Topographic Correction Model Research Articles

Influence of DEM resolution on topographic correction models using spot satellite image

Topographic correction models (TCMs) are valid on satellite image data preprocessing steps. Theillumination angle may be sensitive to different terrain slope and aspect conditions base on sun-terrain-sensorgeometry. Although the topographic correction is influenced by the sun azimuth and zenith angle, the correction resultcan be equally in the same image status. By contrast, the terrain factors change with different digital elevation model(DEM) resolution in the topographic correction equations and cause a significant effect. Slope is sensitive in ruggedterrain, and aspect is impressionable at flat surface at a coarse DEM resolution data. As the DEM resolution lead adistinct result on TCMs, this research is aimed to examine the impact of DEM resolution on the accuracy of terrainrepresentation and of the gradient determined. In this study, five TCMs, including cosine correction, C correction, SCS correction, SCS+C correction and Minnaert correction models are compared by different resolutions using SPOTimage data. The 5 meter DEM obtained from Ministry of the interior will be resampled to 10 to 500 meters to testthose topographic models sustainability on Lienhuachih Research Center. The accuracy of five topographic correctionmodels base on different DEM resolution will be evaluated by root-mean-square error (RMSE).

A neural network topographic correction method for the Lunar Reconnaissance Orbiter Diviner Christiansen feature map

Topographic correction for lunar optical and thermal infrared remote sensing images is a challenging task. Although geometrical, photometrical or albedo-dependent correction methods have been used in an attempt to eliminate topographic effects in the initial lunar reflectance and emissivity images, these methods did not perform well on lunar steep slopes, large crater interiors or walls. In this paper, a novel neural network topographic correction (NNTC) model that was trained based on the relationships between flat (< 0.1° slope angle) and rugged pixels with similar Kaguya FeO abundance and optical maturity parameters (OMATs) (relative ratio < 0.1%) was applied to the Lunar Reconnaissance Orbiter (LRO) Diviner standard Christiansen feature (CF) image. The topographic correction accuracy is 0.03 μm, and no outliers are produced. Visual comparisons with the previous topographic corrected reflectance and emissivity images indicate that the topographic effects on various slope and aspect pixels were effectively corrected. The excavated anorthosite materials in the highland crater (such as the Jackson, Giordano Bruno and Tycho) interiors, surroundings, ejecta, and ray deposits have very similar NNTC CF values. The crater structures in maria or cryptomaria are identified more clearly. In addition, the NNTC method has the potential to be a universal topographic correction method for lunar optical and thermal infrared images, and it provides reliable data sources and a new method for space weathering correction.

A comprehensive evaluation method for topographic correction model of remote sensing image based on entropy weight method

AbstractThe effect of topographic correction (TOC) has a profound influence on the quantitative application of remote sensing image. With regard to the invalid evaluation of the TOC model with such a single topographic correction assessment (TCA) method, we have selected five TCA indexes from five different perspectives: the difference in mean radiance radiometry between sunlit and shaded slopes, the cosine empirical relationship, stability, heterogeneity, and outlier ratio. The entropy weight method was used to assign weight to each TCA indexes, and the comprehensive evaluation value (CEV) of TOC for each band of remote sensing image was obtained by weighted superposition. After that, the weight of each band of the remote sensing image is determined by the entropy weight method, and the CEV of the TOC of the remote sensing image is obtained by weighting and superposition, so as to realize the effect evaluation of the six TOC models of C, SCS + C, VECA, Teillet, Minnaert, and Minnaert + SCS. The results indicate that the proposed method can effectively evaluate the correction effect of the TOC model. Results indicate that the SCS + C model has the best correction effect, while the Minnaert model performs the worst. The results generated from the Minnaert + SCS, Teillet, and Minnaert models typically show inferior quality. The SCS + C, VECA, and C models are better suited for generating images with high spectral fidelity, and these three correction models are recommended for TOCs over mountainous areas.

Evaluation of Topographic Correction Models Based on 3-D Radiative Transfer Simulation

The timely and accurate assessment of changes in mountain vegetation biomass and other parameters is of great importance to mountain ecosystem conservation. With the rapid development of remote sensing technology, hyperspectral remote sensing images have facilitated the large-scale and long-time series monitoring of environmental changes in mountainous areas. However, topographic effects cause remote sensing images of mountainous areas to be prone to spectral variations within the same land cover and spectral confusion among different land covers. This phenomenon seriously affects the accuracy of remote sensing inversions and hinders the development and application of remote sensing in mountainous areas. Numerous scholars have established various topographic correction models (TCMs) to eliminate the influence of topographic effects. Comparative evaluation of the performance of different TCMs allows us to better understand their characteristics. Most previous evaluation studies have directly applied in remote sensing images, which were limited by the changing conditions of the study area. Therefore, this letter used computer simulations to controllably evaluate six popular TCMs on hyperspectral images. The results showed that their performance varied with the spectral band, and overall, the best performance was achieved by the C correction model, followed by the sun-canopy-sensor (SCS) + C model. This letter provides a basis for the optimal selection of TCMs in complex terrains.

Optimal Minnaert topographic correction model based on land cover classification

Optimal Minnaert topographic correction model based on land cover classification

An Alternative Method for the Generation of Consistent Mapping to Monitoring Land Cover Change: A Case Study of Guerrero State in Mexico

Land cover is crucial for ecosystems and human activities. Therefore, monitoring land cover changes has become relevant in recent years. This study proposes an alternative method based on conventional change detection techniques combined with maximum likelihood (MaxLike) supervised classification of satellite images to generate consistent Land Use/Land Cover (LULC) maps. The novelty of this method is that the supervised classification is applied in an earlier stage of change detection exclusively to identified dynamics zones. The LULC categories of the stable zones are acquired from an initial date’s previously elaborated base map. The methodology comprised the use of Landsat images from 2011 and 2016, applying the Sun Canopy Sensor (SCS + C) topographic correction model enhanced through the classification of slopes, using derived topographic corrected images with NDVI, and employing Tasseled Cap (TC) Brightness-Greenness-Wetness indices and Principal Components (PCs). The study incorporated a comparative analysis of the consistency of the LULC mapping, which is generated based on control areas. The results show that the proposed method, although slightly laborious, is viable and fully automatable. The generated LULC map is accurate and robust and achieves a Kappa concordance index of 87.53. Furthermore, the boundary consistency was visually superior to the conventional classified map.

Sloping Surface Reflectance: The Best Option for Satellite-Based Albedo Retrieval Over Mountainous Areas

The estimation of satellite-based albedo highly depends on the surface reflectance (SR). In mountainous areas, three types of SRs [i.e., the virtual SR (VSR) that is retrieved from the atmospheric correction model, the topographically corrected SR (TCSR) that is retrieved from the atmospheric and topographic correction model, and the sloping SR (SSR) that is retrieved from the physically bidirectional reflectance distribution function (BRDF)-based mountain-radiative-transfer (MRT) model] are commonly used to retrieve land surface albedo (SA). However, which type of SR is the best option for SA retrieval has not yet been quantitatively addressed. This letter assessed the performance of these three types of SRs on driving SA by comparison with <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">in situ</i> albedo measurements over field sites in the Heihe River Basin, China. Our results show that these three types of albedos have consistent accuracy over flat sites with a root mean squared error (RMSE) smaller than 0.0320. Moreover, the sloping SA (SSA) retrieved from SSR shows the best agreement with <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">in situ</i> albedo measurements over rugged sites with a bias of 0.0008, RMSE of 0.0338, relative RMSE (RMSE <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">R</sub> ) of 12.92%, and correlation coefficient ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$r$ </tex-math></inline-formula> ) of 0.89, followed by the topographically corrected SA (TCSA) from TCSR with a lager bias of 0.0208, RMSE of 0.0470, RMSE <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">R</sub> of 20.24%, and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$r$ </tex-math></inline-formula> of 0.69. The virtual SA (VSA) retrieved from VSR shows the largest uncertainty than the other two types of albedos, with an RMSE of 0.0516. These results illustrate that SSR is the best option of reflectance for satellite-based albedo retrieval over mountainous areas.

The Effect of Topographic Correction on Forest Tree Species Classification Accuracy

Topographic correction can reduce the influences of topographic factors and improve the accuracy of forest tree species classification when using remote-sensing data to investigate forest resources. In this study, the Mount Taishan forest farm is the research area. Based on Landsat 8 OLI data and field survey subcompartment data, four topographic correction models (cosine model, C model, solar-canopy-sensor (SCS)+C model and empirical rotation model) were used on the Google Earth Engine (GEE) platform to carry out algorithmic data correction. Then, the tree species in the study area were classified by the random forest method. Combined with the tree species classification process, the topographic correction effects were analyzed, and the effects, advantages and disadvantages of each correction model were evaluated. The results showed that the SCS+C model and empirical rotation model were the best models in terms of visual effect, reducing the band standard deviation and adjusting the reflectance distribution. When we used the SCS+C model to correct the remote-sensing image, the total accuracy increased by 4% when using the full-coverage training areas to classify tree species and by nearly 13% when using the shadowless training area. In the illumination condition interval of 0.4–0.6, the inconsistency rate decreased significantly; however, the inconsistency rate increased with increasing illumination condition values. Topographic correction can enhance reflectance information in shaded areas and can significantly improve the image quality. Topographic correction can be used as a pretreatment method for forest species classification when the study area’s dominant tree species are in a low light intensity area.

Uncertainty Analysis for Topographic Correction of Hyperspectral Remote Sensing Images

Quantitative uncertainty analysis is generally taken as an indispensable step in the calibration of a remote sensor. A full uncertainty propagation chain has not been established to set up the metrological traceability for surface reflectance inversed from remotely sensed images. As a step toward this goal, we proposed an uncertainty analysis method for the two typical semi-empirical topographic correction models, i.e., C and Minnaert, according to the ‘Guide to the Expression of Uncertainty in Measurement (GUM)’. We studied the data link and analyzed the uncertainty propagation chain from the digital elevation model (DEM) and at-sensor radiance data to the topographic corrected radiance. We obtained spectral uncertainty characteristics of the topographic corrected radiance as well as its uncertainty components associated with all of the input quantities by using a set of Earth Observation-1 (EO-1) Hyperion data acquired over a rugged soil surface partly covered with snow. Firstly, the relative uncertainty of cover types with lower radiance values was larger for both C and Minnaert corrections. Secondly, the trend of at-sensor radiance contributed to a spectral feature, where the uncertainty of the topographic corrected radiance was poor in bands below 1400 nm. Thirdly, the uncertainty components associated with at-sensor radiance, slope, and aspect dominated the total combined uncertainty of corrected radiance. It was meaningful to reduce the uncertainties of at-sensor radiance, slope, and aspect for reducing the uncertainty of corrected radiance and improving the data quality. We also gave some suggestions to reduce the uncertainty of slope and aspect data.

Analysis of the ASTER Image Topographic Correction Method in Complex Mountain Areas

The phenomenon of “anisotropic reflection” caused by gradient and aspect changes in complex mountain areas has large disturbance on the spectral information of ground feature in remote sensing images, which will further affect the inversion of surface information based on the spectral characteristics of ground feature. Topographic correction is an effective means to weaken the topographical effect and improve the inversion accuracy of spectral reflectance of remote sensing images. Ten topographic correction methods, such as Teillet regression, VECA, Cosine, Cosine-B, Cosine-C, Cosine-T, C, SCS, SCS+C and Minnaert, were used to conduct topographic correction of ASTER remote sensing images in the complex mountain areas of Qulong, and visual effects, statistical results and spectral changes of ground features were used to quantitatively evaluate the correction results. The results show that the topographic effect in the study area is weakened to different degrees. Teillet-regression correction model has the best correction effect and the topographic effect is weakened to the maximum extent. VECA, cosine-c, and Minnaert calibration models followed. Teillet-regression model can be used as topographic correction model in complex mountain areas.

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

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.

Modelling of instrument repositioning errors in discontinuous Multi-Campaign Ground-Based SAR (MC-GBSAR) deformation monitoring

Ground-Based SAR (GBSAR) data acquisition in discontinuous mode can be useful for monitoring events whereby deformations become significant over relatively long periods. However, repositioning errors often occur in repeated campaigns and cause inaccuracies in discontinuous GBSAR deformation monitoring. This study firstly investigates the characteristics and quantifies the effects of repositioning errors. Three effects are identified: image shifts, geometric phase ramps, and topographic phase errors. The remainder of this paper then focuses on the modelling and removal of these effects. Images are automatically co-registered through amplitude-based feature matching with a sub-pixel co-registration precision. Whereas traditionally the geometric and topographic phase errors are simply considered as low-frequency signals and removed by filtering, this study presents accurate models for removing these errors. The geometric phase ramps are removed by recovering a 2nd-order polynomial function of the range and azimuth image coordinates. A linear model is introduced to correct the topographic effect without knowing the spatial baseline between different campaigns. Finally, a new combined approach is proposed by merging the geometric and topographic correction models together with a rigorous atmospheric correction model. A new interferometric processing chain is thereby developed on the basis of the proposed combined model for discontinuous Multi-Campaign GBSAR (MC-GBSAR) deformation monitoring. The feasibility of this chain is demonstrated through its application to both synthetic and real-world GBSAR data comprising both moderate and considerable repositioning errors.

Evaluation and Comparison of Topographic Correction Models Is Applied on the Series Landsat Images Using Spectrometery Data

Evaluation and Comparison of Topographic Correction Models Is Applied on the Series Landsat Images Using Spectrometery Data

Effects of Topographic Normalization on the Relationship Between Tropical Forest Biomass and Landsat TM Images

The tropical forest system of Xishuangbanna is of important value in global forest system, because it is the only green island in the tropical desert belt and the largest intact best-preserved tropical rain forest in China. Discovering the relationship between the tropical forest biomass of Xishuangbanna and Landsat TM data is very important for depicting the tropical forest biomass by using remote sensing. The study aimed to (1) discover the relationship between tropical forest biomass and Landsat Thematic Mapper (TM) images in Xishuangbanna Prefecture, Yunnan Province, China, by using forest permanent plot data and correlation analysis; (2) explore how to improve the relationship by using topographic correction models such as the Lambert cosine correction model (LCCM) and the sun–canopy–sensor topographic correction model (SCSTCM). The results revealed the tropical forest biomass characteristics and the relationship characteristics between the tropical forest biomass and Landsat TM data and its improvement by using LCCM and SCSTCM. The results are beneficial to monitoring the tropical forest biomass variation in Xishuangbanna.

A SEMI-EMPIRICAL TOPOGRAPHIC CORRECTION MODEL FOR MULTI-SOURCE SATELLITE IMAGES

Abstract. Topographic correction of surface reflectance in rugged terrain areas is the prerequisite for the quantitative application of remote sensing in mountainous areas. Physics-based radiative transfer model can be applied to correct the topographic effect and accurately retrieve the reflectance of the slope surface from high quality satellite image such as Landsat8 OLI. However, as more and more images data available from various of sensors, some times we can not get the accurate sensor calibration parameters and atmosphere conditions which are needed in the physics-based topographic correction model. This paper proposed a semi-empirical atmosphere and topographic corrction model for muti-source satellite images without accurate calibration parameters.Based on this model we can get the topographic corrected surface reflectance from DN data, and we tested and verified this model with image data from Chinese satellite HJ and GF. The result shows that the correlation factor was reduced almost 85 % for near infrared bands and the classification overall accuracy of classification increased 14 % after correction for HJ. The reflectance difference of slope face the sun and face away the sun have reduced after correction.

A quantitative method to evaluate the performance of topographic correction models used to improve land cover identification

Topographic correction methods have been widely used prior to land cover identification in sloping terrain because the topographic variation on the Earth's surface can interfere with the classifications. The topographic correction involves the normalization of brightness or surface reflectance values from the slanted to the horizontal plane. Several topographic correction models have been proposed, and a quantitative evaluation method is needed for these models because the performance can vary according to the surface cover types and spectral bands. In this study, we proposed an efficient method to evaluate the performance of topographic correction models through measuring the histogram structural similarity (HSSIM) index estimated from the sunlit and sun-shaded slope areas before and after the correction. We tested the HSSIM index by using three different land cover types derived from Landsat-8 Operational Land Imager (OLI) images and eight commonly used topographic correction models. When the proposed HSSIM index was compared with the visual analysis technique, the results matched exactly. Using the HSSIM index, the best correction methods were then determined, and the best ones included the statistical-empirical or SCS+C methods (where SCS+C refers to the sun-canopy-sensor plus C-correction) for the R, G, and B bands and the Minnaert+SCS method for the NIR, SWIR-1, and SWIR-2 bands. These results indicate that (i) the HSSIM index enables quantitative performance evaluations of topographic correction models and (ii) the HSSIM index can be used to determine the best topographic correction method for particular land cover identification applications.

An intercomparison of different topography effects on discrimination performance of fuzzy change vector analysis algorithm

Detection of snow cover changes is vital for avalanche hazard analysis and flood flashes that arise due to variation in temperature. Hence, multitemporal change detection is one of the practical mean to estimate the snow cover changes over larger area using remotely sensed data. There have been some previous studies that examined how accuracy of change detection analysis is affected by different topography effects over Northwestern Indian Himalayas. The present work emphases on the intercomparison of different topography effects on discrimination performance of fuzzy based change vector analysis (FCVA) as change detection algorithm that includes extraction of change-magnitude and change-direction from a specific pixel belongs multiple or partial membership. The qualitative and quantitative analysis of the proposed FCVA algorithm is performed under topographic conditions and topographic correction conditions. The experimental outcomes confirmed that in change category discrimination procedure, FCVA with topographic correction achieved 86.8% overall accuracy and 4.8% decay (82% of overall accuracy) is found in FCVA without topographic correction. This study suggests that by incorporating the topographic correction model over mountainous region satellite imagery, performance of FCVA algorithm can be significantly improved up to great extent in terms of determining actual change categories.

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

Topographic shadows are inevitable obstacles for the interpretation of remote sensing images covering rugged terrain. A general variational topographic correction (TC) framework is proposed in this paper by considering not only self shadows but also cast shadows. Cast shadows are first detected by integrating the radiometric and topographic features of the observed region. The cosine values of the incidence angles for the cast shadows are then corrected by the variational framework. The corrected incidence angles can be used in any traditional TC model to compensate for the shadows in mountainous regions. The proposed variational framework was utilized in eight different traditional TC models, and the results were compared with the traditional results. Images from two different regions were employed to test the framework. The results suggest that the proposed framework can raise the accuracy of shadow correction by both subjective and objective evaluations, owing to the correction of the cast shadows.

Comprehensive Atmosphere and Topographic Correction Model for Improve the Remote Sensing Data Inversion Precision of Rocky Desertification Area

Comprehensive Atmosphere and Topographic Correction Model for Improve the Remote Sensing Data Inversion Precision of Rocky Desertification Area

An improved topographic correction model based on Minnaert

The uneven distribution of solar radiation due to topographic relief can significantly change the correlation between reflectance and other features such as biomass in rugged terrain regions. In this article, we use the transfer theory to improve the Minnaert approach. After comparing topographic correction methods for Landsat 8 Operational Land Imager (OLI) and EO-1 Advanced Land Imager (ALI) imagery acquired from the mountainous region in Beijing, China, we used visual inspection, statistical analysis, and correlation analysis to evaluate the algorithms and performance of the proposed Minnaert-E approach. The results indicate that corrections based on non-Lambertian methods have better performance than those based on the Lambertian assumption. The correction performances can be ranked as the Minnaert-E, followed by the Minnaert and the SCS+C corrections, and, finally, the C-HuangWei correction, which performed the worst. We found that the Minnaert-E approach can effectively weaken the influence of terrain relief on pixels and restore the true reflectance of the pixels in the relief area. Further analysis indicates that the Minnaert-E has a better effect on image processing where the slope gradient is restricted to less than 10° or between 30° and 43°.