A general variational framework considering cast shadows for the topographic correction of remote sensing imagery

Li, Huifang; Xu, Liming; Shen, Huanfeng; Zhang, Liangpei

doi:10.1016/j.isprsjprs.2016.03.021

Cited by 35 publications

(29 citation statements)

References 29 publications

(32 reference statements)

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“…This is because the study area is dominated by coniferous forests that have homogeneous canopy structures. The LPE-SEC model, as a statistical method based on mean approximation, can generate more accurate results than the other models [28]. The LPE-MIN method has the poorest correction accuracy, which is consistent with the findings of [28] that claimed larger solar elevation angles might bring better performances.…”

Section: Comparison Between Traditional Tc Models and Tc Models Consisupporting

confidence: 81%

“…The LPE-SEC model, as a statistical method based on mean approximation, can generate more accurate results than the other models [28]. The LPE-MIN method has the poorest correction accuracy, which is consistent with the findings of [28] that claimed larger solar elevation angles might bring better performances. However, some researchers suggested that the poor results from the solar elevation angle smaller than 25 • [28] should be attributed to the different image acquisition times (such as in August and December).…”

Section: Comparison Between Traditional Tc Models and Tc Models Consisupporting

confidence: 81%

“…Empirical models are based on the statistical relationship between the reflectance of a location or pixel and the cosine of the relative solar incident angle [25,26]. They can be implemented without using ancillary data, but some models have parameters with no physical significance [27,28], such as statistical models. Semiempirical models adopt a digital elevation model (DEM) to represent the variation of solar illumination among different slopes [29].…”

Section: Introductionmentioning

confidence: 99%

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Improving the Estimation of Forest Carbon Density in Mountainous Regions Using Topographic Correction and Landsat 8 Images

et al. 2019

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Spectral reflectance distortions caused by terrain and solar illumination seriously reduce the accuracy of mapping forest carbon density, especially in mountainous regions. Many models have been developed for mitigating or eliminating the terrain effects on the quality of remote sensing images in hilly and mountainous areas. However, these models usually use global parameters, which may lead to overcorrections for regions with poor illumination and steep slopes. In this study, we present a local parameter estimation (LPE) method based on a pixel-moving window for topographic correction (TC), which can be considered as a general optimization framework for most semiempirical TC models. We set seven kernel sizes for the presented framework, which are 15 pixels, 25 pixels, 50 pixels, 100 pixels, 250 pixels, 500 pixels, and 1000 pixels, respectively. The proposed method was then applied to four traditional TC models, Minnaert (MIN), C Correction (CC), Sun Canopy Sensor + C (SCSC) and Statistical Empirical Correction (SEC), to form four new TC models. These new models were used to estimate forest carbon density of a mountainous area in Southern China using field plot data and a Landsat 8 image. Four evaluation methods, including correlation analysis, the stability of land covers, comparison of reflectance between sunlit and shaded slopes, and accuracy assessment of forest carbon density, were employed to evaluate the contributions of moving window sizes, and assess the performance of the TC models for forest carbon density estimation. The results show that the four TC models with LPE perform much better than the traditional TC models in reducing the topographic effects and improving the estimation accuracy of forest carbon density for the study area. Among the traditional TC models, SEC performs slightly better than SCSC, CC, and MIN. Therefore, the SEC-based model with LPE, that is, LPE-SEC, gets greater R2 and smaller relative RMSE values in estimating forest carbon density than other models. Moreover, all the means of the predicted forest carbon density values fall in the confidence interval of the validation data at a significant level of 0.05. Overall, this study implies that the proposed method with LPE provides great potential to improve the performance of TC and forest carbon density estimation for the study area. It is expected that the improved TC method can be applied to other mountainous areas to improve the quality of remotely sensed images.

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Section: Comparison Between Traditional Tc Models and Tc Models Consisupporting

confidence: 81%

Section: Comparison Between Traditional Tc Models and Tc Models Consisupporting

confidence: 81%

Section: Introductionmentioning

confidence: 99%

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Improving the Estimation of Forest Carbon Density in Mountainous Regions Using Topographic Correction and Landsat 8 Images

et al. 2019

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“…In addition, commonly applied topographic-correction methods do not provide a fully comprehensive ARC solution. Topographic-correction approaches can be organized into three categories: spectral-feature extraction methods, empirical modeling, and radiation-transfer modeling [49][50][51]. Empirical methods can be divided into those based on a Lambertian assumption of surface reflectance and those based on a non-Lambertian assumption.…”

Section: Arcmentioning

confidence: 99%

“…A number of authors have indicated the superior performance of the SEC when comparing topographic-correction algorithms, e.g., [59][60][61][62]. As with other topographic correction methods reported in the literature, different algorithms have been used to represent the SEC, e.g., [13,51,63].…”

Section: Empirical Correctionsmentioning

confidence: 99%

Theoretical Evaluation of Anisotropic Reflectance Correction Approaches for Addressing Multi-Scale Topographic Effects on the Radiation-Transfer Cascade in Mountain Environments

et al. 2019

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Research involving anisotropic-reflectance correction (ARC) of multispectral imagery to account for topographic effects has been ongoing for approximately 40 years. A large body of research has focused on evaluating empirical ARC methods, resulting in inconsistent results. Consequently, our research objective was to evaluate commonly used ARC methods using first-order radiation-transfer modeling to simulate ASTER multispectral imagery over Nanga Parbat, Himalaya. Specifically, we accounted for orbital dynamics, atmospheric absorption and scattering, direct- and diffuse-skylight irradiance, land cover structure, and surface biophysical variations to evaluate their effectiveness in reducing multi-scale topographic effects. Our results clearly reveal that the empirical methods we evaluated could not reasonably account for multi-scale topographic effects at Nanga Parbat. The magnitude of reflectance and the correlation structure of biophysical properties were not preserved in the topographically-corrected multispectral imagery. The CCOR and SCS+C methods were able to remove topographic effects, given the Lambertian assumption, although atmospheric correction was required, and we did not account for other primary and secondary topographic effects that are thought to significantly influence spectral variation in imagery acquired over mountains. Evaluation of structural-similarity index images revealed spatially variable results that are wavelength dependent. Collectively, our simulation and evaluation procedures strongly suggest that empirical ARC methods have significant limitations for addressing anisotropic reflectance caused by multi-scale topographic effects. Results indicate that atmospheric correction is essential, and most methods failed to adequately produce the appropriate magnitude and spatial variation of surface reflectance in corrected imagery. Results were also wavelength dependent, as topographic effects influence radiation-transfer components differently in different regions of the electromagnetic spectrum. Our results explain inconsistencies described in the literature, and indicate that numerical modeling efforts are required to better account for multi-scale topographic effects in various radiation-transfer components.

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Estimating millimeter-scale surface roughness of rock outcrops using drone-flyover structure-from-motion (SfM) photogrammetry by applying machine learning model

Nakamura,

Kioka,

Egawa

et al. 2024

Earth Sci Inform

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A quantitative outcrop survey was conducted in three coastal areas in Japan to estimate the relationship between the surface morphology and visual information of well-exposed rocks using photogrammetry of drone yovers. We generated three-dimensional digital outcrop models in the study areas to produce the hue, saturation, value (HSV) color space images and digital elevation model (DEM) data, together with terrain ruggedness index (TRI) computed from the DEM data. Using the data, we examined whether our machine learning model could predict the millimeter-scale surface ruggedness of the given rock outcrop.In the prediction, one of the three studied outcrops was selected as training data, and various patterns of choices from the available georeferenced visual information (i.e., coordinates, H, S, V) and TRI data were used as explanatory and response variables, respectively. The results revealed that our model provided reasonable quantitative predictions of surface ruggedness. In addition, our predictions worked well even in the presence of cast shadows on the studied outcrops, suggesting that the shadow effects were likely negligible. Our ndings emphasize that the HSV color space data acquired by drone-yover photogrammetry alone can quantitively predict the millimeter-scale surface ruggedness of outcrops, facilitating the acquisition of high-resolution surface morphology data without DEMs. This achievement can be a step forward in better acquiring surface geological information, the quality of which is often compromised by the person carrying out the survey.

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A general variational framework considering cast shadows for the topographic correction of remote sensing imagery

Cited by 35 publications

References 29 publications

Improving the Estimation of Forest Carbon Density in Mountainous Regions Using Topographic Correction and Landsat 8 Images

Improving the Estimation of Forest Carbon Density in Mountainous Regions Using Topographic Correction and Landsat 8 Images

Theoretical Evaluation of Anisotropic Reflectance Correction Approaches for Addressing Multi-Scale Topographic Effects on the Radiation-Transfer Cascade in Mountain Environments

Estimating millimeter-scale surface roughness of rock outcrops using drone-flyover structure-from-motion (SfM) photogrammetry by applying machine learning model

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