An integrated deep learning framework of U-Net and inception module for cloud detection of remote sensing images

2021 ◽  
Vol 14 (18) ◽  
Author(s):  
Aarti Kumthekar ◽  
G. Ramachandra Reddy
Keyword(s):  
Remote Sensing ◽  
Deep Learning ◽  
Cloud Detection ◽  
Remote Sensing Images ◽  
Learning Framework

scholarly journals Deep learning based cloud detection for medium and high resolution remote sensing images of different sensors

2019 ◽  
Vol 150 ◽  
pp. 197-212 ◽  
Author(s):  
Zhiwei Li ◽  
Huanfeng Shen ◽  
Qing Cheng ◽  
Yuhao Liu ◽  
Shucheng You ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Deep Learning ◽  
High Resolution ◽  
Cloud Detection ◽  
Remote Sensing Images

Cloud Detection of ZY-3 Satellite Remote Sensing Images Based on Deep Learning

2018 ◽  
Vol 38 (1) ◽  
pp. 0128005
Author(s):  
陈洋 Chen Yang ◽  
范荣双 Fan Rongshuang ◽  
王竞雪 Wang Jingxue ◽  
陆婉芸 Lu Wanyun ◽  
朱红 Zhu Hong ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Deep Learning ◽  
Satellite Remote Sensing ◽  
Cloud Detection ◽  
Remote Sensing Images

Multilevel Cloud Detection in Remote Sensing Images Based on Deep Learning

2017 ◽  
Vol 10 (8) ◽  
pp. 3631-3640 ◽  
Author(s):  
Fengying Xie ◽  
Mengyun Shi ◽  
Zhenwei Shi ◽  
Jihao Yin ◽  
Danpei Zhao
Keyword(s):  
Remote Sensing ◽  
Deep Learning ◽  
Cloud Detection ◽  
Remote Sensing Images

scholarly journals Convolutional Neural Network for the Semantic Segmentation of Remote Sensing Images

2021 ◽  
Vol 26 (1) ◽  
pp. 200-215
Author(s):  
Muhammad Alam ◽  
Jian-Feng Wang ◽  
Cong Guangpei ◽  
LV Yunrong ◽  
Yuanfang Chen
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
Neural Networks ◽  
Image Processing ◽  
Deep Learning ◽  
Semantic Segmentation ◽  
Natural Scene ◽  
Remote Sensing Images ◽  
Advantages And Disadvantages ◽  
Target Segmentation

AbstractIn recent years, the success of deep learning in natural scene image processing boosted its application in the analysis of remote sensing images. In this paper, we applied Convolutional Neural Networks (CNN) on the semantic segmentation of remote sensing images. We improve the Encoder- Decoder CNN structure SegNet with index pooling and U-net to make them suitable for multi-targets semantic segmentation of remote sensing images. The results show that these two models have their own advantages and disadvantages on the segmentation of different objects. In addition, we propose an integrated algorithm that integrates these two models. Experimental results show that the presented integrated algorithm can exploite the advantages of both the models for multi-target segmentation and achieve a better segmentation compared to these two models.

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