Cloud detection of remote sensing images by deep learning

2016 ◽  
Author(s):  
Mengyun Shi ◽  
Fengying Xie ◽  
Yue Zi ◽  
Jihao Yin
Keyword(s):  
Remote Sensing ◽  
Deep Learning ◽  
Cloud Detection ◽  
Remote Sensing Images

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.

scholarly journals Self-Attention in Reconstruction Bias U-Net for Semantic Segmentation of Building Rooftops in Optical Remote Sensing Images

2021 ◽  
Vol 13 (13) ◽  
pp. 2524
Author(s):  
Ziyi Chen ◽  
Dilong Li ◽  
Wentao Fan ◽  
Haiyan Guan ◽  
Cheng Wang ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Deep Learning ◽  
Semantic Segmentation ◽  
Extraction Methods ◽  
The Self ◽  
Optical Remote Sensing ◽  
Building Extraction ◽  
Learning Models ◽  
Remote Sensing Images ◽  
Segmentation Methods

Deep learning models have brought great breakthroughs in building extraction from high-resolution optical remote-sensing images. Among recent research, the self-attention module has called up a storm in many fields, including building extraction. However, most current deep learning models loading with the self-attention module still lose sight of the reconstruction bias’s effectiveness. Through tipping the balance between the abilities of encoding and decoding, i.e., making the decoding network be much more complex than the encoding network, the semantic segmentation ability will be reinforced. To remedy the research weakness in combing self-attention and reconstruction-bias modules for building extraction, this paper presents a U-Net architecture that combines self-attention and reconstruction-bias modules. In the encoding part, a self-attention module is added to learn the attention weights of the inputs. Through the self-attention module, the network will pay more attention to positions where there may be salient regions. In the decoding part, multiple large convolutional up-sampling operations are used for increasing the reconstruction ability. We test our model on two open available datasets: the WHU and Massachusetts Building datasets. We achieve IoU scores of 89.39% and 73.49% for the WHU and Massachusetts Building datasets, respectively. Compared with several recently famous semantic segmentation methods and representative building extraction methods, our method’s results are satisfactory.

Deep Matting for Cloud Detection in Remote Sensing Images

2020 ◽  
Vol 58 (12) ◽  
pp. 8490-8502
Author(s):  
Wenyuan Li ◽  
Zhengxia Zou ◽  
Zhenwei Shi
Keyword(s):  
Remote Sensing ◽  
Cloud Detection ◽  
Remote Sensing Images
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