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A novel Venus’ visible image processing neoteric workflow for improved planetary surface feature analysis

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Abstract

The article presents a novel methodology that comprises of end-to-end Venus’ visible image processing neoteric workflow. The visible raw image is denoised using Tri-State median filter with background dark subtraction, and then enhanced using Contrast Limited Adaptive Histogram Equalization. The multi-modal image registration technique is developed using Segmented Affine Scale Invariant Feature Transform and Motion Smoothness Constraint outlier removal for co-registration of Venus’ visible and radar image. A novel image fusion algorithm using guided filter is developed to merge multi-modal Visible-Radar Venus’ image pair for generating the fused image. The Venus’ visible image quality assessment is performed at each processing step, and results are quantified and visualized. In addition, fuzzy color-coded segmentation map is generated for crucial information retrieval about Venus’ surface feature characteristics. It is found that Venus’ fused image clearly demarked planetary morphological features and validated with publicly available Venus’ radar nomenclature map.

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Data availability

The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.

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Authors and Affiliations

  1. Signal and Image Processing Group, Space Applications Centre, Indian Space Research Organisation (ISRO), Ahmedabad, Gujarat, India

    Indranil Misra, SManthira Moorthi & Debajyoti Dhar

  2. Department of Computer Science and Information Systems, Birla Institute of Technology and Science, Pilani, Rajasthan, India

    Mukesh Kumar Rohil

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  1. Indranil Misra
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Correspondence to Indranil Misra.

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Misra, I., Rohil, M.K., Moorthi, S. et al. A novel Venus’ visible image processing neoteric workflow for improved planetary surface feature analysis. Pattern Anal Applic 27, 31 (2024). https://doi.org/10.1007/s10044-024-01253-4

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