Frontiers in Remote Sensing (Jan 2025)
Improving remote sensing dehazing quality through local hybrid correction and optimization of atmospheric attenuation model based on wavelength
Abstract
Near-ground remote sensing image dehazing is crucial for accurately monitoring land resources. An effective dehazing technique and a precise atmospheric attenuation model are fundamental to acquiring real-time ground data with high fidelity. The dark channel prior (DCP) is a widely used method for improving visibility in hazy conditions, but it often results in reduced image clarity and artifacts, that limit its practical utility. To address these limitations, we propose a novel hybrid correction method, local hybrid correction (LHC), which integrates gamma correction for high-contrast regions and logarithmic correction for low-contrast regions within a dehazed image. We calculated the cumulative distribution function (CDF) of Weber contrast for the dehazed image and analyzed the impact of different contrast thresholds on the effectiveness of improving image clarity and reducing artifacts. Our results showed that a contrast threshold corresponding to the 90% CDF significantly improved image sharpness and reduced artifacts compared to other thresholds. Furthermore, LHC outperformed both gamma and logarithmic corrections in terms of image clarity and artifact reduction, even after applying additional post-processing methods such as multi-exposure fusion and guided filtering. The quantitative analysis of the dehazed images, using gray-level co-occurrence matrix (GLCM) metrics, indicated that the LHC method offered a balanced advantage in enhancing image details, texture consistency, and structural complexity. Specifically, images processed by LHC exhibit moderate contrast and correlation, low homogeneity and high entropy, all these made the LHC method a very suitable solution for near-ground remote sensing tasks that required enhanced image detail and reduced artifacts. We also examined the atmospheric attenuation coefficient, observing that it increased with distance, deviating progressively from empirical values, this phenomenon underscored the complex effects of atmospheric scattering on dehazing accuracy, especially at extended ranges. Additionally, we refined the transmittance attenuation model using light reflection at the 550 nm wavelength from verdant landscapes, which improved the model’s alignment with real-world conditions. This approach was not only effective for this wavelength but could adapt to other wavelengths in future studies. Overall, our research advanced the precision of remote sensing dehazing techniques, promising improved decision-making for land resource management and a variety of environmental applications.
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