Dichloridotris(trimethyl­phosphine)nickel(II)

The title compound, [NiCl2(C3H9P)3], was obtained as a product of the reaction of [NiCl2(PMe3)2] with an equivalent trimethyl­phosphine in diethyl ether. It easily loses trimethyl­phosphine at room temperature to give NiCl2(PMe3)2. There are two independent mol­ecules in the asymmetric unit, and their bond lengths and angles are similar. The Ni environment is trigonal bipyramidal. One Ni, one P and two Cl atoms lie in the equatorial plane, with the remaining two P atoms occupying axial positions. The equatorial Ni—P bond length is shorter than the axial bond lengths

A New Combined Adjustment Model for Geolocation Accuracy Improvement of Multiple Sources Optical and SAR Imagery

Numerous earth observation data obtained from different platforms have been widely used in various fields, and geometric calibration is a fundamental step for these applications. Traditional calibration methods are developed based on the rational function model (RFM), which is produced by image vendors as a substitution of the rigorous sensor model (RSM). Generally, the fitting accuracy of the RFM is much higher than 1 pixel, whereas the result decreases to several pixels in mountainous areas, especially for Synthetic Aperture Radar (SAR) imagery. Therefore, this paper proposes a new combined adjustment for geolocation accuracy improvement of multiple sources satellite SAR and optical imagery. Tie points are extracted based on a robust image matching algorithm, and relationships between the parameters of the range-doppler (RD) model and the RFM are developed by transformed into the same Geodetic Coordinate systems. At the same time, a heterogeneous weight strategy is designed for better convergence. Experimental results indicate that our proposed model can achieve much higher geolocation accuracy with approximately 2.60 pixels in the X direction and 3.50 pixels in the Y direction. Compared with traditional methods developed based on RFM, our proposed model provides a new way for synergistic use of multiple sources remote sensing data

A New Combined Adjustment Model for Geolocation Accuracy Improvement of Multiple Sources Optical and SAR Imagery

Numerous earth observation data obtained from different platforms have been widely used in various fields, and geometric calibration is a fundamental step for these applications. Traditional calibration methods are developed based on the rational function model (RFM), which is produced by image vendors as a substitution of the rigorous sensor model (RSM). Generally, the fitting accuracy of the RFM is much higher than 1 pixel, whereas the result decreases to several pixels in mountainous areas, especially for Synthetic Aperture Radar (SAR) imagery. Therefore, this paper proposes a new combined adjustment for geolocation accuracy improvement of multiple sources satellite SAR and optical imagery. Tie points are extracted based on a robust image matching algorithm, and relationships between the parameters of the range-doppler (RD) model and the RFM are developed by transformed into the same Geodetic Coordinate systems. At the same time, a heterogeneous weight strategy is designed for better convergence. Experimental results indicate that our proposed model can achieve much higher geolocation accuracy with approximately 2.60 pixels in the X direction and 3.50 pixels in the Y direction. Compared with traditional methods developed based on RFM, our proposed model provides a new way for synergistic use of multiple sources remote sensing data

Geo-Positioning Accuracy Improvement of Multi-Mode GF-3 Satellite SAR Imagery Based on Error Sources Analysis

The GaoFen-3 (GF-3) satellite is the only synthetic aperture radar (SAR) satellite in the High-Resolution Earth Observation System Project, which is the first C-band full-polarization SAR satellite in China. In this paper, we proposed some error sources-based weight strategies to improve the geometric performance of multi-mode GF-3 satellite SAR images without using ground control points (GCPs). To get enough tie points, a robust SAR image registration method and the SAR-features from accelerated segment test (SAR-FAST) method is used to achieve the image registration and tie point extraction. Then, the original position of these tie points in object-space is calculated with the help of the space intersection method. With the dataset clustered by the density-based spatial clustering of applications with noise (DBSCAN) algorithm, we undertake the block adjustment with a bias-compensated rational function model (RFM) aided to improve the geometric performance of these multi-mode GF-3 satellite SAR images. Different weight strategies are proposed to develop the normal equation matrix according to the error sources analysis of GF-3 satellite SAR images, and the preconditioned conjugate gradient (PCG) method is utilized to solve the normal equation. The experimental results indicate that our proposed method can improve the geometric positioning accuracy of GF-3 satellite SAR images within 2 pixels

Pre-Processing of Inner CCD Image Stitching of the SDGSAT-1 Satellite

Spliced optical satellite cameras suffering from low stitching accuracy are influenced by various factors which can greatly restrict their applications. Most previous studies have focused on the geometric precision of stitched images, which is influenced by the stitching consistency and the relationships between different inner CCD (Charge-Coupled Device) images. Therefore, the stitching accuracy is of great significance in multiple CCD image production. Traditionally, the line-time normalization method has been applied for inner CCD image stitching based on designed line-times with the assumption of uniform sampling during imaging. However, the misalignment of the designed and actual line-time affected by various factors can lead to image distortion. Therefore, this paper investigates the performance of different normalization methods to produce stitched images with higher geometric performance using the actual line-time. First, the geometric distortions caused by misalignments between the designed and actual line-time are analyzed to show the differences in sampling rate and step-points. To overcome the distortions introduced by the fitting error of the designed line-time, three fine normalization methods based on the actual line-time, respectively called scene-based, block-based, and line-based line-time normalization methods, are introduced and compared with the traditional method. The scene-based and block-based line-time normalization methods fit the actual line-time section-by-section, while the line-based method builds the relationships between adjacent inner CCD images line-by-line. Images obtained from the Sustainable Development Goals Satellite 1 (SDGSAT-1) satellite are used for verification of different methods. The performance of the designed line-time normalization method and three fine actual line-time normalization methods is compared; the stitching accuracy can reach about 0.8, 0.56, 0.5, and 0.45 pixels, respectively. The time consumption of these four compared methods is about 5.5 s, 4.9 s, 5.4 s, and 58.9 s, respectively. Therefore, the block-based actual line-time normalization method utilized in practice can provide a good balance between running time and accuracy. In the future, we intend to find a new way to improve the efficiency of line-based line-time normalization methods to produce stitched images with higher geometric consistency and accuracy

Geometric Positioning Accuracy Improvement of ZY-3 Satellite Imagery Based on Statistical Learning Theory

With the increasing demand for high-resolution remote sensing images for mapping and monitoring the Earth’s environment, geometric positioning accuracy improvement plays a significant role in the image preprocessing step. Based on the statistical learning theory, we propose a new method to improve the geometric positioning accuracy without ground control points (GCPs). Multi-temporal images from the ZY-3 satellite are tested and the bias-compensated rational function model (RFM) is applied as the block adjustment model in our experiment. An easy and stable weight strategy and the fast iterative shrinkage-thresholding (FIST) algorithm which is widely used in the field of compressive sensing are improved and utilized to define the normal equation matrix and solve it. Then, the residual errors after traditional block adjustment are acquired and tested with the newly proposed inherent error compensation model based on statistical learning theory. The final results indicate that the geometric positioning accuracy of ZY-3 satellite imagery can be improved greatly with our proposed method

GPU Accelerated Processing Method for Feature Point Extraction and Matching in Satellite SAR Images

This paper addresses the challenge of extracting feature points and image matching in Synthetic Aperture Radar (SAR) satellite images, particularly focusing on large-scale embedding. The widely used Scale Invariant Transform (SIFT) algorithm, successful in computer vision and optical satellite image matching, faces challenges when applied to satellite SAR images due to the presence of speckle noise, leading to increased matching errors. The SAR–SIFT method is explored and analyzed in-depth, considering the unique characteristics of satellite SAR images. To enhance the efficiency of matching identical feature points in two satellite SAR images, the paper proposes a Graphics Processing Unit (GPU) mapping implementation based on the SAR–SIFT algorithm. The paper introduces a multi-GPU collaborative acceleration strategy for SAR image matching. This strategy addresses the challenge of matching feature points in the region and embedding multiple SAR images in large areas. The goal is to achieve efficient matching processing of multiple SAR images in extensive geographical regions. The proposed multi-GPU collaborative acceleration algorithm is validated through experiments involving feature point extraction and matching using 21 GF-3 SAR images. The results demonstrate the feasibility and efficiency of the algorithm in enhancing the processing speed of matching feature points in large-scale satellite SAR images. Overall, the paper contributes to the advancement of SAR image processing techniques, specifically in feature point extraction and matching in large-scale applications

Application of Principal Component Cluster Analysis in the Quality of Cordyceps Sinensis

International audienceIn this paper, the kinds and contents of amino acids in Cordyceps sinensis from different habitats of Tibet components using principal component analysis and cluster analysis, the principal component cluster analysis to evaluate the nutritional value of different localities, and provide scientific basis for the further research and development and utilization of Cordyceps resources in Tibet area. The principal component analysis method is a method of using the central idea of dimensionality reduction, the analysis method of multi indicators into a multivariate data several comprehensive index of a few statistics. The principal component analysis method can guarantee the minimizing loss of original data information, with less comprehensive variables instead of multiple variables of the original. Cluster analysis can be used to classify samples of multiple variables by using comprehensive information, the classification results are intuitive, clustering dendrogram clearly show the results of numerical classification, clustering analysis results than the traditional classification method is more detailed, comprehensive, reasonable

Fabrication of Magnetic Nanofibers by Needleless Electrospinning from a Self-Assembling Polymer Ferrofluid Cone Array

Magnetic nanofiber has been widely applied in biomedical fields due to its distinctive size, morphology, and properties. We proposed a novel needleless electrospinning method to prepare magnetic nanofibers from the self-assembling “Taylor cones” of poly(vinyl pyrrolidone) (PVP)/Fe3O4 ferrofluid (PFF) under the coincident magnetic and electric fields. The results demonstrated that a static PFF Rosensweig instability with a conical protrusion could be obtained under the magnetic field. The tip of the protrusion emitted an electrospinning jet under the coincident magnetic and electric fields. The needleless electrospinning showed a similar process phenomenon in comparison with conventional electrospinning. The prepared nanofibers were composed of Fe3O4 particles and PVP polymer. The Fe3O4 particles aggregated inside and on the surface of the nanofibers. The nanofibers prepared by needleless electrospinning exhibited similar morphology compared with the conventionally electrospun nanofibers. The nanofibers also exhibited good ferromagnetic and magnetic field responsive properties