Albedo Recovery Research Articles

Forest Fire Effects on Landscape Snow Albedo Recovery and Decay

Surface snow albedo (SSA) darkens immediately following a forest fire, while landscape snow albedo (LSA) brightens as more of the snow-covered surface becomes visible under the charred canopy. The duration and variability of the post-fire snow albedo recovery process remain unknown beyond a few years following the fire. We evaluated the temporal variability of post-fire snow albedo recovery relative to burn severity across a chronosequence of eight burned forests burned from 2000 to 2019, using pre- and post-fire daily, seasonal, and annual landscape snow albedo data derived from the Moderate Resolution Imaging Spectroradiometer (MOD10A1). Post-fire annual LSA increased by 21% the first year following the fire and increased continually by 33% on average across all eight forest fires and burn severity classifications over the period of record (18 years following a fire). Post-fire LSA measurements increased by 63% and 53% in high and moderate burn severity areas over ten years following fire. While minimum and maximum snow albedo values increased relative to annual post-fire LSA recovery, daily snow albedo decay following fresh snowfall accelerated following forest fire during the snowmelt period. Snow albedo recovery over 10 years following fire did not resemble the antecedent pre-fire unburned forest but more resembled open meadows. The degradation of forest canopy structure is the key driver underlying the paradox of the post-fire snow albedo change (SSA vs. LSA).

A NOVEL INTRINSIC IMAGE DECOMPOSITION METHOD TO RECOVER ALBEDO FOR AERIAL IMAGES IN PHOTOGRAMMETRY PROCESSING

Abstract. Recovering surface albedos from photogrammetric images for realistic rendering and synthetic environments can greatly facilitate its downstream applications in VR/AR/MR and digital twins. The textured 3D models from standard photogrammetric pipelines are suboptimal to these applications because these textures are directly derived from images, which intrinsically embedded the spatially and temporally variant environmental lighting information, such as the sun illumination, direction, causing different looks of the surface, making such models less realistic when used in 3D rendering under synthetic lightings. On the other hand, since albedo images are less variable by environmental lighting, it can, in turn, benefit basic photogrammetric processing. In this paper, we attack the problem of albedo recovery for aerial images for the photogrammetric process and demonstrate the benefit of albedo recovery for photogrammetry data processing through enhanced feature matching and dense matching. To this end, we proposed an image formation model with respect to outdoor aerial imagery under natural illumination conditions; we then, derived the inverse model to estimate the albedo by utilizing the typical photogrammetric products as an initial approximation of the geometry. The estimated albedo images are tested in intrinsic image decomposition, relighting, feature matching, and dense matching/point cloud generation results. Both synthetic and real-world experiments have demonstrated that our method outperforms existing methods and can enhance photogrammetric processing.

Shape and Albedo Recovery by Your Phone using Stereoscopic Flash and No-Flash Photography

Shape and Albedo Recovery by Your Phone using Stereoscopic Flash and No-Flash Photography

On Variants of the Main Atmospheric Correction Formula

The article studies the accuracy of the main formula of atmospheric correction allowing us to determine albedo of a underlying (earth) surface based on radiance factor of solar light reflected by the system of atmosphere and underlying surface. The problem of atmospheric correction is considered in three-dimensional geometry with spatial non-uniformity of the underlying surface taken into account. It is demonstrated that the accuracy of albedo recovery depends on the used variant of the main formula.

Hyperspectral image clustering with Albedo recovery Fuzzy C-Means

ABSTRACT Hyperspectral image clustering is usually used for unsupervised learning in different applications. However, the traditional clustering methods have not been considered the complex relationships among neighbouring pixels. The Albedo and Shading elements can define pixel values in the HyperSpectral Images (HSIs). In HSIs, features are different from each other because of their natural physical characteristics and the physical nature of different image features can be described by the Albedo element. Therefore, in this paper, we generate the natural Albedo feature of the HSIs by applying Albedo recovery step to exploit main information from HSIs. Then, we utilized the Fuzzy C-means clustering method to cluster the natural Albedo dataset. In this paper, we propose a novel accurate Albedo Recovery based Fuzzy C-Means (ARFCM) method to cluster HSIs. In the dataset, each feature vector is processed by the Albedo recovery step to create a new feature vector. This new feature vector can describe the dataset better than the original one. Comparing clustering methods as one of the powerful clustering algorithms are widely used in the remote sensing fields of studying. The experiments conducted on several benchmark datasets demonstrated that the proposed clustering method achieves higher performance than other methods and present the efficiency and effectiveness of the proposed method. The results of experiments over different HSI datasets indicated that the proposed method could produce reliable and suitable results compared to the other methods. This shows the robustness of the proposed ARFCM algorithm over the various HSI datasets. Other methods may provide a good response in a given dataset and do not perform well in the other data. Consequently, the ARFCM method, regardless of the study area characteristics and the sensor features, always renders remarkable clustering accuracy.

Detailed Surface Geometry and Albedo Recovery from RGB-D Video under Natural Illumination.

This article presents a novel approach for depth map enhancement from an RGB-D video sequence. The basic idea is to exploit the photometric information in the color sequence to resolve the inherent ambiguity of shape from shading problem. Instead of making any assumption about surface albedo or controlled object motion and lighting, we use the lighting variations introduced by casual object movement. We are effectively calculating photometric stereo from a moving object under natural illuminations. One of the key technical challenges is to establish correspondences over the entire image set. We, therefore, develop a lighting insensitive robust pixel matching technique that out-performs optical flow method in presence of lighting variations. An adaptive reference frame selection procedure is introduced to get more robust to imperfect lambertian reflections. In addition, we present an expectation-maximization framework to recover the surface normal and albedo simultaneously, without any regularization term. We have validated our method on both synthetic and real datasets to show its superior performance on both surface details recovery and intrinsic decomposition.

Recovering variations in facial albedo from low resolution images

Recovering facial albedo from low quality face images is a challenging task which arises when face recognition is attempted in the wild. Low quality of facial images is usually caused by extrinsic factors such as low resolution and noises, and intrinsic ones such as expressions. Existing research recovers facial albedo by dealing with the extrinsic and intrinsic factors separately. However, it is more natural and potentially more useful to approach albedo recovery by removing the two effects simultaneously. In this paper, we present a novel framework which can recover facial albedo by jointly solving these for both the extrinsic and intrinsic sources of uncertainty. This framework models albedo recovery problem by a joint optimization process which alternatively (1) removes intra-personal variations and (2) performs super resolution. To deal with the intrinsic sources of albedo variability, we use a linear model. To handle extrinsic problems associated with low quality imaging, we use a sparse coding method which is applied to super resolution. The proposed method can also significantly improve the performance of face recognition and clustering in case of very low resolution and in the presence of various facial variations. Extensive experiments and comparisons are conducted on the AR and FERET face databases. Experimental results show the effectiveness of the proposed method.

Albedo recovery for hyperspectral image classification

Image intensity value is determined by both the albedo component and the shading component. The albedo component describes the physical nature of different objects at the surface of the earth, and land-cover classes are different from each other because of their intrinsic physical materials. We, therefore, recover the intrinsic albedo feature of the hyperspectral image to exploit the spatial semantic information. Then, we use the support vector machine (SVM) to classify the recovered intrinsic albedo hyperspectral image. The SVM tries to maximize the minimum margin to achieve good generalization performance. Experimental results show that the SVM with the intrinsic albedo feature method achieves a better classification performance than the state-of-the-art methods in terms of visual quality and three quantitative metrics.

Inverse Rendering and Relighting From Multiple Color Plus Depth Images.

We propose a novel relighting approach that takes advantage of multiple color plus depth images acquired from a consumer camera. Assuming distant illumination and Lambertian reflectance, we model the reflected light field in terms of spherical harmonic coefficients of the bi-directional reflectance distribution function and lighting. We make use of the noisy depth information together with color images taken under different illumination conditions to refine surface normals inferred from depth. We first perform refinement on the surface normals using the first order spherical harmonics. We initialize this non-linear optimization with a linear approximation to greatly reduce computation time. With surface normals refined, we formulate the recovery of albedo and lighting in a matrix factorization setting, involving second order spherical harmonics. Albedo and lighting coefficients are recovered up to a global scaling ambiguity. We demonstrate our method on both simulated and real data, and show that it can successfully recover both illumination and albedo to produce realistic relighting results.

Early spring post-fire snow albedo dynamics in high latitude boreal forests using Landsat-8 OLI data.

Taking advantage of the improved radiometric resolution of Landsat-8 OLI which, unlike previous Landsat sensors, does not saturate over snow, the progress of fire recovery progress at the landscape scale (< 100m) is examined. High quality Landsat-8 albedo retrievals can now capture the true reflective and layered character of snow cover over a full range of land surface conditions and vegetation densities. This new capability particularly improves the assessment of post-fire vegetation dynamics across low- to high- burn severity gradients in Arctic and boreal regions in the early spring, when the albedos during recovery show the greatest variation. We use 30 m resolution Landsat-8 surface reflectances with concurrent coarser resolution (500m) MODIS high quality full inversion surface Bidirectional Reflectance Distribution Functions (BRDF) products to produce higher resolution values of surface albedo. The high resolution full expression shortwave blue sky albedo product performs well with an overall RMSE of 0.0267 between tower and satellite measures under both snow-free and snow-covered conditions. While the importance of post-fire albedo recovery can be discerned from the MODIS albedo product at regional and global scales, our study addresses the particular importance of early spring post-fire albedo recovery at the landscape scale by considering the significant spatial heterogeneity of burn severity, and the impact of snow on the early spring albedo of various vegetation recovery types. We found that variations in early spring albedo within a single MODIS gridded pixel can be larger than 0.6. Since the frequency and severity of wildfires in Arctic and boreal systems is expected to increase in the coming decades, the dynamics of albedo in response to these rapid surface changes will increasingly impact the energy balance and contribute to other climate processes and physical feedback mechanisms. Surface radiation products derived from Landsat-8 data will thus play an important role in characterizing the carbon cycle and ecosystem processes of high latitude systems.

Trichromatic red-green-blue camera used for the recovery of albedo and reflectance of rough-textured surfaces under different illumination conditions

Photometric-stereo techniques are based on the fact that image intensity depends upon the orientation of the surface with regard to the source of the illumination and its spectral reflectance. They are of special interest when dealing with rough surfaces because they usually present shadowed regions where sudden illumination changes might be found. In the present work we introduce an extension of the four-source photometric-stereo algorithm to color images that is able to recover the surface spectral reflectance of objects captured with a red-green-blue (RGB) camera. This method allows image rendering, even for rough-textured surfaces, under different directions of the impinging illumination. In addition, the introduction of spectral recovery techniques applied to the albedo and spectral reflectance from rough surfaces offers the possibility of image rendering for scenes captured under sources of illumination differing in spectral distribution. Using albedo instead of RGB information helps to avoid any shadows or highlights that might falsify results. One of the advantages of this spectral-based photometric-stereo method is that it can recover not only the albedo values, but also the spectral reflectance spectrum of an object's surface on a pixel-by-pixel basis, as can be done with more complex hyperspectral imaging devices involving a camera coupled to an extensive set of narrowband filters.

An improved method of photometric stereo using local shape from shading

This paper presents an improved photometric stereo (PS) method by integrating it with a local shape from shading (SFS) algorithm. PS produces the initial estimate of image for the global accuracy and also provides the recovery of albedo, SFS supplies the more detailed information within each homogeneous area. The quality of depth obtained by integrating PS and SFS is compared with the real depth using absolute dept error function, and the improvement ranging from 2.3 to 14% over PS is obtained.

Fire impacts on surface heat, moisture and carbon fluxes from a tropical savanna in northern Australia

Savannas form a large fraction of the total tropical vegetation and are extremely fire prone. We measured radiative, energy and carbon exchanges over unburned and burned (both before and after low and moderate intensity fires) open forest savanna at Howard Springs, Darwin, Australia. Fire affected the radiative balance immediately following fire through the consumption of the grass-dominated understorey and blackening of the surface. Albedo was halved following fire of both intensities (from 0.12 to 0.07 and from 0.11 to 0.06 for the moderate and low intensity sites, respectively), but the recovery of albedo was dependent on the initial fire intensity. The low intensity fire caused little canopy damage with little impact on the surface energy balance and only a slight increase in Bowen ratio. However the moderate fire resulted in a comprehensive canopy scorch and almost complete leaf drop in the weeks following fire. The shutdown of most leaves within the canopy reduced transpiration and altered energy partitioning. Leaf death and shedding also resulted in a cessation of ecosystem carbon uptake and the savanna turned from a sink to a source of carbon to the atmosphere because of the continued ecosystem respiration. Post-fire, the Bowen ratio increased greatly due to large increases in sensible heat fluxes. These changes in surface energy exchange following fire, when applied at the landscape scale, may have impacts on climate through local changes in circulation patterns and changes in regional heating, precipitation and monsoon circulation.

Curved Mondrians: shading analysis of patterned objects1

Most shape‐from‐shading methods assume that surface reflectance is constant within large image regions. This assumption is violated in natural scenes with objects made from different materials. We present a more general method for recovering shape from shading, assuming that surfaces are smooth and albedo is piecewise constant, as would be the case if a Mondrian image was painted on a smooth curved surface. Our method is based on combining Brooks and Horn's method for shape recovery with the recovery of albedo using stochastic relaxation.