This study compares the ability of different robust regression estimators to detect and classify outliers. Well-known estimators with high breakdown points were compared using simulated data. Mean success rates (MSR) ...This study compares the ability of different robust regression estimators to detect and classify outliers. Well-known estimators with high breakdown points were compared using simulated data. Mean success rates (MSR) were computed and used as comparison criteria. The results showed that the least median of squares (LMS) and least trimmed squares (LTS) were the most successful methods for data that included leverage points, masking and swamping effects or critical and concentrated outliers. We recommend using LMS and LTS as diagnostic tools to classify outliers, because they remain robust even when applied to models that are heavily contaminated or that have a complicated structure of outliers.展开更多
Geopotential, dynamic, orthometric and normal height systems and the corrections related to these systems are evaluated in this paper. Along two different routes, with a length of about 5 kilometers, precise leveling ...Geopotential, dynamic, orthometric and normal height systems and the corrections related to these systems are evaluated in this paper. Along two different routes, with a length of about 5 kilometers, precise leveling and gravity measurements are done. One of the routes is in an even field while the other is in a rough field. The magnitudes of orthometric, normal and dynamic corrections are calculated for each route. Orthometric, dynamic, and normal height differences are acquired by adding the corrections to the height differences obtained from geometric leveling. The magnitudes of the corrections between the two routes are compared. In addition, by subtracting orthometric, dynamic, and normal heights from geometric leveling, deviations of these heights from geometric leveling are counted.展开更多
One of the challenges of remote sensing and computer vision lies in the three-dimensional(3-D)reconstruction of individual trees by using automated methods through very high-resolution(VHR)data sets.However,a successf...One of the challenges of remote sensing and computer vision lies in the three-dimensional(3-D)reconstruction of individual trees by using automated methods through very high-resolution(VHR)data sets.However,a successful and complete 3-D reconstruction relies on precise delineation of the trees in two dimensions.In this paper,we present an original approach to detect and delineate citrus trees using unmanned aerial vehicles based on photogrammetric digital surface models(DSMs).The symmetry of the citrus trees in a DSM is handled by an orientationbased radial symmetry transform which is computed in a unique way.Next,we propose an efficient strategy to accurately build influence regions of each tree,and then we delineate individual citrus trees through active contours by taking into account the influence region of each canopy.We also present two efficient strategies to filter out erroneously detected canopy regions without having any height thresholds.Experiments are carried out on eight test DSMs composed of different types of citrus orchards with varying densities and canopy sizes.Extensive comparisons to the state-of-the-art approaches reveal that our proposed approach provides superior detection and delineation performances through supporting a nice balance between precision and recall measures.展开更多
基金Project (No. 28-05-03-03) supported by the Yildiz Technical University Research Fund, Turkey
文摘This study compares the ability of different robust regression estimators to detect and classify outliers. Well-known estimators with high breakdown points were compared using simulated data. Mean success rates (MSR) were computed and used as comparison criteria. The results showed that the least median of squares (LMS) and least trimmed squares (LTS) were the most successful methods for data that included leverage points, masking and swamping effects or critical and concentrated outliers. We recommend using LMS and LTS as diagnostic tools to classify outliers, because they remain robust even when applied to models that are heavily contaminated or that have a complicated structure of outliers.
文摘Geopotential, dynamic, orthometric and normal height systems and the corrections related to these systems are evaluated in this paper. Along two different routes, with a length of about 5 kilometers, precise leveling and gravity measurements are done. One of the routes is in an even field while the other is in a rough field. The magnitudes of orthometric, normal and dynamic corrections are calculated for each route. Orthometric, dynamic, and normal height differences are acquired by adding the corrections to the height differences obtained from geometric leveling. The magnitudes of the corrections between the two routes are compared. In addition, by subtracting orthometric, dynamic, and normal heights from geometric leveling, deviations of these heights from geometric leveling are counted.
基金This work was supported by the Scientific and Technical Research Council of Turkey(TUBITAK)[grant number 114Y671].
文摘One of the challenges of remote sensing and computer vision lies in the three-dimensional(3-D)reconstruction of individual trees by using automated methods through very high-resolution(VHR)data sets.However,a successful and complete 3-D reconstruction relies on precise delineation of the trees in two dimensions.In this paper,we present an original approach to detect and delineate citrus trees using unmanned aerial vehicles based on photogrammetric digital surface models(DSMs).The symmetry of the citrus trees in a DSM is handled by an orientationbased radial symmetry transform which is computed in a unique way.Next,we propose an efficient strategy to accurately build influence regions of each tree,and then we delineate individual citrus trees through active contours by taking into account the influence region of each canopy.We also present two efficient strategies to filter out erroneously detected canopy regions without having any height thresholds.Experiments are carried out on eight test DSMs composed of different types of citrus orchards with varying densities and canopy sizes.Extensive comparisons to the state-of-the-art approaches reveal that our proposed approach provides superior detection and delineation performances through supporting a nice balance between precision and recall measures.
