A Hybrid Atmospheric Satellite Image-Processing Method for Dust and Horizontal Visibility Detection through Feature Extraction and Machine Learning Techniques

Amiri, Mehdi; Soleimani, Seyfollah

doi:10.1007/s12524-021-01460-0

Cited by 8 publications

(6 citation statements)

References 33 publications

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“…This method is able to estimate specific values of visibility, but its results are only validated by a small amount of data which cannot prove its effectiveness. Wang [24] used deep belief networks (DBN) and principal component analysis to predict visibility in shortterm and long-term sequences. However, this method has only 79% accuracy in visibility estimation.…”

Section: B Visibility Estimation Methods Based On Deep Learningmentioning

confidence: 99%

FGS-Net: A Visibility Estimation Method Based on Statistical Feature Stream in Fog Area

Liu

Zhong

et al. 2023

Preprint

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Low visibility always leads to serious traffic accidents worldwide, and it remains a tough problem despite the extensive work researched in the field of meteorology. Therefore, we propose a novel end-to-end network, FGS-Net, for visibility estimation, combining "engineered features" and "learned features" to achieve higher accuracy. Specifically, we propose a novel and effective fog region segmentation method, named ASRS(Auto Seed Region Segmentation), to segment the fog regions in the input image. Subsequently, two "specific features" (transmittance matrix, dark channel matrix) and three "common features" (contrast, average gradient, brightness) are extracted from the fog region. Next, the feature information of the fog region is statistically calculated as "engineering features" for visibility estimation. In addition, our approach uses Transformer, a classical model commonly used in the field of Natural Language Processing (NLP) to obtain the "learned features" for visibility estimation. Furthermore, in order to obtain more efficient "learned features", we embed the Coordinate Attention (CA) module in FGS-Net. Finally, to verify the effectiveness and superiority of our method, We evaluate our approach on two visibility datasets: Visibility Image Dataset Ⅰ (VID I) and Visibility Image Dataset Ⅱ (VID II), where VID I is a real scene visibility dataset and VID II is a synthetic visibility dataset. The experimental results show that our method has better performance than the classical one on these two datasets. And compared with the runner-up, it has 2.2% and 0.9% higher accuracy in VID I and VID II, respectively.

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Section: B Visibility Estimation Methods Based On Deep Learningmentioning

confidence: 99%

FGS-Net: A Visibility Estimation Method Based on Statistical Feature Stream in Fog Area

Liu

Zhong

et al. 2023

Preprint

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“…However although three deeply integrated convolutional neural network streams were connected in parallel in the VisNet, due to the sub-regions in its dateset are manually filtered, it also will lead to a large number of subjective errors. In general, due to lacking "specific features" of a foggy image, the approaches of visibility estimation based on deep learning are suffering a low accuracy [26,27].…”

Section: B Visibility Estimation Methods Based On Deep Learningmentioning

confidence: 99%

STCN-Net: A Novel Multi-Feature Stream Fusion Visibility Estimation Approach

Liu¹,

Chang

Li³

et al. 2022

IEEE Access

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Low visibility always leads to serious traffic accidents worldwide, although extensive works are studied to deal with the estimation of visibility in meteorology areas, it is still a tough problem. Deep learning-based visibility estimation methods, it has low accuracy due to lacking "specific features" of the foggy images. Meanwhile, physical modelbased visibility estimation methods are only applicable to some specific scenes due to its high requirements for extra auxiliary parameters. Therefore, This paper proposes a novel end-to-end framework named STCN-Net for visibility estimation, which combined the "engineered features" and "learned features" to achieve higher accuracy. Specifically, a novel 3D multi-feature stream Matrix, named DDT, is designed for visibility estimation, which is consisted of a transmittance matrix, a dark channel matrix, and a depth matrix. Unlike traditional deep learning methods which only use convolutional neural networks (CNN) to deal with the input data or images, our method combines CNN and Transformer to process the input data or images. In STCN-Net, Swin-Transformer(Swin-T) module takes the original image as input while the CNN module takes the DDT matrix as input. Moreover, in order to integrate different feature information from the CNN and Swin-T, we embed a Coordinate Attention (CA) module in STCN-Net. Finally, two visibility datasets: Visibility Image Dataset Ⅰ (VID I) and Visibility Image Dataset Ⅱ (VID II) were constructed for evaluation where VID I is a real scene visibility dataset and VID II is a synthetic visibility dataset. The experimental results show that our method has better performance than classical methods on the two datasets. And compared with the runner-up, it has 2.1% more accuracy in VID I and 0.5% in VID II.

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“…Proposed image features for pattern search have included gray features [1], texture features [2], color features [3], and convolution features [4], where color features provide universally successful cues for identification of individuals in many applications. These have been extensively utilized in pattern search, computer vision, and image processing [5][6][7]. Analysis of color features plays a vital role in various tasks, including object matching, background subtraction, video tracking, and image retrieval [8][9][10].…”

Section: Introductionmentioning

confidence: 99%

Absent Color Indexing: Histogram-Based Identification Using Major and Minor Colors

2022

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The color histogram is a statistical behavior for robust pattern search or matching; however, difficulties have arisen in using it to discriminate among similar objects. Our method, called absent color indexing (ABC), describes how to use absent or minor colors as a feature in order to solve problems while robustly recognizing images, even those with similar color features. The proposed approach separates a source color histogram into apparent (AP) and absent (AB) color histograms in order to provide a fair way of focusing on the major and minor contributions together. A threshold for this separation is automatically obtained from the mean color histogram by considering the statistical significance of the absent colors. After these have been separated, an inversion operation is performed to reinforce the weight of AB. In order to balance the contributions of the two histograms, four similarity measures are utilized as candidates for combination with ABC. We tested the performance of ABC in terms of the F-measure using different similarity measures, and the results show that it is able to achieve values greater than 0.95. Experiments on Mondrian random patterns verify the ability of ABC to distinguish similar objects by margin. The results of extensive experiments on real-world images and open databases are presented here in order to demonstrate that the performance of our relatively simple algorithm remained robust even in difficult cases.

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A Hybrid Atmospheric Satellite Image-Processing Method for Dust and Horizontal Visibility Detection through Feature Extraction and Machine Learning Techniques

Cited by 8 publications

References 33 publications

FGS-Net: A Visibility Estimation Method Based on Statistical Feature Stream in Fog Area

FGS-Net: A Visibility Estimation Method Based on Statistical Feature Stream in Fog Area

STCN-Net: A Novel Multi-Feature Stream Fusion Visibility Estimation Approach

Absent Color Indexing: Histogram-Based Identification Using Major and Minor Colors

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