Mapping Camera Research Articles

Inventarisasi dan Pemetaan Keberagaman Tanaman Obat Keluarga di Desa Rambipuji, Kabupaten Jember: Upaya Pelestarian Pengetahuan Lokal

This study aims to map the diversity of family medicinal plants (TOGA) in Rambipuji Village, Rambipuji District, Jember Regency, East Java. TOGA is an important component in the lives of Indonesian people as an affordable and easily accessible alternative to traditional medicine. Nonetheless, in Rambipuji Village, the potential of TOGA has not been optimally utilized, and information regarding its geographical distribution is still limited. Therefore, accurate mapping is needed to support the utilization and preservation of TOGA. The research method involved three main stages: preparation, implementation, and map-making. In the preparation stage, the research team consulted with village officials to acquire permission and support. The implementation stage involved collecting coordinate point data of various medicinal plant species in the three main hamlets, namely Gudang Karang, Krajan, and Curah Ancar, utilizing the GPS Map Camera application. This data was later analyzed and imported into the QGIS application for the creation of an interactive map. The resulting map shows that there are 126 coordinate points in Gudang Karang Hamlet, 41 points in Krajan Hamlet, and 32 points in Curah Ancar Hamlet, with a total of 48 different medicinal plant species. The resulting TOGA diversity map provides a clear visual picture of the distribution of medicinal plants in Rambipuji Village, whereby it is expected to become a learning tool for the community and support the preservation and sustainable use of TOGA. This mapping is designed to improve the welfare of the community through a more targeted and coordinated utilization of medicinal plants.

Wavelets for Self-Calibration of Aerial Metric Camera Systems

In this paper, wavelets are applied to develop new models for the self-calibration of aerial metric camera systems. It is well known and mathematically proven that additional parameters (APs) can compensate image distortions and remaining error sources by a rigorous photogrammetric bundle-block adjustment. Thus, kernel functions based on orthogonal wavelets (e. g., asymmetric Daubechies wave- lets, least asymmetric Daubechies wavelets, Battle-Lemarié wavelets, Meyer wavelets) are used to build the wavelets-based family of APs for self-calibrating digital frame cameras. These new APs are called wavelet APs. Its applications in rigorous tests are accomplished by using aerial images taken by an airborne digital mapping camera in situ and practical calibrations. The test results demonstrate that these orthogonal wavelet APs are applicable and largely avoid the risk of over-parameterization. Their external accuracy is evaluated using reliable and high precision check points in the calibration field.

Vision-based autonomous robots calibration for large-size workspace using ArUco map and single camera systems

The low positioning accuracy of industrial robots limits their application in industry. Vision-based kinematic calibration, known for its rapid processing and economic efficiency, is an effective solution to enhance this accuracy. However, most of these methods are constrained by the camera's field of view, limiting their effectiveness in large workspaces. This paper proposes a novel calibration framework composed of monocular vision and computer vision techniques using ArUco markers. Firstly, a robot positioning error model was established by considering the kinematic error based on the Modified Denavit-Hartenberg model. Subsequently, a calibrated camera was used to create an ArUco map as an alternative to traditional single calibration targets. The map was constructed by stitching images of ArUco markers with unique identifiers, and its accuracy was enhanced through closed-loop detection and global optimization that minimizes reprojection errors. Then, initial hand-eye parameters were determined, followed by acquiring the robot's end-effector pose through the ArUco map. The Levenberg-Marquardt algorithm was employed for calibration, involving iterative refinement of hand-eye and kinematic parameters. Finally, experimental validation was conducted on the KUKA kr500 industrial robot, with laser tracker measurements as the reference standard. Compared to the traditional checkerboard method, this new approach not only expands the calibration space but also significantly reduces the robot's absolute positioning error, from 1.359 mm to 0.472 mm.

Identifying fine-scale archaeological features using KH-9 HEXAGON mapping and panoramic camera images: evidence from Liangzhu Ancient City

ABSTRACT Historical fine-scale information of archaeological landscapes is crucial in archaeological investigations. However, documenting such information using satellite sensor data prior to 2000 remains a daunting challenge. Images from the declassified archives of KH-9 HEXAGON (KH-9) cameras, including the panoramic camera system (PCS) and mapping camera system (MCS), offer fine-scale information about archaeological sites. However, noise, contrast distortion and the availability of only a single panchromatic band can limit their potential, particularly for identifying features in subtropical climates within heterogeneous landscape types. This paper focuses on developing a novel multifaceted analytical framework with two components: image pre-processing and feature identification. The image pre-processing component is divided into two steps. First, a trained stationary wavelet transform (SWT) based on the normalized sill (NS) is developed to not only de-noises the image, but also preserve its original image characteristics. Then, the contrast of the de-noised images is optimized by the multi-resolution Top-hat (MTH) using multi-scale information. In the feature identification component, the MCS image is analysed using spatial colour composite write function memory (SCCWFM) and spatial novelty detection (SND). An ultra-fine spatial three-dimensional colour composite (UFSTCC) image and ultra-fine spatial digital surface model (UFSDSM) are produced to aid interpretation of the KH-9 PCS images. The proposed processing pipelines were tested on KH-9 MCS and PCS images of the World Heritage site at Liangzhu Ancient City (LAC) in China, which is characterized by a subtropical climate and a heterogeneous landscape types. The proposed pre-processing pipeline improved considerably the appearance of these images across the LAC landscape while maintaining the original image information. The developed digital analytical approaches for KH-9 PCS and MCS images facilitated straightforward identification of archaeological features in the LAC. The proposed framework has the potential to increase exploitation of the available KH-9 images in archaeological applications.

Effects of camera calibration on the accuracy of Unmanned Aerial Vehicle sensor products

Utilization of Unmanned Aerial Vehicles (UAV) mounted with non-metric consumer grade digital cameras is on the rise globally due to their affordability and ease of operation. For high-accuracy UAV products, there is a need for accurate camera parameters determined through camera calibration. Camera calibration can be performed before (pre-calibration) or during the bundle block adjustment (self-calibration). This study aims to analyze the effect of camera calibration parameters on the accuracy of UAV products namely Digital Elevation Model (DEM) and orthoimage. Camera calibration parameters are estimated using two approaches namely self-calibration which deploys 3D image information of the scene in a bundle adjustment and a 2D reference object-based approach known as Zhang’s technique which requires image information of a planar pattern. A DJI FC220 camera mounted on a DJI Mavic Pro UAV was used. Self-calibration was deployed in Agisoft Metashape software based on Brown’s method and Zhang’s technique was deployed in MATLAB and OpenCV. Based on internal measures of accuracy, OpenCV yields the least reprojection error of 0.14 followed by MATLAB (0.79) and self-calibration (1.21). Processing without calibration yields the highest reprojection error of 2.18. Based on external measures of accuracy, that is the geometric accuracy of UAV products, self-calibration yields the least RMSE of 8.2 and 1.4 cm, for the horizontal and vertical, respectively, followed by Zhang’s technique with 9.6 and 2.3 cm in MATLAB and 13.5 and 4.3 cm in OpenCV. Processing without calibration yields the highest vertical RMSE of 20.0 and 22.9 cm for the horizontal and vertical, respectively. Comparison of accuracy of UAV mapping products computed with and without calibration emphasizes the need for camera calibration to optimize accuracy of UAV products. This study recommends assessing other photogrammetric mapping software and camera calibration approaches and the effect of flying heights on calibration parameters and mapping accuracy.

Non-rigid scene reconstruction of deformable soft tissue with monocular endoscopy in minimally invasive surgery.

The utilization of image-guided surgery has demonstrated its ability to improve the precision and safety of minimally invasive surgery (MIS). Non-rigid scene reconstruction is a challenge in image-guided system duo to uniform texture, smoke, and instrument occlusion, etc. METHODS: In this paper, we introduced an algorithm for 3D reconstruction aimed at non-rigid surgery scenes. The proposed method comprises two main components: firstly, the front-end process involves the initial reconstruction of 3D information for deformable soft tissues using embedded deformation graph (EDG) on the basis of dual quaternions, enabling the reconstruction without the need for prior knowledge of the target. Secondly, the EDG is integrated with isometric nonrigid structure from motion (Iso-NRSFM) to facilitate centralized optimization of the observed map points and camera motion across different time instances in deformable scenes. For the quantitative evaluation of the proposed method, we conducted comparative experiments with both synthetic datasets and publicly available datasets against the state-of-the-art 3D reconstruction method, DefSLAM. The test results show that our proposed method achieved a maximum reduction of 1.6mm in average reconstruction error compared to method DefSLAM across all datasets. Additionally, qualitative experiments were performed on video scene datasets involving surgical instrument occlusions. Our method proved to outperform DefSLAM on both synthetic datasets and public datasets through experiments, demonstrating its robustness and accuracy in the reconstruction of soft tissues in dynamic surgical scenes. This success highlights the potential clinical application of our method in delivering surgeons with critical shape and depth information for MIS.

FPGA-Based Magnetic Field Camera for Dynamic Magnetic Field Mapping

FPGA-Based Magnetic Field Camera for Dynamic Magnetic Field Mapping

New Eldercare Robot with Path-Planning and Fall-Detection Capabilities

The rapid growth of the elderly population has led to an increased demand for effective and personalized eldercare solutions. In this paper, the design and development of an eldercare robot is presented. This robot is specifically tailored to meet the two specific challenges faced by the elderly. The first is the continuous indoor tracking of the elder, while the second is the fall detection. A comprehensive overview of the hardware and software components, as well as the control architecture of the robot is presented. The hardware design of the robot incorporates a range of features, including a perception system comprising a 2D Lidar, IMU, and camera for environment mapping, localization, and fall detection. The software stack of the robot is explained as consisting of layers for perception, mapping, and localization. The robot is tested experimentally to validate its path planning capability by using Hector SLAM and the RRT* technique. Experimental path planning has shown a positioning accuracy of 93.8% on average. Elderly fall detection is achieved by using the YOLOv7 algorithm at a percentage of 96%. Experimental results have been discussed and evaluated.

PEMETAAN KAPAL TERBENGKALAI BERBASIS WEB DI WILAYAH OPERASIONAL PERAIRAN PPN KARANGANTU

The current condition of the Karangantu Archipelago Fisheries Port (PPN) is still considered unfavorable because the operational area of ​​the port waters looks dirty and shabby. There are many ships that are no longer used in the Karangantu PPN operational area. Based on the results of interviews with PPN Karangantu officers, there are several things that cause ships to be abandoned in the operational area of ​​PPN Karangantu waters, including the age of the ship, costs or capital, and human resources. Abandoned ships that are not immediately handled will result in obstacles to the flow of water traffic at the port and create a risk of collision for other ships passing by. Therefore, web-based mapping of abandoned ships (WebGIS) is needed in the operational area of ​​PPN Karangantu waters. It is hoped that the results of this abandoned ship mapping can be used by the port to report abandoned ships to the center so that they can immediately take action to transport the abandoned ships. With the existence of WebGIS mapping abandoned ships, the port does not need to go directly to the field to find the location of abandoned ships because the information can be accessed via the web that has been created. Data collection on coordinate points was carried out by field survey and tagging using the GPS Map Camera application. The research results show that there are 74 abandoned ships in the Karangantu PPN operational area.

Reconstructing historical urban landscapes from KH-9 HEXAGON mapping camera system imagery: an example of Hangzhou City

ABSTRACT Declassified images from the Keyhole (KH)-9 HEXAGON mapping camera system (MCS) offer fine-scale details of urban regions. However, these images have seldom been utilized in urban research due to challenges in labelling (collecting training samples), having only a single panchromatic band and classification. To tackle these limitations, this paper focuses on developing a multi-stage reconstructed historical fine-scale urban landscape (RHFUL) pipeline for KH-9 HEXAGON MCS. The proposed pipeline first integrates internalized parameters, hierarchical object-based image analysis properties and class variability to synthesize new features, abbreviated to IHC. Second, the pipeline uses a weak semi-automated supervised labelling (WSSL) approach to acquire training samples. Finally, the training samples and generated features are subjected to the SegNet deep learning architecture. The performance of each step was assessed against corresponding state-of-the-art benchmark approaches for each of synthesizing features, labelling and classification. In the proposed RHFUL pipeline, the proposed IHC provided the most salient information for urban classification, WSSL labelled urban features more accurately, and the SegNet architecture classified more accurately the urban features relative to the benchmarks. Considering the potential advantages, but also limitations of KH-9 HEXAGON MCS images, further research should be undertaken, particularly drawing on the current advances in pattern recognition techniques for contemporary digital satellite sensors.

Exploring the Use of Terrestrial Robots for Atmospheric Electricity Measurement

Measuring the electric field is a central goal in electrostatics research, such as the study of electrostatics in atmospheric processes. It serves as a key indicator for various atmospheric phenomena, including the presence of lightning, dust or charged clouds. Traditionally, electric field measurements have been conducted from static platforms, with limited mobile measurements from airborne platforms such as balloons or, more recently, Unmanned Aerial Vehicles (UAVs). Here, we explore the potential of terrestrial robots to measure the electric field with some level of autonomy, such as during supervised navigation between user-defined waypoints. We mount a field mill on a four-wheeled rover and use a Global Navigation Satellite System (GNSS) to track the position of its measurements during an outdoor survey. The robot has a depth camera for 3D terrain mapping to contextualise local field measurements. We present plans for future research, including the use of semi-autonomous identification and exploration of electrostatic ‘hotspots’; and deployment of multiple robots (e.g., six) in a ‘sparse swarm’ configuration. We consider opportunities to employ such a robot system in environmental science or space research, for instance on Mars or the moon, where an understanding of electrostatic processes could be significant for future space missions.

Textrm{O}^+ density distribution in the nightside ionosphere reconstructed from ISS-IMAP/EUVI data

The textrm{O}^+ density distribution in the nightside ionosphere has been reconstructed from extreme ultraviolet (EUV) images taken by the EUVI-B imager of the International Space Station Ionosphere, Mesosphere, upper Atmosphere, and Plasmasphere mapping (ISS-IMAP) cameras. The EUVI-B imager covers the wavelength range from about 70 nm to 110 nm and mainly observes the 91.1 nm emission from the recombination of textrm{O}^+ ions and electrons. Assuming that the electron density is equal to the textrm{O}^+ density in the F-region where the imager observes, the EUV intensity observed by EUVI-B is approximately proportional to the line-of-sight integral of the square of the textrm{O}^+ density. This enables us to estimate the textrm{O}^+ density distribution in the F-region from a sequence of EUVI-B data in each International Space Station (ISS) orbit with a Bayesian method. We demonstrate the reconstruction of the textrm{O}^+ distribution. In particular, the textrm{O}^+ density structure of the equatorial ionization anomaly (EIA) in the vicinity of an ISS orbit is obtained.Graphical

Optimising camera and flight settings for ultrafine resolution mapping of artificial night-time lights using an unoccupied aerial system

Light pollution from artificial night-time lights (ANTLs) is a global health, economic, and environmental issue. Remote sensing and unoccupied aerial systems (UASs) provide efficient and cost-effective ways to study ANTL spatial patterns and dynamics over large areas. With ultrahigh-resolution images that can identify individual light sources, UAS offers more detailed image than satellite imagery. However, standardisation and optimisation of camera and flight settings during the acquisition ANTL UAS images is lacking. The aim of this paper is to determine the camera and flight settings to capture high-quality ANTL using a DJI Matrice 300 RTK aircraft with a Zenmuse P1 camera. It emphasises the importance of selecting appropriate camera settings for high-quality ANTL images, which can benefit future research. Results show significant image quality gains when camera and flight settings are chosen appropriately in relation to the lighting conditions. We present three experiments demonstrating a range of camera settings, and we provide practical recommendations for high-quality night-time image collection. The optimal camera settings were determined to be an exposure time of 0.0166 s, ISO of 25600, and aperture of 2.97. This experiment produced outstanding results, with 85% of images having a blur extent below 0.40.

Design of Optical-mechanical System of Catadioptric Aerial Mapping Camera Based on Secondary Mirror Image Motion Compensation

Design of Optical-mechanical System of Catadioptric Aerial Mapping Camera Based on Secondary Mirror Image Motion Compensation

Lake Water Quality Assessment Based on Multispectral Unmanned Aerial Vehicle Images and In-Situ Testing

In recent years, Universiti Tun Hussein Onn Malaysia has experienced several developments in buildings and road construction. A stormwater retention pond was built as a part of the construction plan to prevent the occurrence of floods downstream of the development sites. However, poor monitoring and maintenance of the pond caused water contamination, which had bad effects on the nearby environment and lake ecosystem. Therefore, this study was conducted to assess the current water quality in the pond by using a combination between Multispectral Unmanned Aerial Vehicle images and in-situ sampling. Four water quality parameters were investigated, namely turbidity, dissolved oxygen, temperature, and pH. Aerial images were taken using a multispectral aerial mapping camera attached to UAV. The in-situ testing was performed on the same day of image capturing at 5 different locations. According to the results, it was found that the pH and water temperature were within the normal levels with maximum and minimum values of 7.34 and 6.94 for pH and 28.39oC and 28.04oC for temperature, respectively. Turbidity readings appaired with a significant variation with a maximum value of 30.9 NTU at the lake outlet and a minimum value of 18.8 NTU at the middle of the lake. Dissolved oxygen concentration was noticed to be low, with an average value of 4.3 mg/l. Water quality mapping showed the concentration distribution of each water quality parameter along the lake surface. In each map, the differences in terms of colours were found to be not significant because of the size of the lake, which is relatively small, as well as because of the small differences in terms of water quality ridings at each sampling point.

A new approach to surveying cliff-dwelling endangered plants using drone-based nap-of-the-object photography: A case study of Clematis acerifolia

Cliffs are essential refuges for endangered plants, but their inaccessibility and inadequate reliable survey methods make the assessment of the survival status of these plants difficult. We employed the drone-based Nap-of-the-Object Photography (NoOP) technique, which involves flying close to an object's surface to automatically capture high-precision images of the object surface, to investigate an endangered cliff plant, Clematis acerifolia, in North China. A drone (DJI M300 RTK) equipped with a visible-light mapping camera (DJI Zenmuse P1) was used for automated NoOP flights, enabling the collection and reconstruction of millimeter-level, high-precision three-dimensional (3D) models of the cliff plots. Across 12 cliffs, we identified and measured the precise coordinates and values of eight microtopographical and five morphological characteristic factors for 1444 C. acerifolia plants. The Ground sampling distance (GSD) of the high-precision 3D models reached 0.10 cm/pixel, and the mean value of the root mean squared error of the sampled positioning coordinates was 4.65 cm/pixel. Correlation and correspondence analyses were used for data analyses. The results indicated that the growth status of C. acerifolia was significantly influenced by a complex factor interaction, particularly plant altitude, crevice width, and crevice length. This method for cliff plant surveys using drone-based NoOP determines the parameters for such surveys and demonstrates the effectiveness and potential of this method for cliff plant investigations. The primary microtopographical factors affecting the growth and distribution status of C. acerifolia were identified, providing pointers for translocation conservation and propagation reintroduction of this endangered plant.

Forest terrain and canopy height estimation using stereo images and spaceborne LiDAR data from GF-7 satellite

ABSTRACT Accurate estimation of forest terrain and canopy height is crucial for timely understanding of forest growth. Gao Fen-7 (GF-7) Satellite is China’s first sub-meter-level three-dimensional (3D) mapping satellite for civilian use, which was equipped with a two-line-array stereo mapping camera and a laser altimeter system that can provide stereo images and full waveform LiDAR data simultaneously. Most of the existing studies have concentrated on evaluating the accuracy of GF-7 for topographic survey in bare land, but few have in-depth studied its ability to measure forest terrain elevation and canopy height. The purpose of this study is to evaluate the potential of GF-7 LiDAR and stereo image for forest terrain and height measurement. The Airborne Laser Scanning (ALS) data were utilized to generate reference terrain and forest vertical information. The validation test was conducted in Pu’er City, Yunnan Province of China, and encouraging results have obtained. The GF-7 LiDAR data obtained the accuracy of forest terrain elevation with RMSE of 8.01 m when 21 available laser footprints were used for results verification; meanwhile, when it was used to calculate the forest height, R2 of 0.84 and RMSE of 3.2 m were obtained although only seven effective footprints were used for result verification. The canopy height values obtained from GF-7 stereo images have also been proven to have high accuracy with the resolution of 20 m × 20 m compared with ALS data (R2 = 0.88, RMSE = 2.98 m). When the results were verified at the forest sub-compartment scale that taking into account the forest types, further higher accuracy (R2 = 0.96, RMSE = 1.23 m) was obtained. These results show that GF-7 has considerable application potential in forest resources monitoring.

Investigating Earthquake-Induced Changes in the Persian Gulf Marginal Strip, Using Sentinel-1A Images and Radar Interferometry

Geohazards, such as earthquakes, pose significant threats to human life and infrastructure in various regions across the globe. Iran, in particular, is highly vulnerable to earthquakes due to its unique structural and tectonic characteristics. Therefore, it is crucial to monitor geohazards in order to mitigate their impacts. Several techniques, including the global positioning system, geodesy, tacheometry, and mapping cameras, have been developed for this purpose. Among these methods, radar interferometry has emerged as a particularly accurate and cost-effective approach. It is capable of operating under all weather conditions, 24 h a day, and can cover large areas with high spatial and temporal resolution. In this research, we employed Sentinel 1A images and radar interferometry to investigate the changes in the Earth’s surface following earthquakes in the marginal strip of the Persian Gulf. Specifically, our focus was on earthquakes in Bandar Khamir, and we analyzed the Earth’s surface changes three days and fifteen days after the events. The findings of our study revealed that the most significant uplift occurred around Bandar Khamir, with an uplift rate of 14 cm. Conversely, the highest subsidence was observed near Bandar Charak, with a subsidence of 12 cm. Furthermore, we observed a rise of 32 cm around the eastern and northern regions of Bandar Khamir 11 days after the initial period, accompanied by a subsidence of 31 cm around Bandar Lange and Bandar Charak. These results underscore the importance of continuous monitoring of earthquakes and their impact on the Earth’s surface, particularly in coastal areas where the effects on the water table and coastal infrastructure can be severe. In conclusion, this study highlights the significance of employing radar interferometry as a powerful tool for monitoring and assessing the impacts of earthquakes.

Establishment of Herbal Garden in the Galgotias University Campus - An Ex-situ Conservation Approach

Herbal gardens are a rich source of plant-based medicines and their derivatives. Both in urban and rural locations, herbal plants have an important role in traditional medicine. The development of herbal garden includes propagation, multiplication, research, education, and extension activities as well. In the present study, we took herbal plants from different nurseries of Noida and Greater Noida of Gautam Buddha Nagar district, Uttar Pradesh and developed herbal garden in the Galgotias University campus. The herbal garden has approximately 45 different plants, including shrubs (22), trees (13), herbs (9) and grass (1) etc. The geotagged photographs of each herbal plant were captured using GPS map camera. The ex-situ conservation of herbal garden in Galgotias University would help and motivate faculties and students of the university, to undertake further research on medicinal plants.

Dual-function depth camera array for inline 3D reconstruction of complex pipelines

Efficiently reconstructing dense, complete, and accurate 3D models of long and complex pipelines remains a challenging task. The complex pipelines often contain geometrically unknown curved segments that are difficult to reconstruct by the existing monocular or stereo camera-based approaches. This paper proposes a dual-function depth camera array (DF-DCA) based inline inspection (ILI) system for automated 3D reconstruction of underground pipelines that contain arbitrarily curved segments. This newly developed DCA system consists of a central-positioned depth camera for pipes routes estimation based on a reference-assisted vSLAM framework and four oblique-viewed depth cameras for pipe surface mapping. A subvolume-based approach is used to merge the RGBD sequences from the cameras to obtain an accurate 3D map of the pipeline without using DCA calibration. The results showed that the system can reconstruct high-quality 3D models of the pipes with <0.2% distance error for straight pipes, and with centimeter-level geometric accuracy for pipelines with both straight and curved sections.