Satellite Fog Detection at Dawn and Dusk Based on the Deep Learning Algorithm under Terrain-Restriction

Ran, Yinze; Ma, Hongwei; Liu, Zengwei; Wu, Xiaoshan; Li, Yanan; Feng, Huihui

doi:10.3390/rs14174328

Cited by 5 publications

(3 citation statements)

References 35 publications

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“…At dusk, the algorithm can achieve relatively complete detection of the fog on both sides of the dusk line. Compared with the deep learning method, ST-ViBe was free from sample interference, with faster detection speeds and a more stable detection effect [17].…”

Section: Discussionmentioning

confidence: 99%

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A Novel ST-ViBe Algorithm for Satellite Fog Detection at Dawn and Dusk

Liu

Jiang

et al. 2023

Remote Sensing

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Satellite remote sensing provides a potential technology for detecting fog at dawn and dusk on a large scale. However, the spectral characteristics of fog at dawn and dusk are similar to those of the ground surface, which makes satellite-based fog detection difficult. With the aid of time-series datasets from the Himawari-8 (H8)/AHI, this study proposed a novel algorithm of the self-adaptive threshold of visual background extractor (ST-ViBe) model for satellite fog detection at dawn and dusk. Methodologically, the background model was first built using the difference between MIR and TIR (BTD) and the local binary similarity patterns (LBSP) operator. Second, BTD and scale invariant local ternary pattern (SILTP) texture features were coupled to form scene factors, and the detection threshold of each pixel was determined adaptively to eliminate the influence of the solar zenith angles. The background model was updated rapidly by accelerating the updating rate and increasing the updating quantity. Finally, the residual clouds were removed with the traditional cloud removal method to achieve accurate detection of fog at dawn and dusk over a large area. The validation results demonstrated that the ST-ViBe algorithm could detect fog at dawn and dusk precisely, and on a large scale. The probability of detection, false alarm ratio, and critical success index were 72.5%, 18.5%, 62.4% at dawn (8:00) and 70.6%, 33.6%, 52.3% at dusk (17:00), respectively. Meanwhile, the algorithm mitigated the limitations of the traditional algorithms, such as illumination mutation, missing detection, and residual shadow. The results of this study could guide satellite fog detection at dawn and dusk and improve the detection of similar targets.

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Section: Discussionmentioning

confidence: 99%

“…It is hypothesized to be capable of achieving fog detection at high solar zenith angles. However, there are few relevant studies at high solar zenith angles [17,18].…”

Section: Introductionmentioning

confidence: 99%

A Novel ST-ViBe Algorithm for Satellite Fog Detection at Dawn and Dusk

Liu

Jiang

et al. 2023

Remote Sensing

Self Cite

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“…Deep CNNs has also been applied and developed in the field of sea fog detection and prediction. Ran et al [13] developed an algorithm for sea fog detection during morning and evening hours in the framework of deep learning combined with terrain constraints. Zhu et al [14] used U-Net deep learning model combined with PCA to accomplish effective sea fog detection.…”

Section: A Deep Cnnsmentioning

confidence: 99%

Cloud Image Retrieval for Sea Fog Recognition (CIR-SFR) Using Double Branch Residual Neural Network

Jin

Yao

et al. 2023

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

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Sea fog is a common weather phenomenon at sea, which reduces visibility and causes tremendous hazards to marine transportation, marine fishing and other maritime operations. Traditional sea fog monitoring methods have enormous difficulties in characterizing the diversity of sea fog and distinguishing sea fog from low-level clouds. Thus, we propose a cloud image retrieval method for sea fog recognition (CIR-SFR) in a deep learning framework by combining the advantages of metric learning. CIR-SFR includes the feature extraction module and the retrieval-based sea fog recognition module. The feature extraction module adopts the Double Branch Residual Neural Network (DBRNN) to comprehensively extract the global and local features of cloud images. By introducing local branches and using activation masks, DBRNN can focus on regions of interest in cloud images. Moreover, cloud image features are projected into the semantic space by introducing Multi-Similarity Loss, which effectively improves the discrimination ability of sea fog and lowlevel clouds. For the retrieval-based sea fog recognition module, similar cloud images are retrieved from the cloud image dataset according to the distance in the feature space, and accurate sea fog recognition results are obtained by counting the percentage of various cloud image types in the retrieval results. To evaluate the sea fog recognition system, we establish a dataset of 2544 cloud images including clear sky, low-level cloud, medium high cloud and sea fog. Experimental results show that the proposed method outperforms the traditional methods in sea fog recognition, which provides a new way for sea fog recognition.

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Sunflower mission concept for high-altitude earth observation in LEO via nanosatellite with pyramidal solar sail

Berthet,

Suzuki

2023

Acta Astronautica

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Satellite Fog Detection at Dawn and Dusk Based on the Deep Learning Algorithm under Terrain-Restriction

Cited by 5 publications

References 35 publications

A Novel ST-ViBe Algorithm for Satellite Fog Detection at Dawn and Dusk

A Novel ST-ViBe Algorithm for Satellite Fog Detection at Dawn and Dusk

Cloud Image Retrieval for Sea Fog Recognition (CIR-SFR) Using Double Branch Residual Neural Network

Sunflower mission concept for high-altitude earth observation in LEO via nanosatellite with pyramidal solar sail

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