An emprical point error model for TLS derived point clouds

The random error pattern of point clouds has significant effect on the quality of final 3D model. The magnitude and distribution of random errors should be modelled numerically. This work aims at developing such an anisotropic point error model, specifically for the terrestrial laser scanner (TLS) acquired 3D point clouds. A priori precisions of basic TLS observations, which are the range, horizontal angle and vertical angle, are determined by predefined and practical measurement configurations, performed at real-world test environments. A priori precision of horizontal (σθ) and vertical (σα) angles are constant for each point of a data set, and can directly be determined through the repetitive scanning of the same environment. In our practical tests, precisions of the horizontal and vertical angles were found as σθ=±36.6 and σα=±17.8, respectively. On the other hand, a priori precision of the range observation (σρ) is assumed to be a function of range, incidence angle of the incoming laser ray, and reflectivity of object surface. Hence, it is a variable, and computed for each point individually by employing an empirically developed formula varying as σρ=±2a'12 mm for a FARO Focus X330 laser scanner. This procedure was followed by the computation of error ellipsoids of each point using the law of variance-covariance propagation. The direction and size of the error ellipsoids were computed by the principal components transformation. The usability and feasibility of the model was investigated in real world scenarios. These investigations validated the suitability and practicality of the proposed method.This research was funded by TUBITAK - The Scientific and Technological Research Council of Turkey (Project ID: 115Y239) and by the Scientific Research Projects of Bulent Ecevit University (Project ID: 2015-47912266-01)Publisher's Versio

Stochastic surface mesh reconstruction

This research was funded by TUBITAK – The Scientific and Technological Research Council of Turkey (Project ID: 115Y239) and by the Scientific Research Projects of Bülent Ecevit University (Project ID: 2015-47912266-01)A generic and practical methodology is presented for 3D surface mesh reconstruction from the terrestrial laser scanner (TLS) derived point clouds. It has two main steps. The first step deals with developing an anisotropic point error model, which is capable of computing the theoretical precisions of 3D coordinates of each individual point in the point cloud. The magnitude and direction of the errors are represented in the form of error ellipsoids. The following second step is focused on the stochastic surface mesh reconstruction. It exploits the previously determined error ellipsoids by computing a point-wise quality measure, which takes into account the semi-diagonal axis length of the error ellipsoid. The points only with the least errors are used in the surface triangulation. The remaining ones are automatically discarded.Publisher's Versio

Classification of viewpoint independent image groups

Görüntü gruplarının sınıflandırılmasının, uzaktan algılama, fotogrametri, dijital tablo katalogları ve güvenlik alanlarında değişik uygulamaları mevcuttur. Uzaktan algılama, bilgisayarla görme ve fotogrametri disiplinlerinde çalışan araştırmacılar güçlü sınıflandırma metodları geliştirebilmek için görüntü özelliklerini (renk, doku, şekil) temel alan yaklaşımlar kullanmışlardır. Bu çalışmada, değişik bakış açılarından elde edilen farklı görüntülere ait grupların sınıflandırılması, konik kesitlerin projektif dönüşüm altında değişmezliği prensibine dayanarak geliştirilmiştir. Çalışmanın başlangıcında, test amaçlı bir veri seti oluşturulmuştur. Bu veri seti dokuz ayrı objeden farklı bakış açılarından on görüntü alınması ile toplam doksan görüntüden oluşmaktadır. Veri seti oluşturulduktan sonra objelerin sınırlarının belirlenmesi amacıyla bütün görüntüler üzerinde kenar tanıma işlemi uygulanmıştır. Kenar tanıma işlemi ile elde edilen kenarlar üzerine, geometrisine bağlı olarak konik kesitler oturtulmuştur. Bu işlem sonucunda her görüntü bir konik kesit kümesiyle ifade edilmiştir. Konik kesitler projektif dönüşüme uğradığında şekli değişse bile yine bir konik kesit oluşmaktadır. Her görüntüyü temsil eden konik kesit kümesi kullanılarak görüntüler için bir değişmez işaret hesaplanmıştır. Aynı gruba ait görüntülerin değişmez işaretleri arasında bir benzerlik olacağı varsayılmıştır. Bu değişmez işaretler görsellik sağlaması ve hesaplamalarda kolaylık olması amacıyla histogramlara dönüştürülmüştür. Her görüntüye ait değişmez işaretler çok boyutlu veriler olduklarından dolayı aralarında benzerlik olup olmadığını tespit etmek için Destek Vektör Makineleri uygulanmıştır. Sınıflandırmanın performansını ölçebilmek için ROC(Receiver Operating Characteristics) analizleri yapılmıştır.Classification of image groups has different applications in remote sensing, photogrammetry, digital painting catalogues and security related areas. Researchers from remote sensing, computer vision and photogrammetry have used different approaches based on image features (color, textures, object shapes) in order to develop robust classification methods. In this study, classification of viewpoint independent image groups is developed based on the principle of invariant properties of conic sections under projective transformation. At the beginning of this study a data set is created for testing the method. This dataset consists of nine categories of images, for each category ten images used that are taken from different viewpoints. Edge detection is applied on each image to detect boundary of objects in images. Detected edges are used for conic fitting so that each conic will be represented as a set of conic sections. Under projective transformation conic sections remain as conic section even if their shapes change. For each image in the dataset an invariant signature is computed using set of conic sections. It is assumed that there is similarity between invariant signatures of images belong to the same image category. These invariant signatures are used in histogram form for visual representation and computations. Since invariant signatures are high dimensional data, Support Vector Machines which is a stochastic pattern recognition algorithm is used instead of classical deterministic methods. Performance of classification is evaluated using ROC (Receiver Operating Characteristics) analysis

CLASSIFICATION OF VIEWPOINT INDEPENDENT IMAGE GROUPS

Classification of image groups has different applications in remote sensing, photogrammetry, digital painting catalogues and security related areas. Researchers from remote sensing, computer vision and photogrammetry have used different approaches based on image features (color, textures, object shapes) in order to develop robust classification methods. In this study, classification of viewpoint independent image groups is developed based on the principle of invariant properties of conic sections under projective transformation. Since invariant signatures are high dimensional data, Support Vector Machines which is a stochastic pattern recognition algorithm is used instead of classical deterministic methods. Performance of classification is evaluated using ROC (Receiver Operating Characteristics) analysis.At the beginning of this study a data set is created for testing the method. This dataset consists of nine categories of images, for each category ten images used that are taken from different viewpoints. Edge detection is applied on each image to detect boundary of objects in images. Detected edges are used for conic fitting so that each conic will be represented as a set of conic sections. Under projective transformation conic sections remain as conic section even if their shapes change. For each image in the dataset an invariant signature is computed using set of conic sections. It is assumed that there is similarity between invariant signatures of images belong to the same image category. These invariant signatures are used in histogram form for visual representation and computations

A generic point error model for TLS derived point clouds

This work aims at developing a generic and anisotropic point error model, which is capable of computing magnitude and direction of a priori random errors, described in the form of error ellipsoids for each individual point of the cloud. The direct TLS observations are the range (rho), vertical (alpha) and horizontal (theta) angles, each of which is in fact associated with a priori precision value. A practical methodology was designed and performed in real-world test environments to determine these precision values. The methodology has two experimental parts. The first part is a static and repetitive measurement configuration for the determination of a priori precisions of the vertical (sigma(alpha)) and horizontal (sigma(theta)) angles. The second part is the measurement of a test stand which contains four plates in white, light grey, dark grey and black colors, for the determination of a priori precisions of the range observations (sigma(rho)). The test stand measurement is performed in a recursive manner so that sensor-to-object distance, incidence angle and surface reflectivity are parameterized. The experiment was conducted with three TLSs, namely Faro Focus 3D X330, Riegl VZ400 and Z+F 5010x in the same location and atmospheric conditions. This procedure was followed by the computation of error ellipsoids of each point using the law of variance-covariance propagation. The direction and size of the error ellipsoids were computed by the principal components transformation. Validation of the proposed error model was performed in real world scenarios, which revealed feasibility of the model.SPIEPublisher's Versio

A point cloud filtering method based on anisotropic error model

This study was supported by TUBITAK – The Scientific and Technological Research Council of Türkiye (project ID: 115Y239); and the Scientific Research Projects of Zonguldak Bülent Ecevit University (project ID: 2015‐47912266‐01).Many modelling applications require 3D meshes that should be generated from filtered/cleaned point clouds. This paper proposes a methodology for filtering of terrestrial laser scanner (TLS)-derived point clouds, consisting of two main parts: an anisotropic point error model and the subsequent decimation steps for elimination of low-quality points. The point error model can compute the positional quality of any point in the form of error ellipsoids. It is formulated as a function of the angular/mechanical stability, sensor-to-object distance, laser beam's incidence angle and surface reflectivity, which are the most dominant error sources. In a block of several co-registered point clouds, some parts of the target object are sampled by multiple scans with different positional quality patterns. This situation results in redundant data. The proposed decimation steps removes this redundancy by selecting only the points with the highest positional quality. Finally, the Good, Bad, and the Better algorithm, based on the ray-tracing concept, was developed to remove the remaining redundancy due to the Moiré effects. The resulting point cloud consists of only the points with the highest positional quality while reducing the number of points by factor 10. This novel approach resulted in final surface meshes that are accurate, contain predefined level of random errors and require almost no manual intervention.Scientific Research Projects of Zonguldak Bülent Ecevit UniversityTürkiye Bilimsel ve Teknolojik Araştırma KurumuPublisher's VersionQ3WOS:00105986010000

Comprehensive evaluation of Pleiades-1A bundle images for geospatial applications

This paper presents the results of comprehensive evaluation of Pleiades 1A which is the first civilian satellite of Europe with sub-meter resolution. The analyses consist of radiometric evaluation, georeferencing accuracy assessment, pan-sharpening performance, digital surface/terrain model quality and vector map production. The effective resolution is estimated with a factor slightly below 1.0 for triplet panchromatic images, and signal to noise ratio is in the range of other comparable space borne images. The georeferencing accuracy was estimated with a standard deviation in X and Y directions in the range of 0.45m by bias-corrected and sensor-dependent rational functional model. 3D standard deviation of +/- 0.44m in X direction, +/- 0.51m in Y direction and +/- 1.82m in the Z direction were reached in spite of the very narrow angle of convergence by the same mathematical model. The generated digital surface/terrain models were achieved with +/- 1.6m standard deviation in Z direction in relation to a reference digital terrain model. The pan-sharpened images were generated by various methods, and were validated by quantitative and qualitative analyses. Moreover, a vector map was generated in the level of detail 0 to analyse information content