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Segmentation on remote sensing imagery for atmospheric air pollution using divergent differential evolution algorithm

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Abstract

Air pollution is a global issue causing major health hazards. By proper monitoring of air quality, actions can be taken to control air pollution. Satellite remote sensing is an effective way to monitor global atmosphere. Various sensors and instruments fitted to satellites and airplanes are used to obtain the radar images. These images are quite complex with various wavelength differentiated by very close color differences. Clustering of such images based on its wavelengths can provide the much-needed relief in better understanding of these complex images. Such task related to image segmentation is a universal optimization issue that can be resolved with evolutionary techniques. Differential Evolution (DE) is a fairly fast and operative parallel search algorithm. Though classical DE algorithm is popular, there is a need for varying the mutation strategy for enhancing the performance for varied applications. Several alternatives of classical DE are considered by altering the trial vector and control parameter. In this work, a new alteration of DE technique labeled as DiDE (Divergent Differential Evolution Algorithm) is anticipated. The outcomes of this algorithm were tested and verified with the traditional DE techniques using fifteen benchmark functions. The new variant DiDE exhibited much superior outcomes compared to traditional approaches. The novel approach was then applied on remote sensing imagery collected form TEMIS, a web based service for atmospheric satellite images and the image was segmented. Fuzzy Tsallis entropy method of multi-level thresholding technique is applied over DiDE to develop image segmentation. The outcomes obtained were related with the segmented results using traditional DE and the outcome attained was found to be improved profoundly. Experimental results illustrate that by acquainting DiDE in multilevel thresholding, the computational delay was greatly condensed and the image quality was significantly improved.

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Data availability

The weather radar image dataset that support the findings of this study are available in TEMIS repository which is accessed through www.temis.nl/index.php link.

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Authors and Affiliations

  1. Machine Intelligence Research Labs (MIR Labs), Scientific Network for Innovation and Research Excellence, Auburn, WA, 98071, USA

    Meera Ramadas & Ajith Abraham

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  1. Meera Ramadas
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  2. Ajith Abraham
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Correspondence to Meera Ramadas.

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Ramadas, M., Abraham, A. Segmentation on remote sensing imagery for atmospheric air pollution using divergent differential evolution algorithm. Neural Comput & Applic 35, 3977–3990 (2023). https://doi.org/10.1007/s00521-022-07922-x

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  • DOI: https://doi.org/10.1007/s00521-022-07922-x

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