Active remote sensing technology represented by multi-beam and lidar provides an important approach for the effective acquisition of underwater coral reef geomorphological information. A spatially continuous surface model of coral reef geomorphology reconstructed from active remote sensing datasets can provide important geomorphological parameters for the research of coral reef geomorphological and ecological changes. However, the surface modeling methods commonly used in previous studies, such as ordinary kriging (OK) and natural neighborhood (NN), often represent a “smoothing effect”, which causes the strong spatial variability of coral reefs to be imprecisely reflected by the reconstructed surfaces, thus affecting the accurate calculation of subsequent geomorphological parameters. In this study, a spatial variability modified OK (OK-SVM) method is proposed to reduce the impact of the “smoothing effect” on the high-precision reconstruction of the complex geomorphology of coral reefs. The OK-SVM adopts a collaborative strategy of global parameter transformation, local residual correction, and extremum correction to modify the spatial variability of the reconstructed model, while maintaining high local accuracy. The experimental results show that the OK-SVM has strong robustness to spatial variability modification. This method was applied to the geomorphological reconstruction of the northern area of a coral atoll in the Nansha Islands, South China Sea, and the performance was compared with that of OK and NN. The results show that OK-SVM has higher numerical accuracy and attribute accuracy in detailed morphological fidelity, and is more adaptable in the geomorphological reconstruction of coral reefs with strong spatial variability. This method is relatively reliable for achieving high-precision reconstruction of complex geomorphology of coral reefs from active remote sensing datasets, and has potential to be extended to other geomorphological reconstruction applications.
Corner feature matching remains a difficult task for wide-baseline images because of viewpoint distortion, surface discontinuities, and partial occlusions. In this paper, we propose a robust Harris corner matching method based on the quasi-homography transform (QHT) and self-adaptive window. Our method is divided into three steps. First, high-quality Harris corners were extracted from stereo images using optimal detecting, and initial matches were simultaneously acquired by integrating complementary affine-invariant features and the scale-invariant feature transform descriptor. Second, the pair of fundamental matrices was estimated based on the initial matches and improved random sample consensus algorithm. Subsequently, the global QHT was produced by duplicate epipolar geometries. Third, conjugate Harris corners were obtained by combining QHT and normalized cross correlation, and the accuracy of the corresponding points was further improved based on self-adaptive least-squares matching (SALSM). Experiments on six groups of wide-baseline images demonstrate the effectiveness of the proposed method, and a comprehensive comparison with the existing corner matching algorithms indicates that our method has notable superiority in terms of accuracy and distribution. The main contribution of this paper is that the proposed global QHT can reduce the search range effectively for candidates, and the proposed SALSM can notably improve the accuracy of the corresponding corners.
Monitoring regional terrestrial water load deformation is of great significance to the dynamic maintenance and hydrodynamic study of the regional benchmark framework. In view of the lack of a spatial interpolation method based on the GNSS (Global Navigation Satellite System) elevation time series for obtaining terrestrial water load deformation information, this paper proposes to employ a CORS (Continuously Operating Reference Stations) network combined with environmental loading data, such as ECMWF (European Centre for Medium-Range Weather Forecasts) atmospheric data, the GLDAS (Global Land Data Assimilation System) hydrological model, and MSLA (Mean Sea Level Anomaly) data. Based on the load deformation theory and spherical harmonic analysis method, we took 38 CORS stations in southeast Zhejiang province as an example and comprehensively determined the vertical deformation of the crust as caused by regional terrestrial water load changes from January 2015 to December 2017, and then compared these data with the GRACE (Gravity Recovery and Climate Experiment) satellite. The results show that the vertical deformation value of the terrestrial water load in southeast Zhejiang, as monitored by the CORS network, can reach a centimeter, and the amplitude changes from -1.8 cm to 2.4 cm. The seasonal change is obvious, and the spatial distribution takes a ladder form from inland to coastal regions. The surface vertical deformation caused by groundwater load changes in the east-west-south-north-central sub-regions show obvious fluctuations from 2015 to 2017, and the trends of the five sub-regions are consistent. The amplitude of surface vertical deformation caused by groundwater load change in the west is higher than that in the east. We tested the use of GRACE for the verification of CORS network monitoring results and found a relatively consistent temporal distribution between both data sets after phase delay correction on GRACE, except for in three months-November in 2015, and January and February in 2016. The results show that the comprehensive solution based on the CORS network can effectively improve the monitoring of crustal vertical deformation during regional terrestrial water load change.
A novel two-way ranging approach was introduced into the Wireless Fidelity (WiFi) standard, and its ranging accuracy reached one meter in a low multipath environment. However, in harsh environments due to multipath or non-line of sight (NLOS), the range measurement based on the WiFi round trip time (RTT) usually has low accuracy and cannot maintain the one-meter accuracy. Thus, this paper proposes an indoor positioning method based on Gaussian process regression (GPR) for harsh environments. There are two stages in the proposed method: construction of a positioning model and location estimation. In the model construction stage, based on known positions of access points (APs), we can determine the position coordinates of some ground points and the reference distances between them and the APs, and the offline ranging difference fingerprints can be generated by the reference distances, which means that there is no need to collect data. Gaussian process regression (GPR) utilizes offline ranging difference fingerprints based on the reference distance to establish a positioning model, and the particle swarm optimization (PSO) algorithm is employed to estimate the GPR hyperparameters. In the location estimation stage, the gathered actual range measurements generate the online ranging difference fingerprint, which is the input data of the positioning model. The output of the model is the estimated position of the smartphone. Experimental results show that the mean errors (MEs) of the proposed method and Least Squares (LS) algorithm are 1.097 and 3.484 meters, respectively, in a harsh environment, and the positioning accuracy of the proposed method improved by 68.5% compared with the LS algorithm.
This paper presents a novel registration method for oblique synthetic aperture radar (SAR) images based on complementary integrated filtering (CIF) and multilevel matching. Our algorithm is divided into three steps. First, we considered different type of noises and employed the CIF to increase the signal-to-noise ratio of SAR images. Second, complementary affine invariant features, namely maximally stable extremal regions and Harris-affine features, were extracted simultaneously from image pairs, and then the initial matches were obtained based on the scale invariant feature transform (SIFT) descriptor and Euclidean distance. Therefore, the fundamental and homography matrixes could be calculated between image pairs, and then more matches were obtained under quasi-affine invariant SIFT (QAISIFT) and the hybrid geometric constraints. We further implemented the least square matching (LSM) based on the second-polynomial geometric model (SPGM), and thus the matching error of each corresponding point can be compensated according to the optimal SPGM. Third, the precise registration was achieved based on the matches of the second step. Experiments on four groups of oblique SAR images demonstrated the effectiveness of the proposed method. The contribution of this paper includes three aspects. One is that the proposed CIF can remove SAR image noise as much as possible; another is that the proposed QAISIFT can achieve near affine invariance across viewpoint change images; the third is that the advanced LSM can notably improve the accuracy of feature matches.
Compared with the synthetic aperture radar (SAR) image processing theory based on local neighborhood, the nonlocal theory is not limited to a local neighborhood of an image and has great potential in change detection of SAR images. In this study, an approach using ratio-based nonlocal information (RNLI) is proposed for change detection in multitemporal SAR images. First, the RNLI is extracted from a spatial–temporal nonlocal neighborhood where the similarity of two pixels in the nonlocal neighborhood is well characterized by the proposed ratio-based Gaussian kernel function. The parameters of RNLI: noise level and matching window size are adaptively determined to avoid the uncertainty of the change detection result caused by user experience. Second, the difference image is generated by using the RNLI and the ratio operator. Finally, the change map is obtained by segmenting the difference image with a threshold. Experiments conducted on two real datasets and two simulated datasets showed that the proposed method performed better than the other advanced change detection methods, which can better retain the edge information of the changed area while reducing the overall error of the change detection results.
Lake area, water level, and water storage changes of terminal lakes are vital for regional water resource management and for understanding local hydrological processes. Nevertheless, due to the complex geographical conditions, it is difficult to investigate and analyze this change in ungauged regions. This study focuses on the ungauged, semi-arid Gahai Lake, a typical small terminal lake in the Qaidam Basin. In addition to the scant observed data, satellite altimetry is scarce for the excessively large fraction of outlier points. Here, we proposed an effective and simple algorithm for extracting available lake elevation points from CryoSat-2, ICESat-2 and Sentinel-3. Combining with the area data from Landsat, Gaofen (GF), and Ziyuan (ZY) satellites, we built an optimal hypsographic curve (lake area versus water level) based on the existing short-term data. Cross-validation was used to validate whether the curve accurately could predict the lake water level in other periods. In addition, we used multisource high-resolution images including Landsat and digital maps to extract the area data from 1975 to 2020, and we applied the curve to estimate the water level for the corresponding period. Additionally, we adopted the pyramidal frustum model (PFM) and the integral model (IM) to estimate the long-term water storage changes, and analyzed the differences between these two models. We found that there has been an obvious change in the area, water level, and water storage since the beginning of the 21st century, which reflects the impact of climate change and human activities on hydrologic processes in the basin. Importantly, agricultural activities have caused a rapid increase in water storage in the Gahai Lake over the past decade. We collected as much multisource satellite data as possible; thus, we estimated the long-term variations in the area, water level, and water storage of a small terminal lake combining multiple models, which can provide an effective method to monitor lake changes in ungauged basins.
Monitoring and predicting the regional groundwater storage (GWS) fluctuation is an essential support for effectively managing water resources. Therefore, taking Shandong Province as an example, the data from Gravity Recovery and Climate Experiment (GRACE) and GRACE Follow-On (GRACE-FO) is used to invert GWS fluctuation from January 2003 to December 2022 together with Watergap Global Hydrological Model (WGHM), in-situ groundwater volume and level data. The spatio-temporal characteristics are decomposed using Independent Components Analysis (ICA), and the impact factors, such as precipitation and human activities, which are also analyzed. To predict the short-time changes of GWS, the Support Vector Machines (SVM) is adopted together with three commonly used methods Long Short-Term Memory (LSTM), Singular Spectrum Analysis (SSA), Auto-Regressive Moving Average Model (ARMA), as the comparison. The results show that: (1) The loss intensity of western GWS is significantly greater than those in coastal areas. From 2003 to 2006, GWS increased sharply; during 2007 to 2014, there exists a loss rate - 5.80 ± 2.28 mm/a of GWS; the linear trend of GWS change is - 5.39 ± 3.65 mm/a from 2015 to 2022, may be mainly due to the effect of South-to-North Water Diversion Project. The correlation coefficient between GRACE and WGHM is 0.67, which is consistent with in-situ groundwater volume and level. (2) The GWS has higher positive correlation with monthly Global Precipitation Climatology Project (GPCP) considering time delay after moving average, which has the similar energy spectrum depending on Continuous Wavelet Transform (CWT) method. In addition, the influencing facotrs on annual GWS fluctuation are analyzed, the correlation coefficient between GWS and in-situ data including the consumption of groundwater mining, farmland irrigation is 0.80, 0.71, respectively. (3) For the GWS prediction, SVM method is adopted to analyze, three training samples with 180, 204 and 228 months are established with the goodness-of-fit all higher than 0.97. The correlation coefficients are 0.56, 0.75, 0.68; RMSE is 5.26, 4.42, 5.65 mm; NSE is 0.28, 0.43, 0.36, respectively. The performance of SVM model is better than the other methods for the short-term prediction.
Hyperspectral remote sensing provides apromising solution for estimation of heavy metal concentration in soil. However, few studies have focused on the detection of soil heavy metal concentration by the frequency domain analysis. In this letter, the Hilbert–Huang transform (HHT) is introduced to fully explore the hidden information in the spectrum. Based on experimentally acquired spectra of soil contaminated by lead (Pb) and chemical data, HHT was employed to obtain the Hilbert energy spectra (HES) and intrinsic model function (IMF) component of spectra. Then, characteristic spectral bands of Pb could be fully mined through these components, and random forest was utilized to retrieve Pb concentration. The following conclusions are drawn: (1) the components of HHT has good correlation with Pb concentration in the 340–1400 nm, and they can better highlight response of Pb concentration in the 1440–2450 nm; (2) characteristic bands extracted by the IMF components and HES are quite effective as input variables, and its correlation coefficient (r) and root mean square error (RMSE) for random forest is 0.9676 and 0.0741, respectively. Compared with the variables of original spectral reflectance and spectral domain transformation, the proposed spectral HHT variables achieve the highest estimation accuracy.
Zero Doppler centroid imaging is of great significance in synthetic aperture radar interferometry (InSAR). Because the moon's orbit is elliptical and its orientation cannot be changed artificially, the zero Doppler centroid of lunar-based synthetic aperture radar (LB-SAR) is not steerable and will vary with lunar rotation and SAR system's position on lunar surface. Therefore, the distribution of zero Doppler centroid of LB-SAR will provide an important reference for observation model design of LB-SAR/InSAR. In this letter, we propose a calculation method of zero Doppler centroid by using the Jet Propulsion Laboratory (JPL)/NASA development ephemeris 430 (DE430), investigate the spatio-temporal distribution of the zero-Doppler line (ZDL). The results show that 1) the position of zero Doppler centroid deviates from the central meridian of a sublunar point from 0° to 45° within a sidereal month, 2) the maximum longitude difference of ZDLs corresponding to different SAR system's position on lunar surface is about 60 km, and 3) the greatest effect of topography on ZDL estimation is about 1.1 km. The proposed calculation method of ZDL and results analysis are of instructive significance to the site selection and attitude control of lunar-based radar in the design of LB-SAR/InSAR.
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