deep Feature Fusion

2018 ◽  
Vol 15 (2) ◽  
pp. 183-186 ◽  
Author(s):  
Yishu Liu ◽  
Yingbin Liu ◽  
Liwang Ding
Keyword(s):  
Feature Fusion ◽  
Scene Classification ◽  
Two Stage ◽  
Deep Feature

scholarly journals Retraction: Zhu R. et al. Attention-Based Deep Feature Fusion for the Scene Classification of High-Resolution Remote Sensing Images. Remote Sensing. 2019, 11(17), 1996

2020 ◽  
Vol 12 (4) ◽  
pp. 742 ◽  
Author(s):  
Ruixi Zhu ◽  
Li Yan ◽  
Nan Mo ◽  
Yi Liu
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Research Method ◽  
Feature Fusion ◽  
Scene Classification ◽  
Remote Sensing Images ◽  
Deep Feature

We have been made aware that the innovative contributions, research method and the majority of the content of this article [...]

Deep Feature Fusion for VHR Remote Sensing Scene Classification

2017 ◽  
Vol 55 (8) ◽  
pp. 4775-4784 ◽  
Author(s):  
Souleyman Chaib ◽  
Huan Liu ◽  
Yanfeng Gu ◽  
Hongxun Yao
Keyword(s):  
Remote Sensing ◽  
Feature Fusion ◽  
Scene Classification ◽  
Deep Feature
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