Mapping Camera Research Articles

Research on the Control Point Extraction Method of Chinese Gaofen 7 Satellite Using Altimeter and Footprint Camera

Satellite laser altimetry is one of the most advanced information acquisition technologies in Earth observation system. It can provide high-accuracy elevation information, however, due to the lack of detail intensity information, its planimetric accuracy is usually worse than the elevation accuracy. Gaofen 7 (GF-7) satellite scheduled for launch in 2019 will be equipped with laser altimeter, footprint camera, stereo mapping camera, etc. The laser altimeter together with the footprint camera was designed to provide high accuracy ground control point of satellite mapping. The laser altimeter can provide the high-accuracy elevation information and the joint processing of footprint camera and stereo mapping camera can provide high-accuracy planimetric information. Therefore, this paper mainly studies the technology of extracting high-accuracy control points based on GF-7 satellite’s altimeter, footprint camera and stereo mapping camera using a simulated dataset extracted from Quickbird image and ICESat altimetric data.

Design and Data Processing of China's First Spaceborne Laser Altimeter System for Earth Observation: GaoFen-7

The GaoFen-7 (GF-7) satellite, which was launched on November 3, 2019, is China's first civilian submeter stereo mapping satellite. The satellite is equipped with the first laser altimeter officially in China for earth observation. Except for the laser altimeter, the GF-7 spaceborne laser altimeter system also includes two laser footprint cameras and a laser optical axis surveillance camera. The laser altimeter system is designed and used to assist improving the elevation accuracy without ground control points of the two line-array stereo mapping cameras. This article details the design of the GF-7 spaceborne laser altimeter system, its ranging performance in the laboratory, and its data processing method. The type of data products is also released. These data will play a vital role in the application of geography, glaciology, forestry, and other industries.

Practical Calibration Method for Aerial Mapping Camera Based on Multiple Pinhole Collimator

Calibration of aerial mapping camera has an important influence on the applications of earth observation. However, the traditional aerial mapping cameras calibrations depend on large-scale calibration target or collimated light, moreover, it is difficult to build the large-scale calibration targets and the collimated light method requires an accurate turntable and a high-precision goniometer, which is a kind of expensive instrument. To solve this problem, this paper proposes a novel high-precision calibration method which is not restricted by the rigorous conditions, such as small aperture, unevenly energy distribution and expensive equipment. Specifically, a collimator and an elaborately designed multiple pinhole mask are firstly used to generate the collimated light of a large aperture with known directions to simulate the calibration targets at infinity. Then, the camera takes pictures for the aperture of the multiple pinhole collimator at multiple angles to ensure that the image points cover the entire detector. Thirdly, the final calibrated results are obtained by solving the data acquired from multiple angles. Finally, the proposed method is verified by Monte-Carlo simulation and real experimental data, whose results indicate that our method can reach the same accuracy performance as the existing methods at lower cost and faster speed, and thus is practical for engineering application.

Dense Image-Matching via Optical Flow Field Estimation and Fast-Guided Filter Refinement

The development of an efficient and robust method for dense image-matching has been a technical challenge due to high variations in illumination and ground features of aerial images of large areas. In this paper, we propose a method for the dense matching of aerial images using an optical flow field and a fast-guided filter. The proposed method utilizes a coarse-to-fine matching strategy for a pixel-wise correspondence search across stereo image pairs. The pyramid Lucas–Kanade (L–K) method is first used to generate a sparse optical flow field within the stereo image pairs, and an adjusted control lattice is then used to derive the multi-level B-spline interpolating function for estimating the dense optical flow field. The dense correspondence is subsequently refined through a combination of a novel cross-region-based voting process and fast guided filtering. The performance of the proposed method was evaluated on three bases, namely, the matching accuracy, the matching success rate, and the matching efficiency. The evaluative experiments were performed using sets of unmanned aerial vehicle (UAV) images and aerial digital mapping camera (DMC) images. The results showed that the proposed method afforded the root mean square error (RMSE) of the reprojection errors better than ±0.5 pixels in image, and a height accuracy within ±2.5 GSD (ground sampling distance) from the ground. The method was further compared with the state-of-the-art commercial software SURE and confirmed to deliver more complete matches for images with poor-texture areas, the matching success rate of the proposed method is higher than 97% while SURE is 96%, and there is 47% higher matching efficiency. This demonstrates the superior applicability of the proposed method to aerial image-based dense matching with poor texture regions.

Subsurface robotic exploration for geomorphology, astrobiology and mining during MINAR6 campaign, Boulby Mine, UK: part I (Rover development)

AbstractAutonomous exploration requires the use of movable platforms that carry a payload of instruments with a certain level of autonomy and communication with the operators. This is particularly challenging in subsurface environments, which may be more dangerous for human access and where communication with the surface is limited. Subsurface robotic exploration, which has been to date very limited, is interesting not only for science but also for cost-effective industrial exploitation of resources and safety assessments in mines. Furthermore, it has a direct application to exploration of extra-terrestrial subsurface environments of astrobiological and geological significance such as caves, lava tubes, impact or volcanic craters and subglacial conduits, for deriving in-situ mineralogical resources and establishing preliminary settlements. However, the technological solutions are generally tailor-made and are therefore considered as costly, fragile and environment-specific, further hindering their extensive and effective applications. To demonstrate the advantages of rover exploration for a broad-community, we have developed KORE (KOmpact Rover for Exploration); a low-cost, re-usable, rover multi-purpose platform. The rover platform has been developed as a technological demonstration for extra-terrestrial subsurface exploration and terrestrial mining operations pertaining to geomorphological mapping, environmental monitoring, gas leak detections and search and rescue operations in case of an accident. The present paper, the first part of a series of two, focuses on describing the development of a robust rover platform to perform dedicated geomorphological, astrobiological and mining tasks. KORE was further tested in the Mine Analogue Research 6 (MINAR6) campaign during September 2018 in the Boulby mine (UK), the second deepest potash mine in Europe at a subsurface depth of 1.1 km, the results of which will be presented in the second paper of this series. KORE is a large, semi-autonomous rover weighing 160 kg with L × W × H dimensions 1.2 m × 0.8 m × 1 m and a payload carrying capacity of 100 kg using 800 W traction power that can power to a maximum speed of 8.4 km h−1. The rover can be easily dismantled in three parts facilitating its transportation to any chosen site of exploration. Presently, the main scientific payloads on KORE are: (1) a three-dimensional mapping camera, (2) a methane detection system, (3) an environmental station capable of monitoring temperature, relative humidity, pressure and gases such as NO2, SO2, H2S, formaldehyde, CO, CO2, O3, O2,volatile organic compounds and particulates and (4) a robotic arm. Moreover, the design of the rover allows for integration of more sensors as per the scientific requirements in future expeditions. At the MINAR6 campaign, the technical readiness of KORE was demonstrated during 6 days of scientific research in the mine, with a total of 22 h of operation.

Calibration of mobile robot odometry and camera based on hand-eye calibration

The relative pose transformation among several sensors in mobile robots has a certain impact on the pose estimation of the robot. The traditional hand-eye calibration works out the matrix transformation relationship between the camera coordinate system and the robot end effector or its base coordinate system. As for the household sweeping robot, which use monocular camera for localization and mapping. A novel method is proposed calibrating the chassis odometry and the top-view camera. First, we get chassis poses from wheel encoder and camera poses from Perspective-n-Point(PnP).Then we use two types of poses to solve the formula we build which is similar to hand-eye calibration. We make some tests to ensure this method is feasible.

Sparse Depth Enhanced Direct Thermal-Infrared SLAM Beyond the Visible Spectrum

In this letter, we propose a thermal-infrared simultaneous localization and mapping (SLAM) system enhanced by sparse depth measurements from light detection ranging (LiDAR). Thermal-infrared cameras are relatively robust against fog, smoke, and dynamic lighting conditions compared to RGB cameras operating under the visible spectrum. Exploiting thermal-infrared cameras for motion estimation and mapping is highly appealing. However, utilizing a thermal-infrared camera directly in existing vision-based methods is difficult because of the modality difference. This letter proposes a method to use sparse depth measurement for 6-DOF motion estimation via direct tracking under 14-bit raw measurement from the thermal camera. We also refine the local accuracy and include a loop closure to maintain global consistency. The experimental results demonstrate that the system is not only robust under various lighting conditions such as day and night, but it also overcomes the scale problem of monocular cameras.

Application of Hyperspectral Imaging to Underwater Habitat Mapping, Southern Adriatic Sea.

Hyperspectral imagers enable the collection of high-resolution spectral images exploitable for the supervised classification of habitats and objects of interest (OOI). Although this is a well-established technology for the study of subaerial environments, Ecotone AS has developed an underwater hyperspectral imager (UHI) system to explore the properties of the seafloor. The aim of the project is to evaluate the potential of this instrument for mapping and monitoring benthic habitats in shallow and deep-water environments. For the first time, we tested this system at two sites in the Southern Adriatic Sea (Mediterranean Sea): the cold-water coral (CWC) habitat in the Bari Canyon and the Coralligenous habitat off Brindisi. We created a spectral library for each site, considering the different substrates and the main OOI reaching, where possible, the lower taxonomic rank. We applied the spectral angle mapper (SAM) supervised classification to map the areal extent of the Coralligenous and to recognize the major CWC habitat-formers. Despite some technical problems, the first results demonstrate the suitability of the UHI camera for habitat mapping and seabed monitoring, through the achievement of quantifiable and repeatable classifications.

Morphological and Chronological Mapping of Manilius Crater Region Using Chandrayaan-1 Data Sets

Fine-resolution morphological mapping aided by ortho-images and digital elevation model from Chandrayaan-1 Terrain Mapping Camera and 3D GIS visualization has revealed scientifically diverse characteristics of lunar surface features, due to unique topographical significance of morphological features, i.e., highlands, basaltic plains and craters, which are very well manifested in 3D GIS environment. The distribution of various morphological features provides insights into the sequential evolution and surface process of the study area. The highland region represented by the Fra Mauro formation in the study area exhibits high albedo with distinct topography. The northern part of the study area falls in the southern part of major basin Serenitatis, and exhibits the dark mantling material with low albedo. The morphological features, i.e., wrinkle ridges and rilles, indicate volcanic flow events consequence to the loading of basaltic materials in the interior of the Serenitatis and Imbrium Basins and related extensional failure. The Manilius crater, which occupies the central part of the study area, is a complex crater with a central peak and asymmetric ejecta deposit. The ages of the major surficial features were determined based on size, frequency and distribution pattern of craters using crater size-frequency distribution model. Age of the Fra Mauro highland, Manilius crater, Mare Serenitatis and Mare Vaporum is, respectively, 3.9, 3.5, 2.8 and 1.7 Ga years, indicating that the lunar surface of this region evolved in Imbrian to Eratosthenian age of lunar selenological timescale.

A Method for Broccoli Seedling Recognition in Natural Environment Based on Binocular Stereo Vision and Gaussian Mixture Model.

Illumination in the natural environment is uncontrollable, and the field background is complex and changeable which all leads to the poor quality of broccoli seedling images. The colors of weeds and broccoli seedlings are close, especially under weedy conditions. The factors above have a large influence on the stability, velocity and accuracy of broccoli seedling recognition based on traditional 2D image processing technologies. The broccoli seedlings are higher than the soil background and weeds in height due to the growth advantage of transplanted crops. A method of broccoli seedling recognition in natural environments based on Binocular Stereo Vision and a Gaussian Mixture Model is proposed in this paper. Firstly, binocular images of broccoli seedlings were obtained by an integrated, portable and low-cost binocular camera. Then left and right images were rectified, and a disparity map of the rectified images was obtained by the Semi-Global Matching (SGM) algorithm. The original 3D dense point cloud was reconstructed using the disparity map and left camera internal parameters. To reduce the operation time, a non-uniform grid sample method was used for the sparse point cloud. After that, the Gaussian Mixture Model (GMM) cluster was exploited and the broccoli seedling points were recognized from the sparse point cloud. An outlier filtering algorithm based on k-nearest neighbors (KNN) was applied to remove the discrete points along with the recognized broccoli seedling points. Finally, an ideal point cloud of broccoli seedlings can be obtained, and the broccoli seedlings recognized. The experimental results show that the Semi-Global Matching (SGM) algorithm can meet the matching requirements of broccoli images in the natural environment, and the average operation time of SGM is 138 ms. The SGM algorithm is superior to the Sum of Absolute Differences (SAD) algorithm and Sum of Squared Differences (SSD) algorithms. The recognition results of Gaussian Mixture Model (GMM) outperforms K-means and Fuzzy c-means with the average running time of 51 ms. To process a pair of images with the resolution of 640×480, the total running time of the proposed method is 578 ms, and the correct recognition rate is 97.98% of 247 pairs of images. The average value of sensitivity is 85.91%. The average percentage of the theoretical envelope box volume to the measured envelope box volume is 95.66%. The method can provide a low-cost, real-time and high-accuracy solution for crop recognition in natural environment.

Structural design and simulation analysis of the carbon fiber-reinforced polymer main frame of a mapping camera system

The main frame is the primary load-bearing component of a mapping camera system. As the critical structure of the system, the main frame determines the mechanical properties of a camera system. The main frame design is hindered by its large size, complicated interface, as well as high-stability, high-frequency requirement, and lightweight requirements. During the launching process, the main frame should be able to withstand the mechanical action of the active section without affecting the performance of the camera system. This paper describes the design scheme of the main frame. The modal analysis, static analysis and frequency response analysis of the main frame are performed. The simulation analysis results prove that the natural frequency of the mapping camera system satisfies the design requirements and the main frame satisfies the required safety margin in the linear overload in X-, Y-, and Z-directions. Moreover, under condition of the frequency response in X-, Y-, and Z-directions, the main frame has excellent structural stiffness, which provides a proper mechanical environment for the camera system. The design scheme of the main frame is reasonable and feasible.

A new method for detecting individual trees in aerial LiDAR point clouds using absolute height maxima

Data acquired from aerial laser scanner systems are increasingly used for detecting individual trees in operational inventories. In conventional analyses, tree detection is often performed on raster models that use local height maxima filters; an option that is likely to accumulate important errors. In order to reduce errors and improve the detection of individual trees, a new method is proposed that uses an Absolute Height Maxima (AHM) filter applied on the original point clouds obtained from Aerial Laser Scanning (ALS). ALS point clouds at a density of 2 to 4 points per square meter were acquired over forest stands in Hyrcanian forests. In the new method, false trees and commission errors were automatically found and excluded. To evaluate the efficiency of this new method, 121 sample trees in the field were located, with a DGPS and a mapping camera. The height and crown radius of the sample trees were also measured. The field-surveyed variables were compared to the closest detected tree, with an overall detection accuracy of 75.2%. The initial results of this analysis allowed us to hypothesize that a higher detection of tree may be expected with larger densities.

Radiometric homogenisation of aerial images by calibrating with satellite data

ABSTRACTThe use of very high resolution (VHR) aerial imagery for quantitative remote sensing has been limited by unwanted radiometric variation over temporal and spatial extents. In this paper we propose a simple yet effective technique for the radiometric homogenisation of the digital numbers of aerial images. The technique requires a collocated and concurrent, well-calibrated satellite image as surface reflectance reference to which the aerial images are calibrated. The bands of the reference satellite sensor should be spectrally similar to those of the aerial sensor. Using radiative transfer theory, we show that a spatially varying local linear model can be used to approximate the relationship between the surface reflectance of the reference image and the digital numbers of the aerial images. The model parameters for each satellite pixel location are estimated using least squares regression inside a small sliding window. The technique was applied to a set of aerial images captured over multiple days with an Intergraph Digital Mapping Camera (DMC) system. A near-concurrent Moderate Resolution Imaging Spectroradiometer (MODIS) nadir bidirectional reflectance distribution function (BRDF) adjusted reflectance image was used as the reflectance reference dataset. The resulting DMC mosaic was compared to a near-concurrent Satellite Pour l’Observation de la Terre (SPOT) 5 reflectance image of a portion of the same area, omitting the blue channel from the DMC mosaic due to its absence in the SPOT 5 data. The mean absolute reflectance difference was found to be 3.43% and the mean coefficient of determination (R2) over the bands was 0.84. The technique allows the production of seamless mosaics corrected for coarse scale atmospheric and BRDF effects and does not require the manual acquisition (or provision) of ground reflectance references. The accuracy of corrections is limited by the resolution of the reference image, which is generally significantly coarser than VHR imagery. The method cannot correct for small scale BRDF or other variations not captured at the reference resolution. Nevertheless, results show a significant improvement in homogeneity and correlation with SPOT 5 reflectance.

Development and implementation of novel sensor fusion algorithm for occupancy detection and automation in energy efficient buildings

This paper deals with achieving both energy efficiency and comfortness in a particular room by controlling lighting and cooling appliances of a building by deploying modern sensors like Thermal sensors and Camera for accurate mapping of the environment parameters. The unique part of this paper is that a Sensor Fusion algorithm has been used in different segments of the room which combine the sensory output from the Thermal Sensor and Camera to ensure that the occupancy detection is done accurately with least amount of fluctuations without any delay. The two algorithms, namely, image processing algorithm and sensor signal processing runs in distributed computing environment in two different processors and the outputs of processing algorithms are received in the master controller through IoT cloud platform. The proposed image processing algorithm is effective in detecting both static and dynamic event changes using background subtraction and ROI based segregation models. The system develops the use of this sensor fusion algorithm to provide an intuitive control of the environment and improve the efficiency of the decision making process using National Instruments MyRIO 1800 and cRIO 9082 processors in LabVIEW platform which also reduces the chances or consequences of any fault occurring in the system.

REVISITING THE APOLLO PHOTOGRAMMETRIC MAPPING SYSTEM

Abstract. The integrated photogrammetric mapping system flown on the last three Apollo lunar missions (AS15, AS16, and AS17) in 1971 and 1972 incorporated a Metric (mapping) Camera, a high-resolution Panoramic Camera, and a star camera and laser altimeter. The U.S. Geological Survey’s Astrogeology Science Center, the Intelligent Robotics Group of the NASA Ames Research Center, and Arizona State University are working together in an ongoing collaboration to achieve the most complete cartographic development of Apollo mapping system data into versatile digital map products. These will enable a variety of scientific/engineering uses of the data including mission planning, geologic mapping, geophysical process modelling, slope dependent correction of spectral data, and change detection. After a brief discussion of the origins of the mapping system, we describe the Metric and Panoramic cameras, processing of the associated image and support data, work to photogrammetrically control the Metric Camera images, and future plans.

Multi-Camera Imaging System for UAV Photogrammetry.

In the last few years, it has been possible to observe a considerable increase in the use of unmanned aerial vehicles (UAV) equipped with compact digital cameras for environment mapping. The next stage in the development of photogrammetry from low altitudes was the development of the imagery data from UAV oblique images. Imagery data was obtained from side-facing directions. As in professional photogrammetric systems, it is possible to record footprints of tree crowns and other forms of the natural environment. The use of a multi-camera system will significantly reduce one of the main UAV photogrammetry limitations (especially in the case of multirotor UAV) which is a reduction of the ground coverage area, while increasing the number of images, increasing the number of flight lines, and reducing the surface imaged during one flight. The approach proposed in this paper is based on using several head cameras to enhance the imaging geometry during one flight of UAV for mapping. As part of the research work, a multi-camera system consisting of several cameras was designed to increase the total Field of View (FOV). Thanks to this, it will be possible to increase the ground coverage area and to acquire image data effectively. The acquired images will be mosaicked in order to limit the total number of images for the mapped area. As part of the research, a set of cameras was calibrated to determine the interior orientation parameters (IOPs). Next, the method of image alignment using the feature image matching algorithms was presented. In the proposed approach, the images are combined in such a way that the final image has a joint centre of projections of component images. The experimental results showed that the proposed solution was reliable and accurate for the mapping purpose. The paper also presents the effectiveness of existing transformation models for images with a large coverage subjected to initial geometric correction due to the influence of distortion.

Calibration Method for Mapping Camera Based on a Precise Grouped Approach Method

This paper introduces a new calibration method for the mapping camera called Precise Grouped Approach Method (PGAM). The conventional calibration method for the mapping camera is the exact measuring angle method. The accuracy of this method can be reduced by theoretical uncertainties and the number and distribution of observation points. PGAM is able to overcome these disadvantages and improve the accuracy. Firstly, we reduce the theoretical uncertainties by means of a grouped approach method, which rectifies the high-precision rotation stage to zero position. Secondly, a weighted theory is applied to eliminate the effect of the number and distribution of observation points. Finally, the accuracy of PGAM is analyzed. The experiment result shows that the calibration accuracy is significantly improved when using the proposed PGAM algorithm, compared to the conventional one under the identical experimental condition.

Impact analysis of lens shutter of aerial camera on image plane illuminance

In order to obtain high resolution aerial images, in addition to adopt high transfer function of optical system and high quality imaging medium, the exposure time of the shutter should be properly controlled, so as to make sure the appropriate exposure of the CCD sensor. The paper applies an aerial mapping camera shutter the structure and working principle of the mirror, presents the mirror shutter algorithm of the working period of luminous flux, on the basis of radiation theory. The paper also analyze the transfer relations of the object and image of the light energy on the optical system with large field of view, short focal length and large relative aperture. The experimental results show that the illumination precision of image plane improved by 8.9%, compared with the traditional algorithm and the gain exposure of CCD sensor is closer to the actual light energy. In addition, these design can also provide theoretical reference for image uniformity correction, CCD gain adjustment and variable density neutral filter processing.

Integration of Low-Cost GNSS and Monocular Cameras for Simultaneous Localization and Mapping.

Low-cost Global Navigation Satellite System (GNSS) receivers and monocular cameras are widely used in daily activities. The complementary nature of these two devices is ideal for outdoor navigation. In this paper, we investigate the integration of GNSS and monocular camera measurements in a simultaneous localization and mapping system. The proposed system first aligns the coordinates between two sensors. Subsequently, the measurements are fused by an optimization-based scheme. Our system can function in real-time and obtain the absolute position, scale, and attitude of the vehicle. It achieves a high accuracy without a preset map and also has the capability to work with a preset map. The system can easily be extended to create other forms of maps or for other types of cameras. Experimental results on a popular public dataset are presented to validate the performance of the proposed system.

Compositional and chronological characterization of mare crisium using Chandrayaan-1 and LROC-WAC data

The surface of the Moon has been observed by human being since time immemorial and it appears distinctly different from that of the earth. Moon is geologically inactive for quite long time and therefore it's an ideal place to study the early solar system history. The lunar surface is dominated by various morphological features such as craters, rilles, impact melts, swirls, faults, lineaments and wrinkle ridges. The mare crisium (sea of crisis) is a multi-ring mare basin in the eastern nearside of the moon. Compositional analysis and crater counting based chronological studies conducted in the present work by using Chandrayaan-1 Moon Mineralogical Mapper data (M3), Terrain Mapping Camera (TMC) and Lunar Reconnaissance Orbiter Camera - Wide Angle Camera (LROC -WAC) data. The M3 data has been used for FeO and TiO2 mapping and to measure 1 μm, 2 μm integrated band depth to detect the olivine and pyroxene minerals. Ch-1 TMC and LROC WAC data were used for the chronological characterization through Crater Size Frequency Distribution (CSFD). The CSFD analysis by using TMC data have provided the ages for the southwest, southcentral and southeast regions of the crisium basin as 3.4Ga, 3.6Ga, and 3.45Ga respectively. The spectral characterizations of the selected Crisium features were done to map the various lithological features such as olivine, pyroxene, plagioclase, ilmenite and olivine – pyroxene mixtures which are present in the basalt, gabbroic and anorthositic gabbro rocks from the mare crisium.