An adaptive atmospheric correction algorithm for the effective adjacency effect correction of submeter-scale spatial resolution optical satellite images: Application to a WorldView-3 panchromatic image

Wang, Tao; Du, Liang; Yi, Weining; Hong, Jin; Zhang, Liming; Zheng, Jianyu; Li, Chao; Ma, Xiaoshan; Zhang, Dongying; Fang, Wei; Huang, Huaguo; Wang, Anjing; Song, Bo; Zhou, Nan; Ti, Rufang; Li, Kaitao; Cui, Wenyu

doi:10.1016/j.rse.2021.112412

Cited by 17 publications

(6 citation statements)

References 32 publications

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“…The inherent challenge in applying this method is estimating the range of the adjacency effect (H Adj ), which refers to the maximum extent of the adjacency effects around the interest target, because it depends on often unknown factors [24]. Alternatively, the H Adj has been chosen arbitrarily [25,26] or defined iteratively by comparing the corrected image reflectance with the spectral responses of targets [18,27,28]. An improper choice of H Adj can lead to over-or under-correction of water reflectance due to the mischaracterization of environmental conditions around the water target.…”

Section: Introductionmentioning

confidence: 99%

Assessment of Adjacency Correction over Inland Waters Using Sentinel-2 MSI Images

et al. 2022

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Satellite remote sensing data have been used for water quality mapping, but accurate water reflectance retrieval is dependent on multiple procedures, such as atmospheric and adjacency corrections. For the latter, physical-based methods are used to minimize the adjacency effects caused by neighboring land targets close to water pixels, and implementation requires atmospheric and environmental parameters, such as aerosol optical depth and horizontal range (i.e., distance in meters) of the adjacency effect (HAdj). Generally, the HAdj is empirically defined by users and can lead to substantial errors in water reflectance when incorrectly used. In this research, a physical-based approach with three empirical methods to determine the HAdj (fixed, SIMilarity Environment Correction—SIMEC, and Adaptative Window by Proportion—AWP-Inland Water) were used to correct and characterize the adjacency effects in Sentinel-2 images over Brazilian inland waters. An interactive inversion method of the deep blue waveband estimated the aerosol loading for the atmospheric correction procedure. The results of atmospheric and adjacency corrections were validated against in-situ reflectance data. The inverted aerosol loading achieved a good agreement with in-situ measurements, especially at visible wavelengths (Mean Absolute Percentage Error—MAPE for eutrophic (~56%), bright (~80%), and dark (~288%) waters). The adjacency correction performance was near similar between the SIMEC and AWP-Inland Water methods in eutrophic and bright waters (MAPE difference < 3%). However, only the AWP-Inland Water method provided a smaller error (MAPE ~53%) for dark waters compared to the fixed (~108%) and SIMEC (~289%) methods, which shows how critical HAdj parametrization is for low water reflectance values. Simulations of different atmospheric and adjacency effects were performed, and they highlighted the importance of adjacency correction under aerosol loading higher 0.1, which is a typical aerosol loading in a dry climate season, and over extremely dark, low-reflectance waters. This paper contributes to further understanding adjacency effects in medium spatial resolution imagery of inland waters using a physical-based approach including the uncertainties in HAdj determination.

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Section: Introductionmentioning

confidence: 99%

Assessment of Adjacency Correction over Inland Waters Using Sentinel-2 MSI Images

et al. 2022

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“…These images were processed using Quantum Geographical Information system 3.2 (Q-GIS 3) and ArcGIS 10.8. Processing involved radiometric correction -done to improve the fidelity of the brightness value magnitude (Wang et al, 2021), atmospheric correction conducted to remove the effects of the atmosphere on the reflectance values of images (Hadjimitsis and Clayton, 2008) and geometric correction, a process to transform the coordinates and dimensions of a remotely sensed image to eliminate spatial distortions in the original image (Wang et al, 2021). This was followed by image subletting, implying a process of extracting only the region of interest.…”

Section: Data Collectionmentioning

confidence: 99%

Integration of traditional ecological knowledge and western science in natural resources management in the Okavango Delta, Botswana

Gondo¹

2022

J. Afr. Stud. Dev

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Since time immemorial indigenous people (IP) utilised resources to meet their cultural needs. While the long-established utilisation of these resources modified land, however, the alteration was negligible with fewer detrimental impacts on the environment. The adoption of Western Science (WS) saw people shunning the traditional ecological knowledge (TEK), resulting in accelerated resources degradation. Utilising institutional bricolage theory, this study combined remote sensing, geographical information systems, literature review and survey data to examine land use and land cover (LULC) change in three villages within the Okavango Delta and the role of TEK in resources management with a special reference to vegetation, land and water. Results revealed a widespread conversion of land and related resources and also that the adoption of formal education and religions significantly affected the utilisation of TEK in the management of resources. The study concludes that TEK is vital and is crucial in the conservation of natural resources.

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“…Physical model-based methods are derived from atmospheric correction algorithms, which need to estimate various atmospheric parameters during image acquisition. Wang et al proposed an adaptive atmospheric correction algorithm [1]. However, the atmospheric parameters of some remote sensing images cannot be estimated due to their small.…”

Section: Introductionmentioning

confidence: 99%

Color correction of remote sensing images based on style transfer

Dai,

Xu,

Zhang

et al. 2024

International Conference on Remote Sensing, Surveying, and Mapping (RSSM 2024)

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Color distortion of remote sensing image is a common problem, and the color distortion of remote sensing image is not conducive to its subsequent interpretation. The existing color correction methods have problems such as poor correction effect and distortion of texture details. To solve these problems, this paper proposes an improved CAP-VSTNet style migration network. The proposed structure-color loss function can make the texture and color of the result close to the target image. The AdaIN adaptive normalization layer is introduced to improve the correction effect without increasing the calculation amount. Experiments on public datasets show that the proposed method outperforms other comparison methods in mean square error, peak signal-to-noise ratio, structural similarity and IL-NIQE(a completely blind image quality assessment method).

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An adaptive atmospheric correction algorithm for the effective adjacency effect correction of submeter-scale spatial resolution optical satellite images: Application to a WorldView-3 panchromatic image

Cited by 17 publications

References 32 publications

Assessment of Adjacency Correction over Inland Waters Using Sentinel-2 MSI Images

Assessment of Adjacency Correction over Inland Waters Using Sentinel-2 MSI Images

Integration of traditional ecological knowledge and western science in natural resources management in the Okavango Delta, Botswana

Color correction of remote sensing images based on style transfer

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