A Semi-Empirical Inversion Model for Assessing Surface Soil Moisture using AMSR-E Brightness Temperatures

In 2004-2005, 2007 and 2009, three major drought disasters occurred in Guangdong Province of southern China, which caused serious economic losses. Hence, it has recently become an important research subject in China to monitor surface soil moisture (SSM) and the drought disaster quickly and accurately. SSM is an effective indicator for characterizing the degree of drought. First, using the brightness temperatures (T[subscript b]) of the Advanced Microwave Scanning Radiometer on the EOS Aqua Satellite (AMSR-E), a modified surface roughness index was developed to map the land surface roughness. Then by combining microwave polarization difference indices (MPDI)-based vegetation cover classification and the modified surface roughness index, a simple semi-empirical model of SSM was derived from the passive microwave radiative transfer equation using AMSR-E C-band T[subscript b] and observed surface soil temperature (T[subscript s]). The model was inverted to calculate SSM. The results show the ability to discriminate over a broad range of SSM (7%~73%) with an accuracy of 2.11% in bare ground and flat areas (R² =0.87), 2.89% in sparse vegetation and flat surface areas (R²=0.85), about 6%~9% in dense vegetation areas and rough surface areas (0.80≤R²≤0.83). The simulation results were also validated using in-situ SSM data (R²=0.87, RMSE=6.36%). Time series mapping of SSM from AMSR-E imageries further demonstrated that the presented method was effective to detect the initiation, duration and recovery of the drought events.This is the author's peer-reviewed final manuscript, as accepted by the publisher. The published article is copyrighted by Elsevier and can be found at: http://www.journals.elsevier.com/journal-of-hydrology/.Keywords: Drought disaster, Brightness temperature (T[subscript b]), South China, Semi-Empirical model, Surface soil moisture (SSM), AMSR-E, Passive microwave remote sensin

External Parameter Calibration Method of Vehicle Laser Scanning System Based on Planar Features

The calibration of the external parameters of the laser scanner is the precondition and guarantee for obtain high-precision 3D geographic data. Most of the traditional calibration methods require setting up a special calibration field, manual collection of checkpoints, or the amount of calculation in the process of solving is large. Based on this, an automatic calibration method is proposed in this paper, by collecting point cloud data in the same area with different vehicle directions, extracting planar features data and automating registration of these planar features data, through the co-calibration of planar features of different angles.The proposed method realizes the coincidence of point clouds collected by different vehicles in three-dimensional space and finally completes the calibration of the system external parameters. The results show that the method is automatic for the calibration of the external parameters of the vehicle laser scanning system, reduces the need for manual participation, and achieves high precision

A Method for the Destriping of an Orbita Hyperspectral Image with Adaptive Moment Matching and Unidirectional Total Variation

The Orbita hyperspectral satellite (OHS) is the first hyperspectral satellite with surface coating technology for sensors in the world. It includes 32 bands from visible to near-infrared wavelengths. However, technology such as the fabricating process of complementary metal–oxide–semiconductor (CMOS) sensors makes the image contain a lot of random and unsystematic stripe noise, which is so bad that it seriously affects visual interpretation, object recognition and the application of the OHS data. Although a large number of stripe removal algorithms have been proposed, very few of them take into account the characteristics of OHS sensors and analyze the causes of OHS data noise. In this paper, we propose a destriping algorithm for OHS data. Firstly, we use both the adaptive moment matching method and multi-level unidirectional total variation method to remove stripes. Then a model based on piecewise linear least squares fitting is proposed to restore the vertical details lost in the first step. Moreover, we further utilize the spectral information of the OHS image, and extend our 2-D destriping method to the 3-D case. Results demonstrate that the proposed method provides the optimal destriping result on both qualitative and quantitative assessments. Moreover, the experimental results show that our method is superior to the existing single-band and multispectral destriping methods. Also, we further use the algorithm to the stripe noise removal of other real remote sensing images, and excellent image quality is obtained, which proves the universality of the algorithm

Reconstruction of Indoor Navigation Elements for Point Cloud of Buildings with Occlusions and Openings by Wall Segment Restoration from Indoor Context Labeling

Indoor 3D reconstruction and navigation element extraction with point cloud data has become a research focus in recent years, which has important application in community refinement management, emergency rescue and evacuation, etc. Aiming at the problem that the complete wall surfaces cannot be obtained in the indoor space affected by the occluded objects and the existing methods of navigation element extraction are over-segmented or under-segmented, we propose a method to automatically reconstruct indoor navigation elements from unstructured 3D point cloud of buildings with occlusions and openings. First, the outline and occupancy information provided by the horizontal projection of the point cloud was used to guide the wall segment restoration. Second, we simulate the scanning process of a laser scanner for segmentation. Third, we use projection statistical graphs and given rules to identify missing wall surfaces and “hidden doors”. The method is tested on several building datasets with complex structures. The results show that the method can detect and reconstruct indoor navigation elements without viewpoint information. The means of deviation in the reconstructed models is between 0–5 cm, and the completeness and correction are greater than 80%. However, the proposed method also has some limitations for the extraction of “thick doors” with a large number of occluded, non-planar components

Deformation Detection Method of Mine Tunnel Based on Mobile Detection System

Subway structure safety detection is an important method to ensure the safe operation of trains. Efficient, high-precision, and automatic tunnel clearance detection is the key to ensure safe operations. This study introduces a mobile tunnel scanning system that integrates a scanner, an inertial measurement unit (IMU), and a rail car. Global Navigation Satellite System (GNSS) time and system hardware calibration are used to synchronize time and space information of the system; the attitude and speed are corrected using the control points from the tunnel to improve the accuracy of absolute positioning. The section coordinate system is converted using the control points and system calibration parameters to complete the tunnel clearance inspection, and the distance between the nearest point of the section and the clear height of the vault is given. Taking Fengxi Road’s Bashan tunnel section of Chongqing Metro Line 5 as an example, the overall system accuracy was tested. The accuracy of chord line measurements was within 1 mm, the internal coincidence accuracy of repeated measurements of the vault clear height was 1.1 mm, the internal coincidence accuracy of repeated measurements of the closest gauge point was 4.8 mm, and the system calibration accuracy was approximately 2 mm. Compared with the existing scheme, the system combines absolute measurement and relative measurement mode to judge the structural safety of tunnel section from multiple angles, high precision, and high efficiency

Feature-Based Laser Scan Matching and Its Application for Indoor Mapping

Scan matching, an approach to recover the relative position and orientation of two laser scans, is a very important technique for indoor positioning and indoor modeling. The iterative closest point (ICP) algorithm and its variants are the most well-known techniques for such a problem. However, ICP algorithms rely highly on the initial guess of the relative transformation, which will reduce its power for practical applications. In this paper, an initial-free 2D laser scan matching method based on point and line features is proposed. We carefully design a framework for the detection of point and line feature correspondences. First, distinct feature points are detected based on an extended 1D SIFT, and line features are extracted via a modified Split-and-Merge algorithm. In this stage, we also give an effective strategy for discarding unreliable features. The point and line features are then described by a distance histogram; the pairs achieving best matching scores are accepted as potential correct correspondences. The histogram cluster technique is adapted to filter outliers and provide an accurate initial value of the rigid transformation. We also proposed a new relative pose estimation method that is robust to outliers. We use the lq-norm (0 < q < 1) metric in this approach, in contrast to classic optimization methods whose cost function is based on the l2-norm of residuals. Extensive experiments on real data demonstrate that the proposed method is almost as accurate as ICPs and is initial free. We also show that our scan matching method can be integrated into a simultaneous localization and mapping (SLAM) system for indoor mapping

TransUNet++SAR: Change Detection with Deep Learning about Architectural Ensemble in SAR Images

In the application of change detection satellite remote sensing images, synthetic aperture radar (SAR) images have become a more important data source. This paper proposes a new end-to-end SAR image change network architecture—TransUNet++SAR—that combines Transformer with UNet++. First, the convolutional neural network (CNN) was used to obtain the feature maps of the single time SAR images layer by layer. Tokenized image patches were encoded to extract rich global context information. Using improved Transformer for effective modeling of global semantic relations can generate rich contextual feature representations. Then, we used the decoder to upsample the encoded features, connected the encoded multi-scale features with the high-level features by sequential connection to learn the local-global semantic features, recovered the full spatial resolution of the feature map, and achieved accurate localization. In the UNet++ structure, the bitemporal SAR images are composed of two single networks, which have shared weights to learn the features of the single temporal image layer by layer to avoid the influence of SAR image noise and pseudo-change on the deep learning process. The experiment results show that the experimental effect of TransUNet++SAR on the Beijing, Guangzhou, and Qingdao datasets were significantly better than other deep learning SAR image change detection algorithms. At the same time, compared with other Transformer related change detection algorithms, the description of the changed area edge was more accurate. In the dataset experiments, the model had higher indices than the other models, especially the Beijing building change datasets, where the IOU was 9.79% higher and F1-score was 4.38% higher

Precise Positioning Method of Moving Laser Point Cloud in Shield Tunnel Based on Bolt Hole Extraction

Mobile laser scanning technology used for deformation detection of shield tunnel is usually two-dimensional, which is expanded into three-dimensional (3D) through mileage, resulting in low positioning accuracy. This study proposes a 3D laser point cloud positioning method that is divided into rings in the mileage direction and blocks in the ring direction to improve the positional accuracy for shield tunnels. First, the cylindrical tunnel wall is expanded into a plane and the bolt holes are extracted using the self-adaptive parameter adjustment cloth simulation filter (CSF) algorithm combined with a density-based spatial clustering of applications with noise (DBSCAN) algorithm. Second, the mean-shift algorithm is used to obtain the center point of the bolt hole, and a model is designed to recognize the center point of different splicing blocks. Finally, the center point is combined with the standard straight-line equation to fit the straight-line positioning seam, achieving an accurate ring and block segmentation of a shield tunnel as a 3D laser point cloud. The proposed method is compared with existing methods to verify its feasibility and high accuracy using the seams located by the measured tunnel point cloud data and in the measured point cloud. The average differences between the circumferential seams positioned using the proposed method and those in the point cloud at the left waist, vault, and right waist were 3, 4, and 5 mm, respectively, and the average difference between the longitudinal seams was 3.4 mm The proposed research method provides important technical and theoretical support for tunnel safety monitoring and detection

A Microtopographic Feature Analysis-Based LiDAR Data Processing Approach for the Identification of Chu Tombs

Most of the cultural sites hidden under dense vegetation in the mountains of China have been destroyed. In this paper, we present a microtopographic feature analysis (MFA)-based Light Detection and Ranging (LiDAR) data processing approach and an archaeological pattern-oriented point cloud segmentation (APoPCS) algorithm that we developed for the classification of archaeological objects and terrain points and the detection of archaeological remains. The archaeological features and patterns are interpreted and extracted from LiDAR point cloud data to construct an archaeological object pattern database. A microtopographic factor is calculated based on the archaeological object patterns, and this factor converts the massive point cloud data into a raster feature image. A fuzzy clustering algorithm based on the archaeological object patterns is presented for raster feature image segmentation and the detection of archaeological remains. Using the proposed approach, we investigated four typical areas with different types of Chu tombs in Central China, which had dense vegetation and high population densities. Our research results show that the proposed LiDAR data processing approach can identify archaeological remains from large-volume and massive LiDAR data, as well as in areas with dense vegetation and trees. The studies of different archaeological object patterns are important for improving the robustness of the proposed APoPCS algorithm for the extraction of archaeological remains