An Effective Cloud Detection Method for Gaofen-5 Images via Deep Learning

Yu, Junchuan; Li, Yichuan; Zheng, Xiangxiang; Zhong, Yufeng; He, Peng

doi:10.3390/rs12132106

Cited by 27 publications

(10 citation statements)

References 66 publications

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“…All these approaches implement cloud/snow detection through a deep learning-based semantic segmentation task. For the characteristics and difficulties of the cloud/ snow detection task itself, the multiscale convolutional feature fusion method [23] and the multiscale fusion gated cloud detection model MFGNet [24] have also been proposed. ey couple the multiscale features of remote sensing images, which effectively improve the cloud detection accuracy.…”

Section: Introductionmentioning

confidence: 99%

Improving Cloud/Snow Detection in Remote Sensing Image with Spatiotemporal Information Fusion

Wen¹,

Zhang²,

et al. 2022

Security and Communication Networks

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The clouds and snow in optical remote sensing images always interfere with the interpretation of remote sensing images, which even makes an entire image unavailable. In general, the proportion of cloud/snow cover in remote sensing images needs to be clarified to improve the utilization of remote sensing images. The metadata of remote sensing image products contains prior knowledge of spatiotemporal information, such as imaging time, latitude and longitude, and altitude. This paper proposes a remote sensing image cloud/snow detection method that fuses spatial and temporal information. The proposed method can combine spatiotemporal information for feature extraction and stitching, thus improving the accuracy of remote sensing image cloud/snow detection. In this study, the proposed method is trained and tested with a large-scale cloud/snow image dataset. The experimental results show that both the temporal or spatial information alone and the fused temporal and spatial information can improve the cloud/snow detection accuracy in remote sensing images. The easy-to-obtain imaging time information can also significantly improve the detection accuracy for cloud/snow. The proposed method can be used to improve the cloud/snow detection effect of any remote sensing image product containing prior knowledge of spatiotemporal information and has a good application prospect.

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Section: Introductionmentioning

confidence: 99%

Improving Cloud/Snow Detection in Remote Sensing Image with Spatiotemporal Information Fusion

Wen¹,

Zhang²,

et al. 2022

Security and Communication Networks

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“…In particular, the increase in the spatial and spectral resolution of remote sensing images brings new challenges to manually designed features. In addition, the pixel-by-pixel detection in some machine learning methods and threshold methods is prone to produce the salt-and-pepper (SAP) effect, which affects the detection accuracy [20].…”

Section: Introductionmentioning

confidence: 99%

Light-Weight Cloud Detection Network for Optical Remote Sensing Images with Attention-Based DeeplabV3+ Architecture

2021

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Clouds in optical remote sensing images cause spectral information change or loss, that affects image analysis and application. Therefore, cloud detection is of great significance. However, there are some shortcomings in current methods, such as the insufficient extendibility due to using the information of multiple bands, the intense extendibility due to relying on some manually determined thresholds, and the limited accuracy, especially for thin clouds or complex scenes caused by low-level manual features. Combining the above shortcomings and the requirements for efficiency in practical applications, we propose a light-weight deep learning cloud detection network based on DeeplabV3+ architecture and channel attention module (CD-AttDLV3+), only using the most common red–green–blue and near-infrared bands. In the CD-AttDLV3+ architecture, an optimized backbone network-MobileNetV2 is used to reduce the number of parameters and calculations. Atrous spatial pyramid pooling effectively reduces the information loss caused by multiple down-samplings while extracting multi-scale features. CD-AttDLV3+ concatenates more low-level features than DeeplabV3+ to improve the cloud boundary quality. The channel attention module is introduced to strengthen the learning of important channels and improve the training efficiency. Moreover, the loss function is improved to alleviate the imbalance of samples. For the Landsat-8 Biome set, CD-AttDLV3+ achieves the highest accuracy in comparison with other methods, including Fmask, SVM, and SegNet, especially for distinguishing clouds from bright surfaces and detecting light-transmitting thin clouds. It can also perform well on other Landsat-8 and Sentinel-2 images. Experimental results indicate that CD-AttDLV3+ is robust, with a high accuracy and extendibility.

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“…Benefiting from the development of hyperspectral satellite technology, Gaofen-5, 6,7 ZY-1 02D 8 has been successfully launched, thus providing sufficient data guarantee for large-scale LM. 9 Both Gaofen-5 and ZY-1 02D have a width of 60 km, a spatial resolution of 30 m, and hundreds of spectral bands, which are helpful for developing accurate, efficient, and low-cost geological mapping applications.…”

Section: Introductionmentioning

confidence: 99%

3D autoencoder algorithm for lithological mapping using ZY-1 02D hyperspectral imagery: a case study of Liuyuan region

Zhang

Li³

et al. 2021

J. Appl. Rem. Sens.

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A hyperspectral image (HSI) contains hundreds of spectral bands, which provide detailed spectral information, thus offering an inherent advantage in classification. The successful launch of the Gaofen-5 and ZY-1 02D hyperspectral satellites has promoted the need for large-scale geological applications, such as mineral and lithological mapping (LM). In recent years, following the success of computer vision, deep learning methods have shown their advantage in solving the problem of hyperspectral classification. However, the combination of deep learning and HSI to solve the problem of geological mapping is insufficient. We propose a new 3D convolutional autoencoder for LM. A pixel-based and cube-based 3D convolutional neural network architecture is designed to extract spatial-spectral features. Traditional and machine learning methods are employed as competing methods, trained on two real hyperspectral datasets, and evaluated according to the overall accuracy, F1 score, and other metrics. Results indicate that the proposed method can provide convincing results for LM applications on the basis of the hyperspectral data provided by the ZY-1 02D satellite. Compared with traditional methods, the combination of deep learning and hyperspectral can provide more efficient and highly accurate results. The proposed method has better robustness than supervised learning methods and shows great promise under small sample conditions. As far as we know, this work is the first attempt to apply unsupervised spatial-spectral feature learning technology in LM applications, which is of great significance for large-scale applications. © The Authors. Published by SPIE under a Creative Commons Attribution 4.0 International License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.

show abstract

An Effective Cloud Detection Method for Gaofen-5 Images via Deep Learning

Cited by 27 publications

References 66 publications

Improving Cloud/Snow Detection in Remote Sensing Image with Spatiotemporal Information Fusion

Improving Cloud/Snow Detection in Remote Sensing Image with Spatiotemporal Information Fusion

Light-Weight Cloud Detection Network for Optical Remote Sensing Images with Attention-Based DeeplabV3+ Architecture

3D autoencoder algorithm for lithological mapping using ZY-1 02D hyperspectral imagery: a case study of Liuyuan region

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