Exterior Orientation Parameters Research Articles

Spaceborne pose determination based on image to 3D digital surface model matching

Developing an accurate onboard-camera pose estimation is one major challenge of satellite systems, and the attempt of improving remote sensing camera pose accuracy never ceases. The camera pose can be recovered by aligning a captured 2D image and a 3D digital surface model of the corresponding scene. In this paper, a novel camera pose estimation method from captured images with the over known real scene 3D products is proposed to enhance remote sensing camera attitude accuracy. The purpose of this estimation is to determine the pose of a camera purely from an image based on a known 3D model, where 3D products of very high spatial resolution are projected onto image space by virtual camera system with error-contained initial exterior orientation parameters, and whether the pose of the camera can be determined precisely depends on the 2D–3D registration result. The process consists of two steps: (1) feature extraction and (2) similarity measure and registration. Furthermore, the proposed method revises the rotation matrix and translation vector by utilizing formulation based on quaternion representation of rotation, respectively. We evaluate our method on challenging simulation data and results show that acceptable accuracy of camera pose can be achieved.

The influence of targeted selection on the exterior and productive indicators of Stavropol sheep in the dry steppe zone of the Volga region

Relevance. Purposeful selection contributes to obtaining the optimal type of sheep of the Stavropol breed with improved exterior and productive parameters in the steppe conditions of the Volga region.Methods. The experiment took place in the CJSC “New Life” of the Novouzensky district of the Saratov region. The research material is purebred sheep of the Stavropol breed. Formation of experimental groups of queens (3): during selection into constitutionally productive types, an eye-dimensional assessment of external forms was used, which was specified by the measurements of body articles, live weight. In the first group were selected queens with a deviation to the strong type of constitution, in the second — to the gentle, in the third — to the loose. They mated with strong-type tups.Results. The offspring were formed: group I — from strong queens, II — tender, III — loose types. At birth, group III ewes had an advantage over I by 1.70%, over II — by 7.47%. Group I was superior in live weight to group II by 5.67%. In 4.5 months, “loose x strong” offspring exceeded “strong x strong” by 2.43%, “tender x strong” — by 12.27%. “Strong x strong” had an advantage over “gentle x strong” by 9.61%. According to the exterior at birth, the descendants of “loose x strong” surpassed “strong x strong”, “tender x strong” in chest width by 19.52; 5.78%; chest circumference — by 6.08; 10.99%, “strong x strong” prevailed over “gentle x strong” in chest width by 32%; chest circumference — by 4.62%. The trend of changing body measurements was observed at 4.5 monthsas well. According to the downness index at birth, “tender x strong” exceeded “strong x strong”, “loose x strong” by 8.03; 9.12%, at 4.5 months “strong x strong” had superiority over “tender x strong”, “loose x strong” by 3.77; 4.77%. All the young animals inherited the constitutional and productive characteristics of their parents, which is important in the inbreeding selection of sheep of the Stavropol breed in the Volga region.

IMPACT OF GNSS SIGNAL OUTAGE ON EOPS USING FORWARD KALMAN FILTER AND SMOOTHING ALGORITHM

Abstract. The global navigation satellite system (GNSS) has been playing the principal role in positioning applications in past decades. Position robustness degrades with a standalone receiver due to GNSS signal outage in mobile mapping systems. The GNSS and inertial measurement unit (IMU) integration is used to solve positioning degradation. This article studies the GNSS/IMU integration processing (i.e., forward Kalman filter (KF) and smoothing algorithm) using a single or a network of nearby GNSS reference stations. In addition, we analyze the impact of simulated GNSS signal outage on exterior orientation parameters (EOPs). The outcomes confirm that the smoothing algorithm works better than the forward KF and improves the accuracy for position and orientation in the case when there is no GNSS signal outage. Also, it improves the position and orientation accuracy by about 95% and 60% when there is a 180 second GNSS signal outage, respectively.

TIN-BASED ROBUST MOSAICKING OF UAV IMAGES WITH CONSECUTIVE IMAGE CONNECTION

Abstract. UAV image mosaicking could be divided into processing in an absolute space and in a relative space. Among them, the latter technique generates a mosaicked image using only UAV’s EOP (Exterior Orientation Parameters) without additional spatial data. Since this technique uses only images, it is easy to use, but sometimes exhibits unstable quality. In this study, we adopted the mosaicking in a relative space by introducing the TIN (Triangulated Irregular Network) structure for consecutive image connectivity. This method refines a point cloud and extracts optimal images for mosaicking as a pre-processing. After that, this generates a TIN, and performs triangular unit mosaicking and image unit mosaicking to produce a mosaicked image. We evaluated the proposed method with datasets acquired by fixed wing and rotary wing UAVs. We compared this method’s results with Pix4Dmapper’s and the previously developed method’s results. Through the experimental results, we were able to verify that this method was less affected by external factors such as ground topology and UAV status. Moreover, we could confirm this method reduced over-transformation, and mismatch of the junction area between images on mosaicked images. It showed stable quality despite of using only images. Through the above results, our method also showed similar performance to Pix4Dmapper.

HIERARCHICAL AERIAL TRIANGULATION OF OBLIQUE IMAGE DATA

Abstract. Oblique imaging sensor systems are becoming a standard for photogrammetric applications such as city modeling, with increasing need for frequently updated geospatial data in urban areas. The ground processing software has to consider and support this develop-ment, providing a highly performant workflow for projects that can consist of tens or hundreds of thousands of images in different view angles: nadir (vertical), left, right, backward, forward. This is a challenge also for the oblique aerial triangulation (AT), both the large number of images to process and the high amount of overlap in-between them, where more than 50 image measurements for a given ground point are common.At the same time, the large overlap even in a single view generally provides stable sub-blocks for each viewing direction and, therefore, allows for separating the oblique AT by view and performing a hierarchical approach: The nadir block is triangulated first, updating the exterior orientation parameters of all nadir images and providing adjusted ground points. This nadir reference can be used to tie the oblique views’ sub-blocks in subsequent AT runs to eventually provide adjusted exterior orientations for all images, with comparable accuracy to an integrated AT – but providing a significant performance gain. The hierarchical AT is a three-step approach: 1. adjust-ment of the nadir imagery, 2. adjustment of each oblique views independently by cardinal direction (East, West, North, South) while tying it to the nadir reference, and 3. computation of combined statistics and report.The paper details our AT approach and evaluates its parameterization for different oblique data sets, comparing results and performance to a fully integrated AT.

Optimal Self-Calibration Strategies in the Combined Bundle Adjustment of Aerial–Terrestrial Integrated Images

Accurate combined bundle adjustment (BA) is a fundamental step for the integration of aerial and terrestrial images captured from complementary platforms. In traditional photogrammetry pipelines, self-calibrated bundle adjustment (SCBA) improves the BA accuracy by simultaneously refining the interior orientation parameters (IOPs), including lens distortion parameters, and the exterior orientation parameters (EOPs). Aerial and terrestrial images separately processed through SCBA need to be fused using BA. Thus, the IOPs in the aerial–terrestrial BA must be properly treated. On one hand, the IOPs in one flight should be identical for the same images in physics. On the other hand, the IOP adjustment in the cross-platform-combined BA may mathematically improve the aerial–terrestrial image co-registration degree in 3D space. In this paper, the impacts of self-calibration strategies in combined BA of aerial and terrestrial image blocks on the co-registration accuracy were investigated. To answer this question, aerial and terrestrial images captured from seven study areas were tested under four aerial–terrestrial BA scenarios: the IOPs for both aerial and terrestrial images were fixed; the IOPs for only aerial images were fixed; the IOPs for only terrestrial images were fixed; the IOPs for both images were adjusted. The cross-platform co-registration accuracy for the BA was evaluated according to independent checkpoints that were visible on the two platforms. The experimental results revealed that the recovered IOPs of aerial images should be fixed during the BA. However, when the tie points of the terrestrial images are comprehensively distributed in the image space and the aerial image networks are sufficiently stable, refining the IOPs of the terrestrial cameras during the BA may improve the co-registration accuracy. Otherwise, fixing the IOPs is the best solution.

A new method for automatic and accurate coded target recognition in oblique images to improve augmented reality precision

AbstractTo have an accurate augmented reality (AR) in a ubiquitous geospatial information system (UBGIS), camera poses should be accurately estimated. Consumer‐grade sensors of mobile phones do not provide high accuracies. Using artificial markers and image‐based techniques could be a good solution for accurately estimating camera pose parameters. Automatic detection of these markers in very oblique images is challenging. This article aims to combine targets and mobile phone sensors to improve augmented reality precision in visualizing underground infrastructures. To achieve this goal, we propose a new method for automatically recognizing coded targets (CTs) and identifying their centers in oblique images. Then, we estimate the camera pose by computing exterior orientation parameters by combining sensors and CTs for the space restriction procedure. Finally, we visualize underground infrastructures using AR. Comparing the results with the well‐known photogrammetry software Agisoft PhotoScan show that the proposed method is robust, fast, and invariant under different transformations of scaling, rotation, and incidence angle variations. It is also more accurate than the Agisoft with sub‐pixel accuracy (about 0.692 ± 0.141 pixels). Comparing the results of the vision‐based method of calculating exterior orientation parameters with the sensor‐based method demonstrates the precision improvement in camera pose estimation and thus better AR visualization.

Geometric calibration of a hyperspectral frame camera with simultaneous determination of sensors misalignment

The recent development of lightweight and relatively low-cost hyperspectral sensors has created new perspectives for remote sensing applications. This study aimed to investigate the geometric calibration of a hyperspectral frame camera based on a tuneable Fabry–Pérot interferometer (FPI) and two sensors. The radiation passes through the optics and then through the FPI, where it is redirected to two sensors using a beam-splitting prism. Previous studies have shown significant variations between the interior orientation parameters for the different bands, both between bands of the same sensor and between different sensors, and that these variations are due to the principle of image acquisition. Discrepancies of tens of pixels were obtained by comparing image coordinates measured in different bands. In this research, it was proposed to calibrate this camera in a static mode with changes in the mathematical calibration model. The restriction of obtaining only one set of exterior orientation parameters by hypercube was applied, adding parameters related to the misalignment between the sensors and parameters of a linear function relating the camera principal distance to the wavelength values. The application of the parameters estimated with this approach reduced the discrepancies between image coordinates measured in different bands to values smaller than one pixel. Using the sensor calibration parameters in the mobile UAV operation in an aerial bundle adjustment reduced the root mean square error (RMSE) on checkpoints by approximately 20% compared to the traditional model in which the interior orientation parameters and lens distortions were calibrated for each band separately. Thus, it was possible to obtain accurate results that make the use of this camera more practical since only one set of calibration parameters for all bands is needed.

Comparison between Multi Epipolar Geometry & Affine 2d Transformation-Based Filters for Optical Robot Navigation

Abstract: In previous research papers, Multi Epipolar Geometry-based Filter (M-EGF) has proven its high capability to overcome Single Epipolar Geometry-based Filter (S-EGF) as well as with conformal 2D Transformation-based Filter (C-2DF) in terms of providing precise, trusted, outlier-free, and real time Automatic Image Matching (AIM) results for Optical Robot Navigation (ORN) applications. This paper comes in a series of comparing M-EGF with the most common filters extensively used in ORN. Affine 2D Transformation-based Filter (A-2DF) is another familiar filter used for detecting outliers in AIM results and regards as the advanced version of C-2DF, where it can deal with any changing or distortion in image scales in X and Y directions. In this paper, M-EGF has been compared with A-2DF using the same system, tests, tracks and images and the same evaluation techniques used for comparing M-EGF to S-EGF and C-2DF in the related research papers. Tests show that A-2DF is similar to C-2DF in terms of its disability to filter AIM results in areas with open, narrow, and confused Depth Of Field (DOF). A-2DF is also limited in terms of finding out the 6 correct parameters for its mathematical model when AIM results includes a significant number of mismatching points. Difficult view angles has also a noted effect on the performance of A-2DF, but less than that of C-2DF due to its ability to work with different scales. In tests including limited DOF and low level of outliers, A-2DF has shown comparatively adequate outcomes, that is suitable for non-sensitive applications, in which the error does not entail any serious consequences. A-2DF is independent of the errors in Exterior Orientation Parameters (EOP) and Interior Orientation Elements (IOE) of cameras, where it is image points dependent estimation filter. Tests show that M-EGF is time- effective, efficient with any AIM findings, regardless DOF, capturing angle, outliers rate in observations, type of features and tracks. Unlike, A-2DF, tests show that the performance of M-EGF can affect by the quality of IOEs and EOPs, as a number of correctly matched points might be rejected, and this limitation is not related to the filter mathematical design but the professionalism of cameras calibration and EOP determination. in terms of processing time, A-2DF is considerably slow comparing with M-EGF due to its iterative and repeatability design, which makes it, unlike MEGF, unsuitable for real-time precise ORN applications. Keywords: Optical Robot Navigation, Epipolar Geometry, Automatic Image Matching, Co-planarity Equation, Real Time Applications, Affine 2D Transformation

Comparison Between Multi Epipolar Geometry and Conformal 2d Transformation-Based Filters for Optical Robot Navigation

Abstract: This research paper comes in a series of comparisons between the Multi Epipolar Geometry-based Filter (M-EGF) and the common filters that are extensively used in Optical Robot Navigation (ORN) for filtering the results of Automatic Image Matching (AIM). The accuracy of ORN depends mainly on the quality of the AIM results. Conformal 2D transformation-based Filter (C-2DF) is a common filter used in ORN applications. However, C-2DF is limited in terms of: time processing, disability to deal AIM data with significant number of outliers, dealing with images taking from different Depths Of Fields (DOF) and difficult view angles. M-EGF has been introduced recently by the author and compared with Single Epipolar Geometry based Filter (S-EGF), showing high ability to provide ORN with precise, trusted, outlier-free, and real time observations. In this paper, M-EGF has been compared with C-2DF using images captured by optical navigation system simulating ORN, which includes 3 cameras synchronized using GPS time. The performance of the two filters has been evaluated in different AIM environments and the automatically filtered results have been compared to precisely manually reviewed matched points using Matlap. Tests show that C-2DF has failed to deal with the AIM results in areas with open, narrow, and confused DOF. Also, they have failed to find out the right mathematical model in data with high rate of mismatched points and images with difficult view angles. C-2DF is also limited in its capability to deal with figures including different scales. With limited DOF and limited rate of errors, C-2DF has provided relatively sufficient results, which can be suitable for ORN in terms of quality and processing time. C-2DF overcomes S-EGF and M-EGF in terms of being not affected by errors in the Exterior Orientation Parameters (EOP) and Interior Orientation Elements (IOE) of cameras, as C-2DF depends on estimating the mathematical model parameters using only image points.

Multi and single epipolar geometry-based filters vs. affine and conformal 2D transformation-based filters

The accuracy of Optical Robot Navigation (ORN) depends mainly on the quality of the Automatic Image Matching (AIM) results. Conformal 2D transformation-based Filter (C-2DF), affine 2D transformation-based Filter (A-2DF) and Single Epipolar Geometry-based Filter (S-EGF) are common filters used in ORN. Multi-Epipolar Geometry-based Filter (M-EGF) has been introduced, tested and evaluated by the author and compared by each filter of those mentioned above in separate research papers. This paper comes to include all these individually comparisons, with additional details, showing the advantages and limitations of all filters comparing to each other. Tests show that C-2DF and A-2DF have failed to deal with AIM results in areas with open, narrow, and confused DOF. Also, they have failed to find out the right mathematical model in data with high rate of mismatched points and data obtained from images with difficult view angles. With limited DOF and low rate of errors, C-2DF and A-2DF have provided relatively sufficient results, which can be used for ORN applications that do not require precision. A-2DF is relatively better than C-2DF due to its flexibility to deal with figures including different scales, which is the case when dealing with different levels of DOF and different capturing angles between the cameras. The processing time is another disadvantage of C-2DF and A-2DF, where these are based on iterative estimation methods. Tests display how A-2DF is slowest, which keeps it away from using with real time ORN applications. Tests show that S-EGF and M-EGF are timesaving and able to deal with any AIM results, regardless the DOF, view angle and errors rate in observations. S-EGF is affected in areas including lines parallel to the cameras base line. M-EGF has offered the best results in terms of providing error-free filtered matched points in all tests. This can be attributed to the high restriction level of this filter, where the probability for the mismatched point to pass throughput the three co-planarity equations is nearly zero. M-EGF and S-EGF are affected by the quality of the Interior Orientation Elements (IOEs) and Exterior Orientation Parameters (EOPs) of the three cameras, leading to rejection a small number of corrected matched points, which can be avoided with professionally manufactured ORN systems. Tests illustrated that S-EGF and M-EGF are extremely high-speed and S-EGF is the faster and M-EGF, C-2DF, and A-2DF comes after, respectively. All results indicated that M-EGF is the best, as it is fast, restricted, reliable, and error-free technique and is suitable for real-time precise ORN applications.

Challenges and limitation of wearable sensors used in firefighters’ protective clothing

This review aims to present recent improvements and existing challenges in the design of wearable sensors used in the firefighters’ protective clothing. Wearable sensors are generally used directly on the body or placed on wearable items to monitor data for the safety of firefighters. Recently, wearable sensors have attracted much attention from researchers and experts. Most investigations have addressed novel designs for wearable sensors to enhance firefighters’ safety measures and reduce the risk of exposure to fires. This article is an attempt to review design limitations of wearable sensors for future developments and improve existing shortcomings. The growing body of knowledge focused on the application of wearable technology to monitor firefighters’ activity, health, and body temperature. In the following, we have discussed the trials of the design of the existing sensors. Finally, moisture and radiation as common exterior parameters in fire events are discussed which received less attention and have major impact on the performance of firefighters’ wearable sensors.

3D MODELING WITH 5K 360° VIDEOS

Abstract. Video acquisition with 360° (spherical) cameras is becoming increasingly popular for the opportunity to capture the entire scene around the user in a relatively short time. The method can also be attractive for photogrammetric applications. As the overlap between consecutive frames is undoubtedly guaranteed, 3D models can be generated with an automated processing workflow. The paper illustrates the results achieved with 5k 360° videos captured with different Insta360 cameras. As the number of frames can become large, two complementary solutions are proposed to provide approximate initial exterior orientation parameters: the integration of the trajectory captured through GNSS, and the creation of an acquisition plan with a GIS-based application. The availability of approximated EO parameters provides a visibility map between the frames and reduces the computational cost during image matching. Experimental results demonstrate that such preliminary information is necessary for large datasets. Indeed, the photogrammetric processing of the entire dataset without the proposed preliminary EO parameters resulted in unreliable or incomplete orientation results.

INTEGRATED PROCESSING OF PHOTOGRAMMETRIC AND LASER SCANNING DATA FOR FRESCOES RESTORATION

Abstract. The integration of photogrammetry and Terrestrial Laser Scanner (TLS) techniques is often desirable for Cultural Heritage digitization, especially when high metric and radiometric accuracy is required, as for the documentation and restoration of frescoed spaces. Despite the many technological and methodological advances in both techniques, their full integration is still not straightforward. The paper investigates a methodology where TLS and photogrammetric data are processed together through an image matching process between RGB panoramas acquired by the scanner’s integrated camera and frame imagery acquired through photographic equipment. The co-registration is performed without any Ground Control Point (GCP) but using the automatically extracted tie points and the known Exterior Orientation parameters of the panoramas (gathered from TLS data original registration) to set the ground reference. The procedure allowed for effective integrated processing with the possibility of take benefit from TLS and photogrammetry pros and demonstrated to be reliable even with low overlap between photogrammetric images.

AUTOMATIC ENRICHMENT OF INDOOR 3D MODELS USING A DEEP LEARNING APPROACH BASED ON SINGLE IMAGES WITH UNKNOWN CAMERA POSES

Abstract. 3D building modeling is a diverse field of research with a multitude of challenges, where data integration is an inherent component. The intensively growing market of BIM-related consumer applications requires methods and algorithms that enable efficient updates of existing 3D models without the need for cost-intensive data capturing and repetitive reconstruction processes. We propose a novel approach for semantic enrichment of existing indoor models by window objects, based on amateur camera RGB images with unknown exterior orientation parameters. The core idea of the approach is the parallel estimation of image camera poses with semantic recognition of target objects and their automatic mapping onto a 3D vector model. The presented solution goes beyond pure texture matching and links deep learning detection techniques with camera pose estimation and 3D reconstruction. To evaluate the performance of our procedure, we compare the estimated camera parameters with reference data, obtaining median values of 13.8 cm for the camera position and 1.1° for its orientation. Furthermore, a quality of 3D mapping is assessed based on the comparison to the reference 3D point cloud. All the windows presented in the data source were detected successfully, with a mean distance between both point sets equal to 3.6 cm. The experimental results prove that the presented approach achieves accurate integration of objects extracted from single images with an input 3D model, allowing for an effective increase of its semantic coverage.

Higher-Dimensional Regular Reissner–Nordström Black Holes Associated with Linear Electrodynamics

Following the interpretation of matter source that the energy-momentum tensor of anisotropic fluid can be dealt with effectively as the energy-momentum tensor of perfect fluid plus linear (Maxwell) electromagnetic field, we obtain the regular higher-dimensional Reissner–Nordström (Tangherlini–RN) solution by starting with the noncommutative geometry-inspired Schwarzschild solution. Using the boundary conditions that connect the noncommutative Schwarzschild solution in the interior of the charged perfect fluid sphere to the Tangherlini–RN solution in the exterior of the sphere, we find that the interior structure can be reflected by an exterior parameter, the charge-to-mass ratio. Moreover, we investigate the stability of the boundary under mass perturbation and indicate that the new interpretation imposes a rigid restriction upon the charge-to-mass ratio. This restriction, in turn, permits a stable noncommutative black hole only in the 4-dimensional spacetime.

A Two-Step Calibration Method for Vision Measurement With Large Field of View

Nowadays, the commonly used large view calibration methods usually have the problems of low measurement accuracy, big on-site operation limitation, or high tolerance requirement for target preparation. In order to resolve the previously mentioned issues, a two-step calibration method for the interior and exterior parameters of multicameras is proposed based on the principle of industrial close-range photogrammetry. In the first step, by using a small-scale cross target at the closer range, the interior parameter of the front section of the camera is calibrated based on the pyramid method. In the second step, by setting a proper number of serialized targets at the further range of the measured area, the exterior parameter is calibrated based on space resection with multi-image. Finally, the interior and exterior parameters are all optimized by using the bundle adjustment method to get accurate measurement results. A number of experiments are performed to verify the feasibility and accuracy of this proposed method. As per the experiment results, with a camera resolution of <inline-formula> <tex-math notation="LaTeX">$2448 \times 2048$ </tex-math></inline-formula> pixels, the reprojection error is less than 0.11 pixels. For the measuring volume of <inline-formula> <tex-math notation="LaTeX">$10.0 \times 8.4 \times 3.6$ </tex-math></inline-formula> m, the maximum absolute error of 3-D measurement is 0.42 mm. The measurement error for the flapping angle of rotor whose diameter is 10 m is less than 4.5&#x0025;. By using this method, the interior parameter can be collected in the laboratory, while the exterior parameter can be collected in the open field separately. This method has high practicality under the actual engineering working environment.

Orthorectification Model for Extra-Length Linear Array Imagery

The orthorectification accuracy for an extra-length linear array image is restricted by large distortions of the lens, the strong correlation of exterior orientation parameters (EOPs). This paper presents an orthorectification model for solving the above problems through dividing the distortion into two zones and modeling the distortions using two different models, as well as two constrains. The first one is the axial vector rotation coplanar, i.e., the axial vector is located in the imaging plane after two rotations, and the second one is the angle consistent, i.e., the angle between the ground space viewing vectors is equal to the angle between the ideal image space viewing vectors. The Beijing-2(BJ2) satellite images with a linear array of 30,076 pixels and the SPOT- HRV with 6000 pixels are used for verification of our method. The experimental results demonstrate that the orthorectification accuracy of the BJ2 image is improved from 1.938m (about 2.42 pixel) to 1.440m (about 1.80 pixel) and the orthorectification accuracy of the SPOT image is improved from 14.668m (about 1.47 pixel) to 8.657m (about 0.87 pixel). Thus, it can be concluded that the proposed method can achieve a higher accuracy than the traditional orthorectification method does.

Biberiye Uçucu Yağının Yumurtacı Bıldırcın Rasyonlarına İlave Edilmesi Üzerine Bir Araştırma

The purpose of the current study was to detect the effects of different levels of rosemary volatile oil (VO) supplemented to laying quail diets on performance, egg quality parameters, and egg yolk oxidative stability. In this study, a total of 105 laying quails (Coturnix coturnix Pharaoh) at the age of 6 weeks were used and 3 main groups were formed, each containing 35 quails. Also, each main group was separated into 5 replicates containing an equal number of quails. Quails were fed a control diet without rosemary VO or diets which were containing at different levels (200 or 400 mg/kg) of rosemary VO supplemented to the control diet. At the end of the study, which was continued for a total of 56 days, no significant difference was found in the feed intake, feed efficiency and egg weights among the control and experimental groups. The supplementation of rosemary VO at the levels of 200 and 400 mg/kg to the diets caused an increase in quails' egg production (P&amp;lt;0.01). However, there was no effect on the exterior and interior egg quality parameters. A significant decrease in malondialdehyde (MDA) levels in egg yolk were observed with both levels of rosemary VO at storage (+4 0C) for 7 (P&amp;lt;0.001) and 28 (P&amp;lt;0.01) days. Considering these data, it was concluded that rosemary VO can be supplemented to laying quail diets as a natural antioxidant without negatively affecting performance and egg quality parameters.

A Pipeline for Automated Processing of Declassified Corona KH-4 (1962–1972) Stereo Imagery

The Corona KH-4 reconnaissance satellite missions from 1962-1972 acquired panoramic stereo imagery with high spatial resolution of 1.8-7.5 m. The potential of 800,000+ declassified Corona images has not been leveraged due to the complexities arising from handling of panoramic imaging geometry, film distortions and limited availability of the metadata required for georeferencing of the Corona imagery. This paper presents Corona Stereo Pipeline (CoSP): A pipeline for processing of Corona KH-4 stereo panoramic imagery. CoSP utlizes a deep learning based feature matcher SuperGlue to automatically match features point between Corona KH-4 images and recent satellite imagery to generate Ground Control Points (GCPs). To model the imaging geometry and the scanning motion of the panoramic KH-4 cameras, a rigorous camera model consisting of modified collinearity equations with time dependent exterior orientation parameters is employed. The results show that using the entire frame of the Corona image, bundle adjustment using well-distributed GCPs results in an average standard deviation (SD) of less than 2 pixels. The distortion pattern of image residuals of GCPs and y-parallax in epipolar resampled images suggest that film distortions due to long term storage as likely cause of systematic deviations. Compared to the SRTM DEM, the Corona DEM computed using CoSP achieved a Normalized Median Absolute Deviation (NMAD) of elevation differences of ~4 m over an area of approx. 4000 $km^2$. We show that the proposed pipeline can be applied to sequence of complex scenes involving high relief and glacierized terrain and that the resulting DEMs can be used to compute long term glacier elevation changes over large areas.