Browsing by Author "Feng, Ziyi"
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- Calibration and Signal Processing of Airborne Stand Profile Radar used in forest inventory
Sähkötekniikan korkeakoulu | Master's thesis(2016-08-24) Feng, ZiyiTomoRadar is a helicopter/UAV (Unmanned Aerial Vehicle) based FM-CW microwave ranging radar designed for the forest inventory. Developed by FGI (Finnish Geospatial Research Institute), TomoRadar can scan forest with Ku-band signals and then receive the backscatter signal from targets. The target distance can be figured out from the backscattered signal. Furthermore, various information around forest can be evaluated. As a ranging scatterometer the system calibration and signal processing are necessary and critical tasks. In This thesis, these two major problems are researched and solved. The system calibration is limited to complete on ground and restricted in a particular range despite TomoRadar works on helicopter. Thus the range calibration experiment is conducted on the ground test field with a Luneburg lens. Besides, power calibration of system backscatter signal is researched through electrical component tests. The linearity of the radar system frequency sweep is critical in FM-CW radars. Hence, it is studied in this thesis. When TomoRadar works, the output of the whole system is analog signal in intimidate frequency (IF) band. A digitizer samples and records the output analog signal. With these signal, the information around forest cannot be obtained. To draw stand profile of forest and research forest information, the IF band signals need to be further processed. This is signal processing part of the thesis work. As a result of this thesis work, TomoRadar range calibration was achieved. The calibration results were verified by the helicopter based flying tests. The results present that the calibration completed on ground test field applied with the situation that TomoRadar works in air and scans targets in long distance. The linearity of system frequency sweep generated by DDS (Direct Digital Synthesizer) is verified. Finally, the test forest backscattered data was processed and the ranges were evaluated. Some targets stand profiles are generated and presented in this thesis - A Clustering Framework for Monitoring Circadian Rhythm in Structural Dynamics in Plants From Terrestrial Laser Scanning Time Series
A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä(2019-04-17) Puttonen, Eetu; Lehtomäki, Matti; Litkey, Paula; Näsi, Roope; Feng, Ziyi; Liang, Xinlian; Wittke, Samantha; Pandzic, Milos; Hakala, Teemu; Karjalainen, Mika; Pfeifer, NorbertTerrestrial Laser Scanning (TLS) can be used to monitor plant dynamics with a frequency of several times per hour and with sub-centimeter accuracy, regardless of external lighting conditions. TLS point cloud time series measured at short intervals produce large quantities of data requiring fast processing techniques. These must be robust to the noise inherent in point clouds. This study presents a general framework for monitoring circadian rhythm in plant movements from TLS time series. Framework performance was evaluated using TLS time series collected from two Norway maples (Acer platanoides) and a control target, a lamppost. The results showed that the processing framework presented can capture a plant's circadian rhythm in crown and branches down to a spatial resolution of 1 cm. The largest movements in both Norway maples were observed before sunrise and at their crowns' outer edges. The individual cluster movements were up to 0.17 m (99th percentile) for the taller Norway maple and up to 0.11 m (99th percentile) for the smaller tree from their initial positions before sunset. - Feasibility of mobile laser scanning towards operational accurate road rut depth measurements
A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä(2021-02-02) El Issaoui, Aimad; Feng, Ziyi; Lehtomäki, Matti; Hyyppä, Eric; Hyyppä, Hannu; Kaartinen, Harri; Kukko, Antero; Hyyppä, JuhaThis paper studied the applicability of the Roamer-R4DW mobile laser scanning (MLS) system for road rut depth measurement. The MLS system was developed by the Finnish Geospatial Research Institute (FGI), and consists of two mobile laser scanners and a Global Navigation Satellite System (GNSS)-inertial measurement unit (IMU) positioning system. In the study, a fully automatic algorithm was developed to calculate and analyze the rut depths, and verified in 64 reference pavement plots (1.0 m × 3.5 m). We showed that terrestrial laser scanning (TLS) data is an adequate reference for MLS-based rutting studies. The MLS-derived rut depths based on 64 plots resulted in 1.4 mm random error, which can be considered adequate precision for operational rutting depth measurements. Such data, also covering the area outside the pavement, would be ideal for multiple road environment applications since the same data can also be used in applications, from highdefinition maps to autonomous car navigation and digitalization of street environments over time and in space. - Is field-measured tree height as reliable as believed – A comparison study of tree height estimates from field measurement, airborne laser scanning and terrestrial laser scanning in a boreal forest
A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä(2019-01-01) Wang, Yunsheng; Lehtomäki, Matti; Liang, Xinlian; Pyörälä, Jiri; Kukko, Antero; Jaakkola, Anttoni; Liu, Jingbin; Feng, Ziyi; Chen, Ruizhi; Hyyppä, JuhaQuantitative comparisons of tree height observations from different sources are scarce due to the difficulties in effective sampling. In this study, the reliability and robustness of tree height observations obtained via a conventional field inventory, airborne laser scanning (ALS) and terrestrial laser scanning (TLS) were investigated. A carefully designed non-destructive experiment was conducted that included 1174 individual trees in 18 sample plots (32 m × 32 m) in a Scandinavian boreal forest. The point density of the ALS data was approximately 450 points/m2. The TLS data were acquired with multi-scans from the center and the four quadrant directions of the sample plots. Both the ALS and TLS data represented the cutting edge point cloud products. Tree heights were manually measured from the ALS and TLS point clouds with the aid of existing tree maps. Therefore, the evaluation results revealed the capacities of the applied laser scanning (LS) data while excluding the influence of data processing approach such as the individual tree detection. The reliability and robustness of different tree height sources were evaluated through a cross-comparison of the ALS-, TLS-, and field- based tree heights. Compared to ALS and TLS, field measurements were more sensitive to stand complexity, crown classes, and species. Overall, field measurements tend to overestimate height of tall trees, especially tall trees in codominant crown class. In dense stands, high uncertainties also exist in the field measured heights for small trees in intermediate and suppressed crown class. The ALS-based tree height estimates were robust across all stand conditions. The taller the tree, the more reliable was the ALS-based tree height. The highest uncertainty in ALS-based tree heights came from trees in intermediate crown class, due to the difficulty of identifying treetops. When using TLS, reliable tree heights can be expected for trees lower than 15–20 m in height, depending on the complexity of forest stands. The advantage of LS systems was the robustness of the geometric accuracy of the data. The greatest challenges of the LS techniques in measuring individual tree heights lie in the occlusion effects, which lead to omissions of trees in intermediate and suppressed crown classes in ALS data and incomplete crowns of tall trees in TLS data. - Pavement distress detection using terrestrial laser scanning point clouds – Accuracy evaluation and algorithm comparison
A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä(2022) Feng, Ziyi; el Issaoui, Aimad; Lehtomäki, Matti; Ingman, Matias; Kaartinen, Harri; Kukko, Antero; Savela, Joona; Hyyppä, Hannu; Hyyppä, JuhaIn this paper, we compared five crack detection algorithms using terrestrial laser scanner (TLS) point clouds. The methods are developed based on common point cloud processing knowledge in along- and across-track profiles, surface fitting or local pointwise features, with or without machine learning. The crack area and volume were calculated from the crack points detected by the algorithms. The completeness, correctness, and F1 score of each algorithm were computed against manually collected references. Ten 1-m-by-3.5-m plots containing 75 distresses of six distress types (depression, disintegration, pothole, longitudinal, transverse, and alligator cracks) were selected to explain variability of distresses from a 3-km-long-road. For crack detection at plot level, the best algorithm achieved a completeness of up to 0.844, a correctness of up to 0.853, and an F1 score of up to 0.849. The best algorithm’s overall (ten plots combined) completeness, correctness, and F1 score were 0.642, 0.735, and 0.685 respectively. For the crack area estimation, the overall mean absolute percentage errors (MAPE) of the two best algorithms were 19.8% and 20.3%. In the crack volume estimation, the two best algorithms resulted in 19.3% and 14.5% MAPE. When the plots were grouped based on crack detection complexity, in the ‘easy’ category, the best algorithm reached a crack area estimation MAPE of 8.9%, while for crack volume estimation, the MAPE obtained from the best algorithm was 0.7%. - A practical method utilizing multi-spectral LiDAR to aid points cloud matching in SLAM
A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä(2020) Jiang, Changhui; Chen, Yuwei; Tian, Wenxin; Feng, Ziyi; Li, Wei; Zhou, Chunchen; Shao, Hui; Puttonen, Eetu; Hyyppa, JuhaLight Detection and Ranging (LiDAR) sensors are popular in Simultaneous Localization and Mapping (SLAM) owing to their capability of obtaining ranging information actively. Researchers have attempted to use the intensity information that accompanies each range measurement to enhance LiDAR SLAM positioning accuracy. However, before employing LiDAR intensities in SLAM, a calibration operation is usually carried out so that the intensity is independent of the incident angle and range. The range is determined from the laser beam transmitting time. Therefore, the key to using LiDAR intensities in SLAM is to obtain the incident angle between the laser beam and target surface. In a complex environment, it is difficult to obtain the incident angle robustly. This procedure also complicates the data processing in SLAM and as a result, further application of the LiDAR intensity in SLAM is hampered. Motivated by this problem, in the present study, we propose a Hyperspectral LiDAR (HSL)-based-intensity calibration-free method to aid point cloud matching in SLAM. HSL employed in this study can obtain an eight-channel range accompanied by corresponding intensity measurements. Owing to the design of the laser, the eight-channel range and intensity were collected with the same incident angle and range. According to the laser beam radiation model, the ratio values between two randomly selected channels' intensities at an identical target are independent of the range information and incident angle. To test the proposed method, the HSL was employed to scan a wall with different coloured papers pasted on it (white, red, yellow, pink, and green) at four distinct positions along a corridor (with an interval of 60 cm in between two consecutive positions). Then, a ratio value vector was constructed for each scan. The ratio value vectors between consecutive laser scans were employed to match the point cloud. A classic Iterative Closest Point (ICP) algorithm was employed to estimate the HSL motion using the range information from the matched point clouds. According to the test results, we found that pink and green papers were distinctive at 650, 690, and 720 nm. A ratio value vector was constructed using 650-nm spectral information against the reference channel. Furthermore, compared with the classic ICP using range information only, the proposed method that matched ratio value vectors presented an improved performance in heading angle estimation. For the best case in the field test, the proposed method enhanced the heading angle estimation by 72%, and showed an average 25.5% improvement in a featureless spatial testing environment. The results of the primary test indicated that the proposed method has the potential to aid point cloud matching in typical SLAM of real scenarios. - UAV-borne profiling radar for forest research
A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä(2017) Chen, Yuwei; Hakala, Teemu; Karjalainen, Mika; Feng, Ziyi; Tang, Jian; Litkey, Paula; Kukko, Antero; Jaakkola, Anttoni; Hyyppä, JuhaMicrowave Radar is an attractive solution for forest mapping and inventories because microwave signals penetrates into the forest canopy and the backscattering signal can provide information regarding the whole forest structure. Satellite-borne and airborne imaging radars have been used in forest resources mapping for many decades. However, their accuracy with respect to the main forest inventory attributes substantially varies depending on the wavelength and techniques used in the estimation. Systems providing canopy backscatter as a function of canopy height are, practically speaking, missing. Therefore, there is a need for a radar system that would enable the scientific community to better understand the radar backscatter response from the forest canopy. Consequently, we undertook a research study to develop an unmanned aerial vehicle (UAV)-borne profiling (i.e., waveform) radar that could be used to improve the understanding of the radar backscatter response for forestry mapping and inventories. A frequency modulation continuous waveform (FMCW) profiling radar, termed FGI-Tomoradar, was introduced, designed and tested. One goal is the total weight of the whole system is less than 7 kg, including the radar system and georeferencing system, with centimetre-level positioning accuracy. Achieving this weight goal would enable the FGI-Tomoradar system to be installed on the Mini-UAV platform. The prototype system had all four linear polarization measuring capabilities, with bistatic configuration in Ku-band. In system performance tests in this study, FGI-Tomoradar was mounted on a manned helicopter together with a Riegl VQ-480-U laser scanner and tested in several flight campaigns performed at the Evo site, Finland. Airborne laser scanning data was simultaneously collected to investigate the differences and similarities of the outputs for the same target area for better understanding the penetration of the microwave signal into the forest canopy. Preliminary analysis confirmed that the profiling radar measures a clear signal from the canopy structure and has substantial potential to improve our understanding of radar forest mapping using the UAV platform.