Fuzzy statistics-based affinity propagation technique for clustering in satellite cloud image

Devika, G; Parthasarathy, S.

doi:10.1080/22797254.2018.1482731

Cited by 6 publications

(2 citation statements)

References 34 publications

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“…Unsupervised image segmentation is a promising approach for acquiring spatially expanded potential ocean-fog patches [39], [40]. It divides the images into several mutually exclusive segments with similar characteristics.…”

Section: A Sample Extractionmentioning

confidence: 99%

Improved Ocean–Fog Monitoring Using Himawari-8 Geostationary Satellite Data Based on Machine Learning With SHAP-Based Model Interpretation

Sim,

2023

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

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Ocean-fog is a type of fog that forms over the ocean and has a visibility of less than 1 km. Ocean-fog frequently causes incidents over oceanic and coastal regions; ocean-fog detection is required regardless of the time of day. Ocean-fog has distinct thermo-optical properties, and spatially and temporally extensive ocean-fog detection methods based on geostationary satellites are typically employed. Infrared channels of Himawari-8 were used to construct three machine learning models for the continuous detection of ocean-fog. In contrast, visible channels are valid only during the daytime. As control models, we used fog products from the National Meteorological Satellite Center (NMSC) and machine learning models trained by adding a visible channel. The extreme gradient boosting model utilizing infrared channels corrected ocean-fog perfectly day and night, with the highest F1 score of 97.93% and a proportion correct of 98.59% throughout the day. In contrast, the NMSC product had a probability of detection of 87.14%, an F1 score of 93.13%, and a proportion correct of 71.9%. As demonstrated by the qualitative evaluation, the NMSC product overestimates clouds over small and coarsely textured ocean-fog regions. In contrast, the proposed model distinguishes between ocean-fog, clear skies, and clouds at the pixel scale. The Shapley additive explanation analysis demonstrated that the difference between channels 14 and 7 was very useful for ocean-fog detection at night, and its extremely low values contributed significantly to distinguishing non-fog during the daytime. Channel 15, affected by water vapor absorption, contributed most to ocean-fog detection among atmospheric window channels. The research findings can be used to improve operational ocean-fog detection and forecasting.

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Section: A Sample Extractionmentioning

confidence: 99%

Improved Ocean–Fog Monitoring Using Himawari-8 Geostationary Satellite Data Based on Machine Learning With SHAP-Based Model Interpretation

Sim,

2023

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

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“…The strength is that the fully automated process also allows inexperienced users to achieve good results, and the fact that the input images are treated individually to find the best match (Zhang & Mishra, 2014). Devika and Parthasarathy (2018) proposed a fuzzy statistics-based technique for enhancing the satellite images. This algorithm results an efficient and accurate fuzzy clustering.…”

Section: Numerical and Statistical-based Approachmentioning

confidence: 99%

An investigation in satellite images based on image enhancement techniques

Ablin¹,

Sulochana

Prabin

2019

European Journal of Remote Sensing

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In Satellite Images, enhancement plays a dynamic research topic in image processing. The aim of enhancement is to process an image so that the result is more suitable than original image for specific remote sensing application. Satellite image enhancement techniques provide a lot of choices for improving the visual quality of remotely sensed images. In this research review, image fusion plays an important role, since it effectively combines auxiliary image content to enhance information contained in the individual datasets. This article provides an overview of the existing enhancement techniques. There are many techniques which have been proposed for enhancing the digital images which may be used for enhancing Satellite images. Here, a survey on various Satellite image enhancement techniques has been performed which recommends fusion-based enhancement performs superior while comparing with non-fusionbased enhancement techniques.

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Phenology based classification index method for land cover mapping from hyperspectral imagery

Sivabalan

Ramaraj

2021

Multimed Tools Appl

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Fuzzy statistics-based affinity propagation technique for clustering in satellite cloud image

Cited by 6 publications

References 34 publications

Improved Ocean–Fog Monitoring Using Himawari-8 Geostationary Satellite Data Based on Machine Learning With SHAP-Based Model Interpretation

Improved Ocean–Fog Monitoring Using Himawari-8 Geostationary Satellite Data Based on Machine Learning With SHAP-Based Model Interpretation

An investigation in satellite images based on image enhancement techniques

Phenology based classification index method for land cover mapping from hyperspectral imagery

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