2025
Special Issue: Remote Sensing based Intelligent Visual Analytics for Real-time Environmental and Earth Monitoring Systems

Special Issue: Remote Sensing based Intelligent Visual Analytics for Real-time Environmental and Earth Monitoring Systems

Adaptive Pan-sharpening via Contourlet Decomposition and Anisotropic Diffusion for Multispectral-Panchromatic 2D signals

Published 2025-12-31

  • Suraj Thakur
    • ,
  • Ayush Dogra
    • ,
  • Shalli Rani
    • ,
  • Bhawna Goyal
    • ,
  • M Sravan Kumar Reddy

Suraj Thakur
Chitkara University Institute of Engineering and Technology, Rajpura, Punjab, 140401, India.

Ayush Dogra
Chitkara University Institute of Engineering and Technology, Rajpura, Punjab, 140401, India.

Shalli Rani
Chitkara University Institute of Engineering and Technology, Rajpura, Punjab, 140401, India.

Bhawna Goyal
Department of Engineering, Marwadi University Research Centre, Marwadi University, Rajkot, Gujarat, India.

M Sravan Kumar Reddy
RGM College of Engineering and Technology (Autonomous), Nandyal Dist., AP, India.

Abstract

Pan-sharpening is now indispensable in remote-sensing computing by means of its ability to render not only high-spatial-resolution images but also precise spectral fidelity to the images. These features are essential for competent environmental measurement and investigation. The paper presents a contourlet-based pan-sharpening algorithm, which aims to combine the high spatial resolution of the panchromatic (PAN) images with the multispectral (MS) ones to extract high spectral resolution of the fused image. The algorithm tends to enhance the details of the space and reduce the spectral distortion of a multi-stage fusion procedure to some extent. The structure initiates with contourlet decomposition images captured by both PAN and MS, thus resembling directional and multi-scale structure. At the high-frequency sub-bands, an anisotropic filter is used to reduce noise, leaving salient edges intact, and a maximum-absolute selection rule is applied iteratively to inject spatial details effectively. In order to have spectral fidelity and to maintain global contrast on low-frequency components, saliency maps are used in conjunction with adaptive weight maps. The sub-bands reconstructed progressively give a high-resolution, artifact-minimal fused image. The proposed framework is validated through benchmark remote sensing datasets. The proposed methodology is based on objective metrics. Performance evaluation based on a benchmark remote sensing dataset demonstrates that the proposed methodology outperforms existing approaches in terms of sharper edges, contrast, and spectral features and offers an efficient solution for environmental applications.

Keywords

Pan-sharpening, , anisotropic, fused image, anisotropic

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