Continual learning for scene classification of high resolution remote sensing images

2021 ◽  
Author(s):  
Jiangbo Xi ◽  
Ziyun Yan ◽  
Wandong Jiang ◽  
Yaobing Xiang ◽  
Dashuai Xie
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Scene Classification ◽  
Remote Sensing Images ◽  
Continual Learning

scholarly journals Learning Multi-Modality Features for Scene Classification of High-Resolution Remote Sensing Images

2018 ◽  
Vol 06 (11) ◽  
pp. 185-193
Author(s):  
Feng’an Zhao ◽  
Xiongmei Zhang ◽  
Xiaodong Mu ◽  
Zhaoxiang Yi ◽  
Zhou Yang
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Scene Classification ◽  
Remote Sensing Images

scholarly journals Fuzzy Classification of High Resolution Remote Sensing Scenes Using Visual Attention Features

2017 ◽  
Vol 2017 ◽  
pp. 1-9 ◽  
Author(s):  
Linyi Li ◽  
Tingbao Xu ◽  
Yun Chen
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Visual Attention ◽  
Fuzzy Classification ◽  
Accuracy Evaluation ◽  
Scene Classification ◽  
Remote Sensing Images ◽  
Quantitative Accuracy ◽  
Extraction Algorithm

In recent years the spatial resolutions of remote sensing images have been improved greatly. However, a higher spatial resolution image does not always lead to a better result of automatic scene classification. Visual attention is an important characteristic of the human visual system, which can effectively help to classify remote sensing scenes. In this study, a novel visual attention feature extraction algorithm was proposed, which extracted visual attention features through a multiscale process. And a fuzzy classification method using visual attention features (FC-VAF) was developed to perform high resolution remote sensing scene classification. FC-VAF was evaluated by using remote sensing scenes from widely used high resolution remote sensing images, including IKONOS, QuickBird, and ZY-3 images. FC-VAF achieved more accurate classification results than the others according to the quantitative accuracy evaluation indices. We also discussed the role and impacts of different decomposition levels and different wavelets on the classification accuracy. FC-VAF improves the accuracy of high resolution scene classification and therefore advances the research of digital image analysis and the applications of high resolution remote sensing images.

Scene classification of high-resolution remote sensing images based on IMFNet

2019 ◽  
Vol 13 (04) ◽  
pp. 1
Author(s):  
Xin Zhang ◽  
Yongcheng Wang ◽  
Ning Zhang ◽  
Dongdong Xu ◽  
Bo Chen ◽  
...  
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Scene Classification ◽  
Remote Sensing Images

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

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