Cloud Detectionof ZY-3 Remote Sensing Images Based on Fully Convolutional Neural Network and Conditional Random Field

Liang, 裴亮 Pei; Yang, 刘阳 Liu; Lin, 高琳 Gao

doi:10.3788/lop56.102802

Cited by 4 publications

(4 citation statements)

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“…Conditional random field reasoning refers to finding a marker sequence Y ={ y 1 , y 2 ,…, y n } corresponding to the most probable one given an observation sequence X ={ x 1 , x 2 ,…, x n }. In the distribution function of conditional random fields, the normalized factor is completely independent of the marker sequence [ 20 ]. Therefore, given the model parameters, the most likely marker sequence can be expressed as:

…”

Section: Methodsmentioning

confidence: 99%

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Key Information Extraction Algorithm of Different Types of Digital Archives for Cultural Operation and Management

2022

Computational Intelligence and Neuroscience

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In order to improve the effect of key information extraction from digital archives, a key information extraction algorithm for different types of digital archives is designed. Preprocess digital archive information, taking part of speech and marks as key information. Self-organizing feature mapping network is used to extract the key information features of digital archives, and the semantic similarity calculation results are obtained by combining the feature extraction results. Combine with mutual information collection, take that word with the highest mutual information value as the collection cent, traverse all keywords, and take the central word as the key information of digital archives to complete the extraction of key information. Experiments show that the recall rate of the algorithm ranges from 96% to 99%, the extraction accuracy of key information of digital archives is between 96 and 98%, and the average extraction time of key information of digital archives is 0.63 s. The practical application effect is good.

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…”

Section: Methodsmentioning

confidence: 99%

“…, x n 􏼈 􏼉. In the distribution function of conditional random fields, the normalized factor is completely independent of the marker sequence [20]. erefore, given the model parameters, the most likely marker sequence can be expressed as:…”

Section: E Part Of Speechmentioning

confidence: 99%

Key Information Extraction Algorithm of Different Types of Digital Archives for Cultural Operation and Management

2022

Computational Intelligence and Neuroscience

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“…Taking the conditional random field as the post-processing of model output data, it can not only take into account the spatial context information, but also reflect the interdependence between observation variables, refine and smooth the edges of model segmentation, and remove small error segmentation regions. [61] .…”

Section: Fusion Post-processing Machine Modelmentioning

confidence: 99%

A brief analysis of remote sensing image cloud extraction method based on deep learning

Lin,

Gai

2023

Third International Conference on Machine Learning and Computer Application (ICMLCA 2022)

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“…Mohajerani et al [33] use full convolutional neural networks for cloud recognition in Landsat8 images. Liang et al [34] realized the cloud detection in ZY-03 satellite images by combining conditional random fields on the basis of full convolutional neural network, which improves the detection accuracy compared with FCN. Hu et al [35] introduced a high frequency characterization extraction module and multiscale convolution, which are based on UNet and added a spatial a priori self-attention mechanism, which resulted in high Mean Intersection over Union.…”

Section: Introductionmentioning

confidence: 99%

Cloud Detection Method Based on Improved DeeplabV3+ Remote Sensing Image

Qian,

Ci,

Tan

et al. 2024

IEEE Access

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Cloud cover is a phenomenon that inevitably exists in remote sensing images, and ground information is lost due to the presence of clouds. To a large extent, it causes degradation of the remote sensing image quality. Therefore, the detection of clouds in remote sensing images is the foundation and key to further emphasizing and use of remote sensing image information. To tackle the question of misjudgment, omission, and long training time of current deep learning-based cloud detection methods, this article suggests an improved cloud detection method for remote sensing images with a deeplabV3+ network. The method aims to detect cloud-covered regions in remote sensing images more accurately, quickly and efficiently. The training time is reduced by improving the Xception backbone network to reduce the amount of parameters; the improved CBAM module is added after the Atrous Spatial Pyramid Pooling (ASPP) module to strengthen the sensory field to better capture the contextual information; and the GAU module is used to replace the traditional bilinear interpolation up-sampling in the decoder part, which results in a better quality of the upsampling, more spatial sense of the fusion, and improved accuracy of the segmentation. Experiments were conducted by using datasets from the homegrown ZY-03 satellite and comparing them with traditional DeeplabV3+, MobileNet and U-Net. And the generalization ability is verified using the public Sentinal-2 dataset. Compared to legacy deeplabV3+ networks, the accuracy rate is improved by 3.91%, the improved precision by 3.12%, the improved recall by 2.78%, and the improved Mean Intersection over Union (MIoU) by 5.75%. Compared to Mobilenet, the improved accuracy by 1.79%, the improved precision by 1.47%, the improved recall by 1.42%, and the improved Mean Intersection over Union (MIoU) by 4.15%. Compared with the Unet network, the improved accuracy by 1.28%, the improved precision by 1.36%, the improved recall by 1.32%, and the improved Mean Intersection over Union (MIoU) by 3.36%. The method presented in this article demonstrates superior performance in cloud detection compared to several other networks.

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Cloud Detectionof ZY-3 Remote Sensing Images Based on Fully Convolutional Neural Network and Conditional Random Field

Cited by 4 publications

References 0 publications

Key Information Extraction Algorithm of Different Types of Digital Archives for Cultural Operation and Management

Key Information Extraction Algorithm of Different Types of Digital Archives for Cultural Operation and Management

A brief analysis of remote sensing image cloud extraction method based on deep learning

Cloud Detection Method Based on Improved DeeplabV3+ Remote Sensing Image

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