Cloud detection of space-borne video remote sensing using improved Unet method

2021 ◽  
Author(s):  
Chongbin Xu ◽  
Shengling Geng ◽  
Defang Wang ◽  
Mingquan Zhou
Keyword(s):  
Remote Sensing ◽  
Cloud Detection ◽  
Video Remote Sensing

Cloud Detection From Remote Sensing Imagery Based on Domain Translation Network

2021 ◽  
pp. 1-5
Author(s):  
Jianhua Guo ◽  
Jingyu Yang ◽  
Huanjing Yue ◽  
Yang Chen ◽  
Chunping Hou ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Cloud Detection ◽  
Remote Sensing Imagery

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

Space Target Luminosity Measurement Based on Video Remote Sensing Satellites

2019 ◽  
Vol 48 (12) ◽  
pp. 1228002-1228002
Author(s):  
Yan MA Yan MA ◽  
Chi MA Chi MA ◽  
Yan-hao XIE Yan-hao XIE ◽  
Fang WANG Fang WANG
Keyword(s):  
Remote Sensing ◽  
Video Remote Sensing ◽  
Space Target

scholarly journals Cloud Detection of SuperView-1 Remote Sensing Images Based on Genetic Reinforcement Learning

2020 ◽  
Vol 12 (19) ◽  
pp. 3190
Author(s):  
Xiaolong Li ◽  
Hong Zheng ◽  
Chuanzhao Han ◽  
Haibo Wang ◽  
Kaihan Dong ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Reinforcement Learning ◽  
False Positive Rate ◽  
Optical Remote Sensing ◽  
Cloud Detection ◽  
Remote Sensing Images ◽  
Threshold Method ◽  
Data Set ◽  
Action Strategy ◽  
Positive Rate

Cloud pixels have massively reduced the utilization of optical remote sensing images, highlighting the importance of cloud detection. According to the current remote sensing literature, methods such as the threshold method, statistical method and deep learning (DL) have been applied in cloud detection tasks. As some cloud areas are translucent, areas blurred by these clouds still retain some ground feature information, which blurs the spectral or spatial characteristics of these areas, leading to difficulty in accurate detection of cloud areas by existing methods. To solve the problem, this study presents a cloud detection method based on genetic reinforcement learning. Firstly, the factors that directly affect the classification of pixels in remote sensing images are analyzed, and the concept of pixel environmental state (PES) is proposed. Then, PES information and the algorithm’s marking action are integrated into the “PES-action” data set. Subsequently, the rule of “reward–penalty” is introduced and the “PES-action” strategy with the highest cumulative return is learned by a genetic algorithm (GA). Clouds can be detected accurately through the learned “PES-action” strategy. By virtue of the strong adaptability of reinforcement learning (RL) to the environment and the global optimization ability of the GA, cloud regions are detected accurately. In the experiment, multi-spectral remote sensing images of SuperView-1 were collected to build the data set, which was finally accurately detected. The overall accuracy (OA) of the proposed method on the test set reached 97.15%, and satisfactory cloud masks were obtained. Compared with the best DL method disclosed and the random forest (RF) method, the proposed method is superior in precision, recall, false positive rate (FPR) and OA for the detection of clouds. This study aims to improve the detection of cloud regions, providing a reference for researchers interested in cloud detection of remote sensing images.

scholarly journals Discriminative Feature Learning Constrained Unsupervised Network for Cloud Detection in Remote Sensing Imagery

2020 ◽  
Vol 12 (3) ◽  
pp. 456
Author(s):  
Weiying Xie ◽  
Jian Yang ◽  
Yunsong Li ◽  
Jie Lei ◽  
Jiaping Zhong ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Feature Learning ◽  
Residual Error ◽  
Observation System ◽  
Landsat 8 ◽  
Cloud Detection ◽  
Adversarial Learning ◽  
Remote Sensing Imagery ◽  
Discriminative Feature ◽  
Latent Space

Cloud detection is a significant preprocessing step for increasing the exploitability of remote sensing imagery that faces various levels of difficulty due to the complexity of underlying surfaces, insufficient training data, and redundant information in high-dimensional data. To solve these problems, we propose an unsupervised network for cloud detection (UNCD) on multispectral (MS) and hyperspectral (HS) remote sensing images. The UNCD method enforces discriminative feature learning to obtain the residual error between the original input and the background in deep latent space, which is based on the observation that clouds are sparse and modeled as sparse outliers in remote sensing imagery. The UNCD enforces discriminative feature learning to obtain the residual error between the original input and the background in deep latent space, which is based on the observation that clouds are sparse and modeled as sparse outliers in remote sensing imagery. First, a compact representation of the original imagery is obtained by a latent adversarial learning constrained encoder. Meanwhile, the majority class with sufficient samples (i.e., background pixels) is more accurately reconstructed than the clouds with limited samples by the decoder. An image discriminator is used to prevent the generalization of out-of-class features caused by latent adversarial learning. To further highlight the background information in the deep latent space, a multivariate Gaussian distribution is introduced. In particular, the residual error with clouds highlighted and background samples suppressed is applied in the cloud detection in deep latent space. To evaluate the performance of the proposed UNCD method, experiments were conducted on both MS and HS datasets that were captured by various sensors over various scenes, and the results demonstrate its state-of-the-art performance. The sensors that captured the datasets include Landsat 8, GaoFen-1 (GF-1), and GaoFen-5 (GF-5). Landsat 8 was launched at Vandenberg Air Force Base in California on 11 February 2013, in a mission that was initially known as the Landsat Data Continuity Mission (LDCM). China launched the GF-1 satellite. The GF-5 satellite captures hyperspectral observations in the Chinese Key Projects of High-Resolution Earth Observation System. The overall accuracy (OA) values for Images I and II from the Landsat 8 dataset were 0.9526 and 0.9536, respectively, and the OA values for Images III and IV from the GF-1 wide field of view (WFV) dataset were 0.9957 and 0.9934, respectively. Hence, the proposed method outperformed the other considered methods.

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