Walrus Optimization Algorithm for panchromatic and multispectral image fusion

IF 3.8 Q2 ENVIRONMENTAL SCIENCES

Remote Sensing Applications-Society and Environment Pub Date : 2025-04-01 DOI:10.1016/j.rsase.2025.101562

R. Dileep , J. Jayanth , A.L. Choodarathnakar , H.K. Ravikiran

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引用次数: 0

Abstract

The fusion of Panchromatic (PAN) and Multispectral (MS) images is critical for enhancing spatial and spectral resolution in remote sensing, especially in agriculture. However, traditional methods face limitations, such as spectral distortion in the Multiplicative Transform (MT), over-enhanced spatial details in the Brovey Transform (BT), and trade-offs in wavelet-based approaches. This study introduces the Walrus Optimization Algorithm (WAOA), which dynamically optimizes spectral weights and spatial adjustments while refining wavelet coefficients to balance spatial and spectral quality. Comparative analysis was conducted using BT, MT, Wavelet Transform (WT), and their WaOA-optimized versions on PAN and MS images from one agricultural region. Metrics such as Average Difference (AD), Root Mean Squared Error (RMSE), correlation coefficient (r), and Structural Similarity Index (SSIM) were evaluated. WT-WaOA emerged as the best method with an AD of 0.00007, RMSE of 0.03882, SSIM of 0.84561, and band means closest to the MS benchmark (e.g., Band 1: 89.57 for WT-WaOA vs. 99.86 for MS). SD analysis highlights WT-WaOA's ability to preserve contrast and variability, ranking second only to MS and PAN across all bands. These findings position WT-WaOA as a reliable fusion method for balanced spatial and spectral detail integration.

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用于全色和多光谱图像融合的海象优化算法

摘要全色（PAN）和多光谱（MS）图像的融合是提高遥感尤其是农业遥感空间和光谱分辨率的关键。然而，传统方法面临着诸多限制，如乘性变换（MT）中的频谱失真、Brovey变换（BT）中空间细节的过度增强以及基于小波的方法中的权衡等。本文介绍了海象优化算法（Walrus Optimization Algorithm， WAOA），该算法在细化小波系数的同时动态优化频谱权值和空间调整，以平衡空间和光谱质量。利用BT、MT、小波变换（Wavelet Transform， WT）及其waoa优化版本对某农业区的PAN和MS图像进行对比分析。评估指标如平均差（AD）、均方根误差（RMSE）、相关系数(r)和结构相似指数（SSIM）。WT-WaOA被认为是最佳方法，其AD为0.00007，RMSE为0.03882，SSIM为0.84561，波段均值最接近MS基准（例如，波段1:WT-WaOA为89.57，MS为99.86）。SD分析表明，WT-WaOA具有保持对比度和可变性的能力，在所有波段中仅次于MS和PAN。这些发现表明WT-WaOA是平衡空间和光谱细节整合的可靠融合方法。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Remote Sensing Applications-Society and Environment Multiple-

CiteScore

8.00

自引率

8.50%

发文量

204

审稿时长

65 days

期刊介绍： The journal ''Remote Sensing Applications: Society and Environment'' (RSASE) focuses on remote sensing studies that address specific topics with an emphasis on environmental and societal issues - regional / local studies with global significance. Subjects are encouraged to have an interdisciplinary approach and include, but are not limited by: " -Global and climate change studies addressing the impact of increasing concentrations of greenhouse gases, CO2 emission, carbon balance and carbon mitigation, energy system on social and environmental systems -Ecological and environmental issues including biodiversity, ecosystem dynamics, land degradation, atmospheric and water pollution, urban footprint, ecosystem management and natural hazards (e.g. earthquakes, typhoons, floods, landslides) -Natural resource studies including land-use in general, biomass estimation, forests, agricultural land, plantation, soils, coral reefs, wetland and water resources -Agriculture, food production systems and food security outcomes -Socio-economic issues including urban systems, urban growth, public health, epidemics, land-use transition and land use conflicts -Oceanography and coastal zone studies, including sea level rise projections, coastlines changes and the ocean-land interface -Regional challenges for remote sensing application techniques, monitoring and analysis, such as cloud screening and atmospheric correction for tropical regions -Interdisciplinary studies combining remote sensing, household survey data, field measurements and models to address environmental, societal and sustainability issues -Quantitative and qualitative analysis that documents the impact of using remote sensing studies in social, political, environmental or economic systems