Radiometric Transformations Research Articles

Computational Optimization of the 3D Least-Squares Matching Algorithm by Direct Calculation of Normal Equations

3D least-squares matching is an algorithm that allows to measure subvoxel-precise displacements between two data sets of computed tomography voxel data. The determination of precise displacement vector fields is an important tool for deformation analyses in in-situ X-ray micro-tomography time series. The goal of the work presented in this publication is the development and validation of an optimized algorithm for 3D least-squares matching saving computation time and memory. 3D least-squares matching is a gradient-based method to determine geometric (and optionally also radiometric) transformation parameters between consecutive cuboids in voxel data. These parameters are obtained by an iterative Gauss-Markov process. Herein, the most crucial point concerning computation time is the calculation of the normal equations using matrix multiplications. In the paper at hand, a direct normal equation computation approach is proposed, minimizing the number of computation steps. A theoretical comparison shows, that the number of multiplications is reduced by 28% and the number of additions by 17%. In a practical test, the computation time of the 3D least-squares matching algorithm was proven to be reduced by 27%.

Assessment of DSM Based on Radiometric Transformation of UAV Data.

Unmanned Aerial Vehicle (UAV) is one of the latest technologies for high spatial resolution 3D modeling of the Earth. The objectives of this study are to assess low-cost UAV data using image radiometric transformation techniques and investigate its effects on global and local accuracy of the Digital Surface Model (DSM). This research uses UAV Light Detection and Ranging (LIDAR) data from 80 m and UAV Drone data from 300 and 500 m flying height. RAW UAV images acquired from 500 m flying height are radiometrically transformed in Matrix Laboratory (MATLAB). UAV images from 300 m flying height are processed for the generation of 3D point cloud and DSM in Pix4D Mapper. UAV LIDAR data are used for the acquisition of Ground Control Points (GCP) and accuracy assessment of UAV Image data products. Accuracy of enhanced DSM with DSM generated from 300 m flight height were analyzed for point cloud number, density and distribution. Root Mean Square Error (RMSE) value of Z is enhanced from ±2.15 m to ±0.11 m. For local accuracy assessment of DSM, four different types of land covers are statistically compared with UAV LIDAR resulting in compatibility of enhancement technique with UAV LIDAR accuracy.

Matched Manifold Detection for Group-Invariant Registration and Classification of Images

Consider the set of possible observations turned out by geometric and radiometric transformations of an object. This set is generally a manifold in the ambient space of observations. It has been shown [1] that in those cases where the geometric deformations are affine and the radiometric deformations are monotonic, the radiometry invariant universal manifold embedding (RIUME) provides a mapping from the orbit of deformed observations to a single low dimensional linear subspace of Euclidean space. This linear subspace is invariant to the geometric and radiometric transformations and hence is a representative of the orbit. It thus naturally serves as an invariant statistic for solving problems of joint transformation estimation and detection or classification. In the unsupervised detection problem, subspaces evaluated from two observations are tested for the similarity of the observed object and their relative transformation is estimated from the RIUME matrix representation. In the classification set-up the RIUME subspace extracted from an experimental observation is tested against a set of subspaces representing the different object manifolds, in search for the nearest class. We show how to extract a set of mutually orthogonal subspaces, where each subspace represents a different object manifold. In the presence of observation noise, the observations do not lie strictly on the manifold and the resulting RIUME subspaces are noisy. We derive a method for estimating the mean subspace representation of a manifold of deformed observations. To optimize the performance of the matched manifold detector in the presence of observation noise, an analytic solution for choosing the RIUME nonlinear operators is derived, achieving the effect of simultaneous denoising of the object manifolds. The invariant representation of the object is the basis of a matched manifold detection and tracking framework for objects that undergo complex geometric and radiometric deformations. The experimental results on natural scenes demonstrate the generality and applicability of the RIUME framework for classification, detection, and dense registration.

Robust Radiometric Normalization of Multitemporal Satellite Images Via Block Adjustment Without Master Images

Determining appropriate master images, reducing radiometric error accumulation, and eliminating outliers from the cloud, water, and land changes, are three main issues in radiometric normalization of multitemporal high-resolution satellite images (HRSI) during mosaicking. However, these three issues have not been simultaneously considered by the existing methods. This article presents a comprehensive radiometric normalization method for multitemporal HRSI using a radiometric block adjustment without master images. Pseudoinvariant features (PIFs) extracted from image pairs using the iteratively reweighted multivariate alteration detection are used as the corresponding pixel observations and organized to form radiometric tie points according to the corresponding horizontal space coordinates. Radiometric error equations are subsequently constructed, and the linear radiometric transformation parameters are solved by a global adjustment. The time-invariant PIFs generally represent the true corresponding features and naturally avoid the cloud, water, and land changes, which can eliminate the effects of outliers. Furthermore, the pixel values of tie points calculated from the weighted average of the corresponding pixel observations are used as virtual radiometric control points to eliminate the dependency on master images. Moreover, a global optimum can be achieved by the global adjustment, effectively overcoming the error accumulation, which is severe in large datasets. Four groups of HRSI datasets from various satellites are used to validate the performance of the proposed method. Experimental results demonstrate that the proposed method outperforms two state-of-the-art methods and has good applicability and stability, considering both visual effects and quantitative performance.

Application of Remote Sensing Data in Lithological Discrimination of Kerdous Inlier in the Anti Atlas Belt of Morocco

Remote sensing data reveals a great importance for lithological mapping due to their spatial, spectral and radiometric characteristics. Lithological mapping using spatial data is a preliminary and important step to mineral mapping. In this work, several spectral and radiometric transformations methods were applied on Landsat 8 OLI data to enhance lithological units in the study area situated in the Anti Atlas belt. The methods of Optimum Index Factor (OIF), Decorrelation Stretching (DS), Principal Components Analysis (PCA) and Band Ratioing (BR) showed good results for lithological mapping in comparison with the existing geological and field investigation. An RGB color composite of OLI bands 651 was developed for mapping lithological units of the study area by fusing optimum index factor (OIF) and decorrelation stretching methods. furthermore, Band ratios derived from image spectra were applied in two RGB color composites (7+4/2, PC1, PC2)Â and (PC1, 7/6, 3/7) providing good discrimination of the lithological units. The Landsat-8 OLI data significantly provided satisfied results for lithological mapping.

Spectral-consistent relative radiometric normalization for multitemporal Landsat 8 imagery

Radiometric normalization is a fundamental and important preprocessing method for remote sensing applications using multitemporal satellite images due to uncertainties of at-sensor radiances caused by different sun angles and atmospheric conditions. In case the atmospheric model and ground measurements are unavailable during data acquisitions, relative normalization is an alternative method which minimizes the radiometric differences among images without the requirement of additional information. The keys to a successful relative normalization are the selection of pseudo invariant features (PIFs) from bitemporal images and the regression of selected PIFs for transformation coefficient determination. Previous studies on transformation coefficient determination adopted band-by-band regression. These studies have obtained satisfactory normalization results; however, they have not fully considered the spectral inconsistency problem caused by individual band regression. To alleviate this problem, this study proposed a constrained orthogonal regression, which enforces pixel spectral signatures to be as consistent as possible during radiometric normalization while band regression quality is preserved. In addition, instead of selecting one of the input images as reference for radiometric transformation, a common radiometric level located between bitemporal images is selected as the reference to further reduce possible spectral inconsistency. Qualitative and quantitative analyses of several bitemporal images acquired by the Landsat 8 sensor were conducted to evaluate the proposed method with the measurements of spectral distance and similarity. The experimental results demonstrate the superiority of the proposed method to related regression and radiometric normalization methods, in terms of spectral signature consistency.

IMPACT OF REDUCTION OF RADIOMETRIC RESOLUTION IN HYPERSPECTRAL IMAGES ACQUIRED OVER FOREST FIELD

Abstract. The objective of this study was to evaluate the impact of reducing the radiometric information of hyperspectral images. The image data was collected originally with 32 bits and rescaled to 8 and 16 bit/pixel. The images were acquired with a Rikola Hyperspectral Camera attached to an Unmanned Aerial Vehicle (UAV). After the geometric and radiometric processing of the images, a mosaic was obtained with pixels representing reflectance factor coded in 32 bits. Using the minimum and maximum values of each spectral band, a linear equation was thus applied to reduce the radiometric resolution of the original mosaic, from 32 bits to 8 bits and from 32 bits to 16 bits. Following, the Normalized Root Mean Square Error (NRMSE %) and the Mean Absolute Percentage Error (MAPE %) were used to evaluate the results, showing that for the 8 bits mosaic, the loss of information was higher. For this radiometric resolution rescaling, the MAPE % achieved values until 22.486 % and the highest NRMSE % value was 0.455 % while, for the 16 bits mosaics, the highest MAPE % and NRMSE % values were 0.069 % and 0.002 %, respectively. Finally, it can be inferred that the impact of radiometric transformation can be considered as negligible for the hyperspectral mosaic with 16 bits of radiometric resolution, which presented lower values of NRMSE % and MAE % and could not affect the mosaic analysis.

Geometry and Radiometry Invariant Matched Manifold Detection

Consider a set of deformable objects undergoing geometric and radiometric transformations. As a result of the action of these transformations, the set of different realizations of each object is generally a manifold in the space of observations. Assuming the geometric deformations an object undergoes, belong to some finite dimensional family, it has been shown that the universal manifold embedding (UME) provides a set of nonlinear operators that universally maps each of the different manifolds, where each manifold is generated by the set all of possible appearances of a single object, into a distinct linear subspace of an Euclidean space. In this paper, we generalize this framework to the case where the observed object undergoes both an affine geometric transformation, and a monotonic radiometric transformation, and present a novel framework for the detection and recognition of the deformable objects. Applying to each of the observations an operator that makes it invariant to monotonic amplitude transformations, but is geometry-covariant with the affine transformation, the set of all possible observations on that object is mapped by the UME into a single linear subspace-invariant with respect to both the geometric and radiometric transformations. The embedding of the space of observations is independent of the specific observed object; hence it is universal. The invariant representation of the object is the basis of a matched manifold detection and tracking framework of objects that undergo complex geometric and radiometric deformations: the observed surface is tessellated into a set of tiles such that the deformation of each one is well approximated by an affine geometric transformation and a monotonic transformation of the measured intensities. Since each tile is mapped by the radiometry invariant UME to a distinct linear subspace, the detection and tracking problems are solved by evaluating distances between linear subspaces. Classification in this context becomes a problem of determining which labeled subspace in a Grassmannian is closest to a subspace in the same Grassmannian, where the latter has been generated by radiometry invariant UME from an unlabeled observation.

VOXEL BASED REPRESENTATION OF FULL-WAVEFORM AIRBORNE LASER SCANNER DATA FOR FORESTRY APPLICATIONS

The advantages of using airborne full-waveform laser scanner data in forest applications, e.g. for the description of the vertical vegetation structure or accurate biomass estimation, have been emphasized in many publications. To exploit the full potential offered by airborne full-waveform laser scanning data, the development of voxel based methods for data analysis is essential. In contrast to existing approaches based on the extraction of discrete 3D points by a Gaussian decomposition, it is very promising to derive the voxel attributes from the digitised waveform directly. For this purpose, the waveform data have to be transferred into a 3D voxel representation. This requires a series of radiometric and geometric transformations of the raw full-waveform laser scanner data. Thus, the paper deals with the geometric aspects and describes a processing chain from the raw waveform data to an attenuationcorrected volumetric forest stand reconstruction. <br><br> The integration of attenuation-corrected waveform data into the voxel space is realised with an efficient parametric voxel traversal method operating on an octree data structure. The voxel attributes are derived from the amplitudes of the attenuation-corrected waveforms. Additionally, a new 3D filtering approach is presented to eliminate non-object voxel. Applying these methods to real full-waveform laser scanning data, a voxel based representation of a spruce was generated combining three flight strips from different viewing directions.

Least-squares Matching with Advanced Geometric Transformation Models

The least-squares matching algorithm (LSM) for area-based image matching is a well known technique in photogrammetry and computer vision since more than two decades. Differences between two or more images can be modelled by estimating geometric and radiometric transformation functions within the functional model. Commonly the affine transformation is used as geometric transformation. Since this approach is not strict in terms of the projective imaging model, it is worthwhile to investigate alternative transformation models. This paper presents an advanced least-squares matching algorithm that uses the projective transformation model and polynomial transformations to handle geometric distortions between the images. The projective approach is geometrically strict as long as object surface and image sensors are planes. The polynomial approach is supposed to be geometrically strict for plane image sensors and non-plane object surfaces. The possibility of this kind of expansions has been mentioned in several papers but up to now no publicly available investigation is known. First results of the new geometric model have been published by the authors in 2008, showing promising effects on non-planar object patches. * Corresponding author

Características espectrais da soja ao longo do ciclo vegetativo com imagens landsat 5/TM em área agrícola no oeste do Paraná

O objetivo deste trabalho foi estudar as mudanças no comportamento espectral da cultura da soja, por meio dos perfis espectrais temporais dos índices de vegetação NDVI e GVI, expressos em diferentes valores físicos: fator de reflectância bidirecional (FRB) aparente, de superfície e normalizado derivados de imagens Landsat 5/TM. Foi monitorada área de cultura de soja localizada próxima ao município de Cascavel - PR, utilizando cinco imagens da safra de 2004/2005, sendo realizados nessas imagens os procedimentos de transformação radiométrica, correção atmosférica e normalização, determinando valores físicos dos fatores de reflectância bidirecional aparente, de superfície e normalizado, respectivamente. Com o intuito de caracterizar a resposta espectral da biomassa da soja, geraram-se imagens referentes aos índices de vegetação NDVI e GVI. Como resultado, a cultura mostrou-se diferente para os tratamentos dos fatores de reflectância bidirecional aparente, de superfície e de normalização. Por meio dos perfis médios espectrais do NDVI e GVI, foi possível acompanhar todo o ciclo da cultura da soja, caracterizando o seu desenvolvimento. Observou-se, ainda, que os dados provenientes do fator de reflectância bidirecional normalizado descaracterizaram a curva espectral da cultura da soja, principalmente em meio à fase de crescimento vegetativo, na data de 9-12-2004.

Stereo Processing by Semiglobal Matching and Mutual Information

This paper describes the Semi-Global Matching (SGM) stereo method. It uses a pixelwise, Mutual Information based matching cost for compensating radiometric differences of input images. Pixelwise matching is supported by a smoothness constraint that is usually expressed as a global cost function. SGM performs a fast approximation by pathwise optimizations from all directions. The discussion also addresses occlusion detection, subpixel refinement and multi-baseline matching. Additionally, postprocessing steps for removing outliers, recovering from specific problems of structured environments and the interpolation of gaps are presented. Finally, strategies for processing almost arbitrarily large images and fusion of disparity images using orthographic projection are proposed.A comparison on standard stereo images shows that SGM is among the currently top-ranked algorithms and is best, if subpixel accuracy is considered. The complexity is linear to the number of pixels and disparity range, which results in a runtime of just 1-2s on typical test images. An in depth evaluation of the Mutual Information based matching cost demonstrates a tolerance against a wide range of radiometric transformations. Finally, examples of reconstructions from huge aerial frame and pushbroom images demonstrate that the presented ideas are working well on practical problems.

A technique for generating natural colour images from false colour composite images

Colour is widely used in remote sensing work. In many instances, the use of colour conveys additional information both visually and scientifically. Remote sensing satellites view the earth in different spectral bands, viz. near infrared (NIR), red, green, and blue bands, in a conventional multispectral imaging system. In the absence of a blue channel, colour images can be generated using near infrared, red, and green bands in what is known as a false colour composite (FCC) and does not look natural, like the image we see with the naked eye. For a trained interpreter, this does not pose any problems. However, when the intended use is a fly‐through of a draped terrain, visual interpretation, or a display, meant for the non‐remote sensing professional, this becomes a handicap. To overcome this, there is a requirement to generate natural colour composites (NCC) from the given false colour composite, which demands the simulation of a blue band to be combined with green and red bands. This paper describes a unique method of generating a blue band to form natural colour images from a given false colour image set. We use a spectral transformation method to establish a relationship between the false colour and true colour image pairs provided by a sensor with all the four bands, which has a broader spectral coverage. A transformation function is fitted by selecting radiometric control points along the line of geometric registration to find a set of coefficients to be used for simulating a blue band. This blue band, along with the green and red bands, provides a near true colour or ‘natural colour’ on the display. In this paper, we present a set of adjustable radiometric transformation coefficients to accommodate variation in spatial and dynamic range offered by sensors to generate natural colour. These coefficients seem to work on a large number of images of different seasons, provided similar spectral bands and terrain are used. The proposed ‘natural colour generator’ can be used in changing false colour images to natural colour images with the aim of ‘what you get is what you would have seen’.

A variational approach to the radiometric enhancement of digital imagery

In this correspondence, we present a variational approach to the problem of finding suitable radiometric image transformations that optimize desirable characteristics of the output image histogram. This variational approach can be interpreted as the minimization of the cumulative spacing between histogram bars in the least squares sense subject to some weight function. Most of the common histogram transformation procedures used in remote sensing applications can be deduced from this general variational approach with an appropriate choice of the weight function.

Digital image correlation with an analytical plotter

A method of digital image correlation is presented which matches the matrices of grey values of two homologous image windows by a linear geometric and radiometric transformation. The image correlation procedure is implemented on an analytical plotter (Zeiss Planicomp C 100) for on-line operation. First, the mathematical formulation of the correlation method is given. The unknown transformation parameters are determined by a least-squares solution which minimizes the differences of grey values. Then the hardware extension of the Planicomp C 100 system by two CCD-video cameras for digitization of image windows is described. Finally, the results of experimental investigations with the correlator are discussed with respect to accuracy and time consumption.