Multimodel Fusion Method for Cloud Detection in Satellite Laser Footprint Images

Tang, Xinming; Yao, Jiaqi; Chen, Jiyi; Li, Guoyuan; Zhang, Wenjun

doi:10.1109/lgrs.2022.3192067

Cited by 5 publications

(3 citation statements)

References 17 publications

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“…PR curves set the abscissa as recall and ordinate as precision. The corresponding definitions are as below: precision = TP TP + FP (25) recall = TP TP + FN (26) When the PR curve is close to the upper right corner, the area under the curve is large, indicating that the method has excellent detection performance.…”

Section: Evaluation Metricsmentioning

confidence: 99%

“…Besides using spectral or physical properties, since remote sensing technology, computer vision and artificial intelligence are undergoing a spurt of progress, some scholars have developed many new detection methods for cirrus detection [22][23][24][25][26][27]. Machine learning has improved the detection accuracy within a certain range; however, since the real cirrus scene lacks sufficient data to train the model, the method based on machine learning cannot achieve great detection performance in real scenes.…”

Section: Introductionmentioning

confidence: 99%

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Infrared Cirrus Detection Using Non-Convex Rank Surrogates for Spatial-Temporal Tensor

Xiao

Peng

2023

Remote Sensing

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Infrared small target detection (ISTD) plays a significant role in earth observation infrared systems. However, some high reflection areas have a grayscale similar to the target, which will cause a false alarm in the earth observation infrared system. For the sake of raising the detection accuracy, we proposed a cirrus detection measure based on low-rank sparse decomposition as a supplementary method. To better detect cirrus that may be sparsely insufficient in a single frame image, the method treats the cirrus sequence image with time continuity as a tensor, then uses the visual saliency of the image to divide the image into a cirrus region and a cirrus-free region. Considering that the classical tensor rank surrogate cannot approximate the tensor rank very well, we used a non-convex tensor rank surrogate based on the Laplace function for the spatial-temporal tensor (Lap-NRSSTT) to surrogate the tensor rank. In an effort to compute the proposed model, we used a high-efficiency optimization approach on the basis of alternating the direction method of multipliers (ADMM). Finally, final detection results were obtained by the reconstructed cirrus images with a set threshold segmentation. Results indicate that the proposed scheme achieves better detection capabilities and higher accuracy than other measures based on optimization in some complex scenarios.

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Section: Evaluation Metricsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Infrared Cirrus Detection Using Non-Convex Rank Surrogates for Spatial-Temporal Tensor

Xiao

Peng

2023

Remote Sensing

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“…In addition, we post-processed the obtained k maps in order to obtain higher accuracy k maps. TTA and CRF are used in the post-processing process to optimize the detection effect of k maps [44], [45].…”

Section: B Introduction Of Network Architecturementioning

confidence: 99%

MMPhU-Net: A Novel Multi-Model Fusion Phase Unwrapping Network for Large-Gradient Subsidence Deformation

Gao,

Yao,

Zheng

et al. 2024

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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The problem of phase unwrapping (PhU) in the large-gradient deformation areas is the bottleneck problem of interferometric synthetic aperture radar (InSAR) data processing. However, the extraction of large-gradient deformation areas is one of the key issues in coal mining deformation monitoring. Here, we propose a novel multi-model fusion PhU Network, abbreviated as MMPhU-Net, and apply it to the extraction of large-gradient deformation areas. The major advantages of MMPhU-Net are: 1) MMPhU-Net combines the advantages of different basic network models, which can improve the model convergence speed and phase gradient estimation accuracy. MMPhU-Net can improve the lack of recognition effect of a single basic model; 2) Different from existing deep learning PhU methods, MMPhU-Net directly estimates the gradient ambiguity numbers, k, so its phase gradient estimation completely breaks through the (-π, π) limitation. Therefore, MMPhU-Net can obtain ideal PhU results in large-gradient deformation areas. In addition, optimization algorithm models are used to optimize the estimation results of the multi-model fusion network. Subsequently, the obtained k and a novel two-step filtering method are combined to obtain the final PhU results. Through the verifications of simulated data sets and realistic GaoFen-3 SAR data sets, the proposed MMPhU-Net method can achieve superior excellent results than the commonly used PhU method.

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CSDUNet: Automatic Cloud and Shadow Detection from Satellite Images Based on Modified U-Net

Surya,

Abdul Rahiman

2024

J Indian Soc Remote Sens

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Multimodel Fusion Method for Cloud Detection in Satellite Laser Footprint Images

Cited by 5 publications

References 17 publications

Infrared Cirrus Detection Using Non-Convex Rank Surrogates for Spatial-Temporal Tensor

Infrared Cirrus Detection Using Non-Convex Rank Surrogates for Spatial-Temporal Tensor

MMPhU-Net: A Novel Multi-Model Fusion Phase Unwrapping Network for Large-Gradient Subsidence Deformation

CSDUNet: Automatic Cloud and Shadow Detection from Satellite Images Based on Modified U-Net

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