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For mobile robots, the high-precision integrated calibration and structural robustness of multi-sensor systems are important prerequisites for ensuring healthy operations in the later stage. Currently, there is no well-established validation method for the calibration accuracy and structural robustness of multi-sensor systems, especially for dynamic traveling situations. This paper presents a novel validation method for the calibration accuracy and structural robustness of a multi-sensor mobile robot. The method employs a ground–object–air cooperation mechanism, termed the "ground surface simulation field (GSSF)—mobile robot -photoelectric transmitter station (PTS)". Firstly, a static high-precision GSSF is established with the true north datum as a unified reference. Secondly, a rotatable synchronous tracking system (PTS) is assembled to conduct real-time pose measurements for a mobile vehicle. The relationship between each sensor and the vehicle body is utilized to measure the dynamic pose of each sensor. Finally, the calibration accuracy and structural robustness of the sensors are dynamically evaluated. In this context, epipolar line alignment is employed to assess the accuracy of the evaluation of relative orientation calibration of binocular cameras. Point cloud projection and superposition are utilized to realize the evaluation of absolute calibration accuracy and structural robustness of individual sensors, including the navigation camera (Navcam), hazard avoidance camera (Hazcam), multispectral camera, time-of-flight depth camera (TOF), and light detection and ranging (LiDAR), with respect to the vehicle body. The experimental results demonstrate that the proposed method offers a reliable means of dynamic validation for the testing phase of a mobile robot. [ABSTRACT FROM AUTHOR]

With the increasing availability of satellite monitoring data, the demand for storage and computational resources for updating the results of monitoring the surface subsidence in a mining area continues to rise. Sequential adjustment (SA) models are considered effective for rapidly updating time series interferometry synthetic aperture radar (TS-InSAR) measurements. However, the accuracy of surface subsidence values estimated through traditional sequential adjustment is highly sensitive to abnormal observations or prior information on anomalies. Moreover, the surface subsidence associated with mining exhibits nonlinear and large gradient characteristics, making general InSAR methods challenging for obtaining reliable monitoring results. In this study, we employ the phase unwrapping network (PUNet) to obtain unwrapped values of differential interferograms. To mitigate the impact of abnormal errors in the near real-time small baseline subset InSAR (SBAS-InSAR) sequential updating process in mining areas, a robust sequential adjustment method based on M-estimation is proposed to estimate the temporal deformation parameters by using the equivalent weight model. Using a coal backfilling mining face in Shanxi, China, as the study area and the Sentinel-1 SAR dataset, we comprehensively evaluate the performance of unwrapping methods and subsidence time series estimation techniques and evaluate the effect of filling mining on surface subsidence control. The results are validated using leveling measurements within the study area. The relative error of the proposed method is less than 5%, which can meet the requirements of monitoring the surface subsidence in mining areas. The method proposed in this study not only enhances computational efficiency but also addresses the issue of underestimation encountered by InSAR methods in mining area applications. Furthermore, it also mitigates unwrapping phase anomalies on the monitoring results. [ABSTRACT FROM AUTHOR]

A robust and effective method for the identification of point-distributed coded targets (IPCT) in a video-simultaneous triangulation and resection system (V-STARS) was reported recently. However, its limitations were the setting of critical parameters, it being non-adaptive, making misidentifications in certain conditions, having low positioning precision, and its identification effect being slightly inferior to that of the V-STARS. Aiming to address these shortcomings of IPCT, an improved IPCT, named I-IPCT, with an adaptive binarization, a more precise ellipse-center localization, and especially an invariance of the point–line distance ratio (PLDR), was proposed. In the process of edge extraction, the adaptive threshold Gaussian function was adopted to realize the acquisition of an adaptive binarization threshold. For the process of center positioning of round targets, the gray cubic weighted centroid algorithm was adopted to realize high-precision center localization. In the template point recognition procedure, the invariant of the PLDR was used to realize the determination of template points adaptively. In the decoding procedure, the invariant of the PLDR was adopted to eliminate confusion. Experiments in indoor, outdoor, and unmanned aerial vehicle (UAV) settings were carried out; meanwhile, sufficient comparisons with IPCT and V-STARS were performed. The results show that the improvements can make the identification approximately parameter-free and more accurate. Meanwhile, it presented a high three-dimensional measurement precision in close-range photogrammetry. The improved IPCT performed equally well as the commercial software V-STARS on the whole and was slightly superior to it in the UAV test, in which it provided a fantastic open solution using these kinds of coded targets and making it convenient for researchers to freely apply the coded targets in many aspects, including UAV photogrammetry for high-precision automatic image matching and three-dimensional real-scene reconstruction. [ABSTRACT FROM AUTHOR]

The Sichuan–Tibet Railway (STR) is currently under construction and serves as an important transportation route in western China. Identifying potential geohazards along the route is important for project construction. However, research on the frozen soil of the Western Sichuan Plateau, and on frozen soil identification using interferometric synthetic aperture radar (InSAR) is relatively negligible. As a low-cost, all-weather spatial geodesy tool, InSAR is frequently used for geohazard identification. We selected a study area located along the Litang section of the STR, starting from Litang County in the east and extending 60 km to the west. The geological conditions along the line are complex, with numerous fault zones and hidden danger points for landslide. To identify unstable slopes along the line, distribute scatterer InSAR (DS-InSAR) was used to obtain surface displacement information from 2018 to 2021. Based on the displacement information obtained from the ascending and descending orbit images from Sentinel-1, a spatial density clustering method identified 377 and 388 unstable slopes in the study area, respectively, of these, 132 were consistent. The identified unstable slopes were mostly located in areas with a relatively high altitude and moderate slope. The Luanshibao landslide, which is a typical landslide in the study area, had notable signs of displacement, where the displacement rate along the back edge of the landslide can reach 20 mm/a. An inversion method for the seasonal frozen soil area distribution was proposed based on the periodic subsidence and uplift model and time-series monitoring data; the calculated seasonal freeze–thaw amplitude exceeded 20 mm. Further analysis revealed a 2-month lag in the response of the freeze–thaw phenomenon to the air temperature. This study demonstrated that DS-InSAR offers optimal surface displacement data, which can provide an important basis to identify engineering geological hazards. [ABSTRACT FROM AUTHOR]

GaoFen 7 (GF-7) is China's first submeter high-resolution stereo mapping satellite with dual-linear-array cameras and a laser altimeter system onboard for high-precision mapping. To further take advantage of the very high elevation accuracy of laser altimetry data and the high relative accuracy with stereo images, an innovative combined adjustment method for GF-7 stereo images with laser altimetry data is presented in this paper. In this method, two flexible and effective schemes were proposed to extract the elevation control point according to the registration of footprint images and stereo images and then utilized as vertical control in the block adjustment to improve the elevation accuracy without ground control points (GCPs). The validation experiments were conducted in Shandong, China, with different terrains. The results demonstrated that, after using the laser altimetry data, the root mean square error (RMSE) of elevation was dramatically improved from the original 2.15 m to 0.75 m, while the maximum elevation error was less than 1.6 m. Moreover, by integrating a few horizontal control points, the planar and elevation accuracy can be simultaneously improved. The results show that the method will be useful for reducing the need for field survey work and improving mapping efficiency. [ABSTRACT FROM AUTHOR]

The water use efficiency (WUE) is an essential indicator of carbon–water coupling between terrestrial ecosystems and the atmosphere, and it is an important parameter for studying ecosystem responses to global climate change. A comprehensive understanding of the water–carbon coupling process in the Loess Plateau can reflect the balance between the "carbon absorption" and "water consumption" in vegetation, which drives the ecosystem succession process. In recent years, scholars have gained a more comprehensive understanding of the WUE and the driving factors of the Loess Plateau. However, there is still a need to study the carbon and water coupling mechanisms of different land use types in the Loess Plateau region. In this article, based on the gross primary productivity (GPP), evapotranspiration (ET), surface cover remote sensing products, and meteorological observation data, the trend of WUE changes for different vegetation types in the Loess Plateau from 2001 to 2020 and the correlations with the Normalized Difference Vegetation Index (NDVI), precipitation, and temperature values were analyzed using the Theil–Sen median (SEN) trend analysis method and correlation coefficient analysis method. The spatial distribution patterns of the changes with the drought index showed that the multi-year average WUE value of the Loess Plateau was 1.24 g C mm−1 H2O, and the mean WUE values in different seasons were ranked as follows: summer > autumn > spring. The WUE growth rates of all vegetation types showed a decreasing trend with the increase in drought index, and the size of the WUE response rate for each vegetation type to drought was ranked as follows: grassland > forest > shrub > crop. The annual average WUE increase rate of the Loess Plateau was 0.02 g C mm−1 H2O yr−1, of which 93.36% of the area showed an increasing trend. The NDVI was the dominant factor affecting the spatial and temporal variations in WUE rates in the Loess Plateau, and the correlation between the NDVI and WUE was strongest in summer. In the more arid regional ecosystems, the WUE was negatively correlated with the precipitation and temperature, but in summer the precipitation had a positive effect on the WUE. The correlation of grassland and shrub WUE rates with temperature was more sensitive to the drought index than that of the forest and crop areas, but there was also a threshold effect. Therefore, when vegetation restoration is carried out in arid and semi-arid regions, the carbon and water coupling mechanisms of different vegetation types and the reasonable allocation of regional water resources should be fully considered. [ABSTRACT FROM AUTHOR]

In close-range or unmanned aerial vehicle (UAV) photogrammetry, Schneider concentric circular coded targets (SCTs), which are public, are widely used for image matching and as ground control points. GSI point-distributed coded targets (GCTs), which are only mainly applied in a video-simultaneous triangulation and resection system (V-STARS), are non-public and rarely applied in UAV photogrammetry. In this paper, we present our innovative detailed solution to identify GCTs. First, we analyze the structure of a GCT. Then, a special 2D P2-invariant of five coplanar points derived from cross ratios is adopted in template point registration and identification. Finally, the affine transformation is used for decoding. Experiments indoors—including different viewing angles ranging from 0° to 80° based on 6 mm-diameter GCTs, smaller 3 mm-diameter GCTs, and different sizes mixed—and outdoors with challenging scenes were carried out. Compared with V-STARS, the results show that the proposed method can preserve the robustness and achieves a high accuracy rate in identification when the viewing angle is not larger than 65° through indoor experiments, and the proposed method can achieve approximate or slightly weaker effectiveness than V-STARS on the whole. Finally, we attempted to extend and apply the designed GCTs in UAV photogrammetry for a preliminary experiment. This paper demonstrates that GCTs can be designed, printed, and identified easily through our method. It is expected that the proposed method may be helpful when applied to image matching, camera calibration, camera orientation, or 3D measurements or serving as control points in UAV photogrammetry for scenarios with complex structures in the future. [ABSTRACT FROM AUTHOR]

The Grain for Green Project (GGP) has considerably improved the vegetation cover of the Loess Plateau, as well as changed the carbon and water coupling process of local vegetation to a certain extent. Water use efficiency (WUE) is a crucial measure for evaluating ecosystem responses to global climate change and is a key indicator of the carbon–water coupling between terrestrial ecosystems and the environment. A comprehensive understanding of the impact of vegetation reconstruction on WUE on the Loess Plateau is of great significance to the vegetation growth and contribution to sustainable of the Loess Plateau. In recent years, scholars have gained a more comprehensive understanding of the distribution and drivers of WUE on the Loess Plateau. However, through the study of carbon and water coupling in the Loess Plateau, it is found that the effects of different vegetation restoration levels on WUE are still to be studied in depth in terms of spatial and temporal heterogeneity and long timeseries. In this paper, we analyzed the trends of Normalized Difference vegetation cover (NDVI) and WUE from 2001 to 2010 and 2011 to 2020, respectively, to research at the WUE of the vegetation in this area in relation to vegetation restoration. It was found that the Loess Plateau's vegetation WUE rose from 2001 to 2020 at a rate of 0.023 g C kg−1 H2O per year, and that the increase from 2011 to 2020 was more significant than the growth from 2000 to 2010. The Loess Plateau's area with a growing trend in vegetation water use rate increased from 77.12% in 2001–2010 to 88.63% in 2011–2020, with the majority of the increased area occurring in the northeastern Inner Mongolia region. After 20 years of the reforestation project, the area where NDVI and WUE increased simultaneously accounted for 71.54% of the Loess Plateau, the area where NDVI increased but WUE decreased accounted for 10.95% of the Loess Plateau, and the area where NDVI increased but WUE decreased accounted for 7.15% of the Loess Plateau. The correlation between temperature precipitation and WUE was not significant for the whole Loess Plateau, further indicating that the increase in vegetation cover was the main reason for the increase in vegetation water efficiency. Therefore, the effect of vegetation cover on WUE should be fully considered when vegetation restoration is carried out on the Loess Plateau. [ABSTRACT FROM AUTHOR]

The exploitation of underground coal resources has stepped up local economic and social development significantly. However, it was inevitable that time-dependent surface settlement would occur above the mined-out voids. Subsidence associated with local geo-mining can last from several months to scores of years and can seriously impact infrastructure, city planning, and underground space utilization. This paper addresses the problems in predicting progressive residual surface subsidence. The subsidence process was divided into three phases: a duration period, a residual subsidence period, and a long-term subsidence period. Then, a novel mathematical model calculating surface progressive residual subsidence was proposed based on the logistic time function. After the duration period, the residual subsidence period was extrapolated according to the threshold of the surface sinking rate. The validation for the proposed model was estimated in light of observed in situ data. The results demonstrate that the logistic time function is an ideal time function reflecting surface subsidence features from downward movement, subsidence rate, and sinking acceleration. The surface residual subsidence coefficient, which plays a crucial role in calculating surface settling, varies directly with model parameters and inversely with time. The influence of the amount of in situ data on predicted values is pronounced. Observation time for surface subsidence must extend beyond the active period. Thus back-calculated parameters with in situ measurement data can be reliable. Conversely, the deviation between predictive values and field-based ones is significant. The conclusions in this study can guide the project design of surface subsidence measurement resulting from longwall coal operation. The study affords insights valuable to land reutilization, city planning, and stabilization estimation of foundation above an abandoned workface. [ABSTRACT FROM AUTHOR]

Considering the shortcomings of the currently used time functions for dynamically predicting surface mining subsidence and calculating its parameters, a novel time function is proposed on the basis of an in-depth analysis on the movement characteristics of mining surface points in a fully mined area and the measured mining subsidence data in the field during the course of the mining process. The proposed function can be used to effectively characterize the surface subsidence, the subsidence velocity, and the acceleration of the mining area. All the parameters involved in the function have their physical meaning, and their influence on the function was also analyzed in this study. A parameter calculation method is proposed for the new time function based on the normalization method and least square principle. Taking the measured dynamic subsidence data of 22,618 working faces in a coal mine as an example, the reliability of the new time function model was verified by comparing the measured data with the predicted results. The results show that the average relative root-mean-square error was 5.2%, and the prediction accuracy was improved compared with the Knothe time function, double-parameter Knothe time function, and piecewise optimized Knothe time function. [ABSTRACT FROM AUTHOR]

In the indoor and outdoor transition area, due to its poor availability in a complex positioning environment, the BDS/GPS SPP (single-point positioning by combining BeiDou Navigation Satellite System (BDS) and Global Positioning System (GPS)) is unable to provide an effective positioning service. In view of the poor positioning accuracy and low sampling rate of the BDS/GPS SPP and the gross error, such as the non-line-of-sight error of UWB (Ultra-Wide-Band), making the accuracy of positioning results poor, a BDS/GPS/UWB tightly coupled navigation model considering pedestrian motion characteristics is proposed to make positioning results more reliable and accurate in the transition area. The core content of this paper is divided into the following three parts: (1) Firstly, the dynamic model and positioning theories of BDS/GPS SPP and UWB are introduced, respectively. (2) Secondly, the BDS/GPS/UWB tightly coupled navigation model is proposed. An environment discrimination factor is introduced to adaptively adjust the variance factor of the system state. At the same time, the gross error detection factor is constructed by using the a posteriori residuals to make the variance factor of the measurement information of the combined positioning system able to be adjusted intelligently for the purpose of eliminating the interference of gross error observations on positioning results. On the other hand, pedestrian motion characteristics are introduced to establish the constraint equation to improve the consistency of positioning accuracy. (3) Thirdly, the actual measured data are used to demonstrate and analyze the reliability of the positioning model proposed by this paper. The experimental results show that the BDS/GPS/UWB tightly coupled navigation model can effectively improve the accuracy and availability of positioning. Compared with BDS/GPS SPP, the accuracy of this model is improved by 57.8%, 76.0% and 56.5% in the E, N and U directions, respectively. [ABSTRACT FROM AUTHOR]

The detection of track geometry parameters is essential for the safety of high-speed railway operation. To improve the accuracy and efficiency of the state detector of track geometry parameters, in this study we propose an inertial GNSS odometer integrated navigation system based on the federated Kalman, and a corresponding inertial track measurement system was also developed. This paper systematically introduces the construction process for the Kalman filter and data smoothing algorithm based on forward filtering and reverse smoothing. The engineering results show that the measurement accuracy of the track geometry parameters was better than 0.2 mm, and the detection speed was about 3 km/h. Thus, compared with the traditional Kalman filter method, the proposed design improved the measurement accuracy and met the requirements for the detection of geometric parameters of high-speed railway tracks. [ABSTRACT FROM AUTHOR]

Line matching is the foundation of three-dimensional (3D) outline reconstruction for city buildings in aerial photogrammetry. Many existing studies have good line matching effects when dealing with aerial images with short baselines and small viewing angles. However, when faced with wide-baseline and large viewing-angle images, the matching effect drops sharply or even fails altogether. This paper deals with an efficient and simple method to achieve better line matching performance by a pair of wide-baseline aerial images, which make use of viewpoint-in variance to conduct line matching in rectified image spaces. Firstly, the perspective transformation relationship between the image plane and the geoid plane can be established from a Positioning and Orientation System (POS). Then, according to perspective projection matrices, two original images are separately rectified to conformal images, whose perspective deformation of large viewing-angle can be eliminated. Finally, the rectified images are used to conduct line matching, and the matched line segments obtained are back-projected to the original images. Four pairs of urban oblique aerial images are used to demonstrate the validity and efficiency of this method. Compared with line matching on original images, the number and the correctness of the matched line segments are greatly improved. Moreover, there is no loss of time efficiency. The proposed method can also be applied to general UAV (Unmanned Aerial Vehicle) aerial photogrammetry and introduced into matching for other geometric features, such as points, circles, curves, etc. [ABSTRACT FROM AUTHOR]

Yellow rust is one of the most destructive diseases for winter wheat and has led to a significant decrease in winter wheat quality and yield. Identifying and monitoring yellow rust is of great importance for guiding agricultural production over large areas. Compared with traditional crop disease discrimination methods, remote sensing technology has proven to be a useful tool for accomplishing such a task at large scale. This study explores the potential of the Sentinel-2 Multispectral Instrument (MSI), a newly launched satellite with refined spatial resolution and three red-edge bands, for discriminating between yellow rust infection severities (i.e., healthy, slight, and severe) in winter wheat. The corresponding simulative multispectral bands for the Sentinel-2 sensor were calculated by the sensor’s relative spectral response (RSR) function based on the in situ hyperspectral data acquired at the canopy level. Three Sentinel-2 spectral bands, including B4 (Red), B5 (Re1), and B7 (Re3), were found to be sensitive bands using the random forest (RF) method. A new multispectral index, the Red Edge Disease Stress Index (REDSI), which consists of these sensitive bands, was proposed to detect yellow rust infection at different severity levels. The overall identification accuracy for REDSI was 84.1% and the kappa coefficient was 0.76. Moreover, REDSI performed better than other commonly used disease spectral indexes for yellow rust discrimination at the canopy scale. The optimal threshold method was adopted for mapping yellow rust infection at regional scales based on realistic Sentinel-2 multispectral image data to further assess REDSI’s ability for yellow rust detection. The overall accuracy was 85.2% and kappa coefficient was 0.67, which was found through validation against a set of field survey data. This study suggests that the Sentinel-2 MSI has the potential for yellow rust discrimination, and the newly proposed REDSI has great robustness and generalized ability for yellow rust detection at canopy and regional scales. Furthermore, our results suggest that the above remote sensing technology can be used to provide scientific guidance for monitoring and precise management of crop diseases and pests. [ABSTRACT FROM AUTHOR]

Economic globalization is developing more rapidly than ever before. At the same time, economic growth is accompanied by energy consumption and carbon emissions, so it is particularly important to estimate, analyze and evaluate the economy accurately. We compared different nighttime light (NTL) index models with various constraint conditions and analyzed their relationships with economic parameters by linear correlation. In this study, three indices were selected, including original NTL, improved impervious surface index (IISI) and vegetation highlights nighttime-light index (VHNI). In the meantime, all indices were built in a linear regression relationship with gross domestic product (GDP), employed population and power consumption in southeast China. In addition, the correlation coefficient R 2 was used to represent fitting degree. Overall, comparing the regression relationships with GDP of the three indices, VHNI performed best with the value of R 2 at 0.8632. For the employed population and power consumption regression with these three indices, the maximum R 2 of VHNI are 0.8647 and 0.7824 respectively, which are also the best performances in the three indices. For each individual province, the VHNI perform better than NTL and IISI in GDP regression, too. When taking employment population as the regression object, VHNI performs best in Zhejiang and Anhui provinces, but not all provinces. Finally, for power consumption regression, the value of VHNI  R 2 is better than NTL and IISI in every province except Hainan. The results show that, among the indices under different constraint conditions, the linear relationships between VHNI and GDP and power consumption are the strongest under vegetation constraint in southeast China. Therefore, VHNI index can be used for fitting analysis and prediction of economy and power consumption in the future. [ABSTRACT FROM AUTHOR]

At present, GNSS (Global Navigation Satellite System) positioning technology is widely used for outdoor positioning services because of its high-precision positioning characteristics. However, in indoor environments, effective position information cannot be provided, because of the signals being obscured. In order to improve the accuracy and continuity of indoor positioning systems, in this paper, we propose a PDR/UWB (Pedestrian Dead Reckoning and Ultra Wide Band) integrated navigation algorithm based on an adaptively robust EKF (Extended Kalman Filter) to address the problem of error accumulation in the PDR algorithm and gross errors in the location results of the UWB in non-line-of-sight scenarios. First, the basic principles of UWB and PDR location algorithms are given. Then, we propose a loose combination of the PDR and UWB algorithms by using the adaptively robust EKF. By using the robust factor to adjust the weight of the observation value to resist the influence of the gross error, and by adjusting the variance of the system adaptively according to the positioning scene, the algorithm can improve the robustness and heading factor of the PDR algorithm, which is constrained by indoor maps. Finally, the effectiveness of the algorithm is verified by the measured data. The experimental results showed that the algorithm can not only reduce the accumulation of PDR errors, but can also resist the influence of gross location errors under non-line-of-sight UWB scenarios. [ABSTRACT FROM AUTHOR]

Exhausted or abandoned underground longwall mining may lead to long-term residual subsidence on surface land, which can cause some problems when the mined-out land is used for construction, land reclamation and ecological reconstruction. Thus, it is important to assess the stability and suitability of the land with a consideration of residual surface subsidence. Assuming a linear monotonic decrease in the annual residual surface subsidence, the limit of the sum of the annual residual subsidence factor, and continuity between surface subsidence in the last year of the weakening period and the residual surface subsidence in the first year, we establish a model to calculate the duration of residual subsidence and the annual residual surface subsidence factor caused by abandoned longwall coal mining. The duration of residual surface subsidence increases with the increase in mining thickness as well as the factor of extreme residual subsidence. The proposed method can quantitatively calculate the annual residual subsidence, the accumulative residual subsidence, and the potential future accumulative residual subsidence. This approach can be used to reasonably evaluate the stability and suitability of old mining subsidence areas and will be beneficial for the design of mining subsidence land reclamation and ecological reconstruction. [ABSTRACT FROM AUTHOR]

Jointly using spatial and spectral information has been widely applied to hyperspectral image (HSI) classification. Especially, convolutional neural networks (CNN) have gained attention in recent years due to their detailed representation of features. However, most of CNN-based HSI classification methods mainly use patches as input classifier. This limits the range of use for spatial neighbor information and reduces processing efficiency in training and testing. To overcome this problem, we propose an image-based classification framework that is efficient and straightforward. Based on this framework, we propose a multiscale spatial-spectral CNN for HSIs (HyMSCN) to integrate both multiple receptive fields fused features and multiscale spatial features at different levels. The fused features are exploited using a lightweight block called the multiple receptive field feature block (MRFF), which contains various types of dilation convolution. By fusing multiple receptive field features and multiscale spatial features, the HyMSCN has comprehensive feature representation for classification. Experimental results from three real hyperspectral images prove the efficiency of the proposed framework. The proposed method also achieves superior performance for HSI classification. [ABSTRACT FROM AUTHOR]

Yellow rust, a widely known destructive wheat disease, affects wheat quality and causes large economic losses in wheat production. Hyperspectral remote sensing has shown potential for the detection of plant disease. This study aimed to analyze the spectral reflectance of the wheat canopy in the range of 350–1000 nm and to develop optimal spectral indices to detect yellow rust disease in wheat at different growth stages. The sensitive wavebands of healthy and infected wheat were located in the range 460–720 nm in the early-mid growth stage (from booting to anthesis), and in the ranges 568–709 nm and 725–1000 nm in the mid-late growth stage (from filling to milky ripeness), respectively. All possible three-band combinations over these sensitive wavebands were calculated as the forms of PRI (Photochemical Reflectance Index) and ARI (Anthocyanin Reflectance Index) at different growth stages and assessed to determine whether they could be used for estimating the severity of yellow rust disease. The optimal spectral index for estimating wheat infected by yellow rust disease was PRI (570, 525, 705) during the early-mid growth stage with R2 of 0.669, and ARI (860, 790, 750) during the mid-late growth stage with R2 of 0.888. Comparison of the proposed spectral indices with previously reported vegetation indices were able to satisfactorily discriminate wheat yellow rust. The classification accuracy for PRI (570, 525, 705) was 80.6% and the kappa coefficient was 0.61 in early-mid growth stage, and the classification accuracy for ARI (860, 790, 750) was 91.9% and the kappa coefficient was 0.75 in mid-late growth stage. The classification accuracy of the two indices reached 84.1% and 93.2% in the early-mid and mid-late growth stages in the validated dataset, respectively. We conclude that the three-band spectral indices PRI (570, 525, 705) and ARI (860, 790, 750) are optimal for monitoring yellow rust infection in these two growth stages, respectively. Our method is expected to provide a technical basis for wheat disease detection and prevention in the early-mid growth stage, and the estimation of yield losses in the mid-late growth stage. [ABSTRACT FROM AUTHOR]

Aerial images are widely used for building detection. However, the performance of building detection methods based on aerial images alone is typically poorer than that of building detection methods using both LiDAR and image data. To overcome these limitations, we present a framework for detecting and regularizing the boundary of individual buildings using a feature-level-fusion strategy based on features from dense image matching (DIM) point clouds, orthophoto and original aerial images. The proposed framework is divided into three stages. In the first stage, the features from the original aerial image and DIM points are fused to detect buildings and obtain the so-called blob of an individual building. Then, a feature-level fusion strategy is applied to match the straight-line segments from original aerial images so that the matched straight-line segment can be used in the later stage. Finally, a new footprint generation algorithm is proposed to generate the building footprint by combining the matched straight-line segments and the boundary of the blob of the individual building. The performance of our framework is evaluated on a vertical aerial image dataset (Vaihingen) and two oblique aerial image datasets (Potsdam and Lunen). The experimental results reveal 89% to 96% per-area completeness with accuracy above almost 93%. Relative to six existing methods, our proposed method not only is more robust but also can obtain a similar performance to the methods based on LiDAR and images. [ABSTRACT FROM AUTHOR]

The progressive TIN (triangular irregular network) densification (PTD) filter algorithm is widely used for filtering point clouds. In the PTD algorithm, the iterative densification parameters become smaller over the entire process of filtering. This leads to the performance—especially the type I errors of the PTD algorithm—being poor for point clouds with high density and standard variance. Hence, an improved PTD filtering algorithm for point clouds with high density and variance is proposed in this paper. This improved PTD method divides the iterative densification process into two stages. In the first stage, the iterative densification process of the PTD algorithm is used, and the two densification parameters become smaller. When the density of points belonging to the TIN is higher than a certain value (in this paper, we define this density as the standard variance intervention density), the iterative densification process moves into the second stage. In the second stage, a new iterative densification strategy based on multi-scales is proposed, and the angle threshold becomes larger. The experimental results show that the improved PTD algorithm can effectively reduce the type I errors and total errors of the DIM point clouds by 7.53% and 4.09%, respectively, compared with the PTD algorithm. Although the type II errors increase slightly in our improved method, the wrongly added objective points have little effect on the accuracy of the generated DSM. In short, our improved PTD method perfects the classical PTD method and offers a better solution for filtering point clouds with high density and standard variance. [ABSTRACT FROM AUTHOR]

Yellow rust, a widely known destructive wheat disease, affects wheat quality and causes large economic losses in wheat production. Hyperspectral remote sensing has shown potential for the detection of plant disease. This study aimed to analyze the spectral reflectance of the wheat canopy in the range of 350⁻1000 nm and to develop optimal spectral indices to detect yellow rust disease in wheat at different growth stages. The sensitive wavebands of healthy and infected wheat were located in the range 460⁻720 nm in the early-mid growth stage (from booting to anthesis), and in the ranges 568⁻709 nm and 725⁻1000 nm in the mid-late growth stage (from filling to milky ripeness), respectively. All possible three-band combinations over these sensitive wavebands were calculated as the forms of PRI (Photochemical Reflectance Index) and ARI (Anthocyanin Reflectance Index) at different growth stages and assessed to determine whether they could be used for estimating the severity of yellow rust disease. The optimal spectral index for estimating wheat infected by yellow rust disease was PRI (570, 525, 705) during the early-mid growth stage with R² of 0.669, and ARI (860, 790, 750) during the mid-late growth stage with R² of 0.888. Comparison of the proposed spectral indices with previously reported vegetation indices were able to satisfactorily discriminate wheat yellow rust. The classification accuracy for PRI (570, 525, 705) was 80.6% and the kappa coefficient was 0.61 in early-mid growth stage, and the classification accuracy for ARI (860, 790, 750) was 91.9% and the kappa coefficient was 0.75 in mid-late growth stage. The classification accuracy of the two indices reached 84.1% and 93.2% in the early-mid and mid-late growth stages in the validated dataset, respectively. We conclude that the three-band spectral indices PRI (570, 525, 705) and ARI (860, 790, 750) are optimal for monitoring yellow rust infection in these two growth stages, respectively. Our method is expected to provide a technical basis for wheat disease detection and prevention in the early-mid growth stage, and the estimation of yield losses in the mid-late growth stage.