Search

Noticeable striping artifacts in collected satellite imagery, caused by the inconsistency of pushbroom sensor detector responses, degrade the image quality. Relative radiometric calibration aims to calibrate inconsistencies in terms of detector responses, thereby eliminating detector-level striping artifacts. Yaw calibration has become the preferred imagery-based calibration method for satellites without on-board calibration equipment, owing to its high accuracy and convenience. However, it often relies on a large uniform field on the Earth's surface and is a linear calibration method. This method does not consider acquisition-related geometric errors associated with the sensor, resulting in reduced calibration accuracy. In this study, we propose an improved method for yaw calibration, which accounts for the geometric distortion of sensor imaging and does not require a uniform field. A fast geometric-positioning algorithm was used to remove geometric distortion, followed by high-precision extraction of the calibration reference for each sensor's detector. The dynamic range of the sensor calibration was extended, followed by the calibration of the sensor nonlinear response model. Our results based on Yaogan-25 images suggest the following: 1) the improved method effectively eliminates the ``sawtooth'' caused by the yaw calibration method without considering the geometric distortion and 2) it outperforms other imagery-based calibration methods with respect to visual destriping effects such that the root-mean-square deviations of the corrected imagery experienced a decrease of 0.65 percentage point, compared with that of the statistical method, and 0.17 percentage point for the yaw calibration. Such improvements will promote high-quality applications of remote-sensing images.

Rolling element bearings are crucial, of high failure rate and easy damage parts of rotating machinery, and significantly affect safe and reliable production processes. Much more attention has been focused on fault diagnosis of rolling element bearings in recent years. This article presents a novel feature extraction scheme for the classification of multiple bearing faults. Multi-Scale Sample Entropy (M-SSampEn) is combined with Energy Moment (EM) to construct a time-domain Multi-Scale Sample Entropy-based Energy Moment (M-SSampEn-EM) feature extractor. The classifier model for the proposed fault classification system has been built using the Least Square Support Vector Machine (LS-SVM). The M-SSampEn-EM feature extractor is used to capture two-dimensional representative eigenvectors from multiple fault classes' bearing vibration data. Its separability performance is ensured by optimizing the feature-extraction parameters, including the Embedded Dimension and the Tolerance, etc. The LS-SVM classifier is also compared with two Neural Network (NN)-based classifiers, i.e., Radial Basis Function NN (RBFNN) and Probabilistic NN (PNN), to show it better generalization performance on bearing fault classification. The experimental study verifies the excellent capacity of the proposed approach in bearing fault classification.

Sensor instability, dark currents, and other factors often cause stripe noise corruption in hyperspectral remote sensing images and severely limit their application in practical purposes. Previous studies have proposed numerous destriping algorithms that have yielded impressive results. Although most destriping algorithms are based on the premise of additive noise, a few studies have focused directly on multiplicative stripe noise. This article fully analyzes the characteristics of the stripe noise of OHS-01 images and proposes a multiplicative stripe noise removal method. Specifically, stripe noise is tackled by performing radiometric normalization of different columns in the image. First, the relative gain coefficients of adjacent columns are separated based on prior knowledge. Second, the local relative intensity correspondence of the image columns are established by means of intensity propagation, intensity connection, and so on. Finally, the above-mentioned process is iterated in multiscale space, and the accumulated gain correction coefficient maps were used to correct the radiation of the original image. The results of extensive experiments on simulated and real remote sensing image data demonstrate that the proposed method can, in most cases, yield desirable results. In certain cases, the results are even better, visually, and quantitatively, than those obtained using classical algorithms. Moreover, the proposed method has high robustness and efficiency. Thus, it can conform to the requirements of engineering applications.

Mental fatigue is often associated with decreased mental alertness and worsening performances. But its detection method is still a difficult issue due to the contradiction between practicability and accuracy. In the current study, we attempt to provide a new method for mental fatigue detection to realize the unity of practicability and accuracy based on deep learning and respiratory signal. To this end, respiratory signals were collected and two deep learning models, convolutional neural network (CNN) and Long Short Term Memory (LSTM), were constructed. Wavelet scale maps and time series of respiratory signal were as input to CNN and LSTM respectively. The data set was divided into training set, verification set and test set according to the ratio of 6:2:2. Bayesian optimization was used for hyperparametric optimization of CNN and LSTM. The results showed that LSTM model has a better performance of test accuracy of 89.16% than CNN that of 77.29%. Our findings indicated that respiratory signal combined with LSTM classifier may be a practical and effective strategy for mental fatigue detection.

Brain fatigue is often associated with inattention, mental retardation, prolonged reaction time, decreased work efficiency, increased error rate, and other problems. In addition to the accumulation of fatigue, brain fatigue has become one of the important factors that harm our mental health. Therefore, it is of great significance to explore the practical and accurate brain fatigue detection method, especially for quantitative brain fatigue evaluation. In this study, a biomedical signal of ballistocardiogram (BCG), which does not require direct contact with human body, was collected by optical fiber sensor cushion during the whole process of cognitive tasks for 20 subjects. The heart rate variability (HRV) was calculated based on BCG signal. Machine learning classification model was built based on random forest to quantify and recognize brain fatigue. The results showed that: Firstly, the heart rate obtained from BCG signal was consistent with the result displayed by the medical equipment, and the absolute difference was less than 3 beats/min, and the mean error is 1.30 ± 0.81 beats/min, secondly, the random forest classifier for brain fatigue evaluation based on HRV can effectively identify the state of brain fatigue, with an accuracy rate of 96.54%, finally, the correlation between HRV and the accuracy was analyzed, and the correlation coefficient was as high as 0.98, which indicates that the accuracy can be used as an indicator for quantitative brain fatigue evaluation during the whole task. The results suggested that the brain fatigue quantification evaluation method based on the optical fiber sensor cushion and machine learning can carry out real-time brain fatigue detection on the human brain without disturbance, reduce the risk of human accidents in human–machine interaction systems, and improve mental health among the office and driving personnel.

Mental fatigue has serious negative impacts on the brain cognitive functions and has been widely explored by the means of brain functional networks with the neuroimaging technique of electroencephalogram (EEG). Recently, several researchers reported that brain functional network constructed from EEG signals has fractal feature, raising an important question: what are the effects of mental fatigue on the fractal dimension of brain functional network? In the present study, the EEG data of alpha1 rhythm (8-10 Hz) at task state obtained by a mental fatigue model were chosen to construct brain functional networks. A modified greedy colouring algorithm was proposed for fractal dimension calculation in both binary and weighted brain functional networks. The results indicate that brain functional networks still maintain fractal structures even when the brain is at fatigue state; fractal dimension presented an increasing trend along with the deepening of mental fatigue fractal dimension of the weighted network was more sensitive to mental fatigue than that of binary network. Our current results suggested that mental fatigue has great regular impacts on the fractal dimension in both binary and weighted brain functional networks.

Geometric calibration can eliminate systematic errors in the imaging parameters to improve the positioning accuracy. The conventional method depends on high-accuracy control data to detect and eliminate systematic errors; therefore, it requires a satellite to scan the calibration fields before carrying out geometric calibration, which reduces its timeliness. This paper presents an interior self-calibration method to eliminate CCD distortions without using high-accuracy control data on the calibration fields, in contrast to the conventional calibration method. Instead, only the 90-m digital elevation data from the global shuttle radar topography mission (90m-SRTM) are used. Extraordinarily, the calibration accuracy of this method is not restricted by the inadequate accuracy of 90m-SRTM, and very promising accuracy comparable to that of the conventional calibration method is achieved. In the experiment, multispectral images obtained by the ZY02C satellite were used, and the results show that the proposed method can eliminate the high-order distortion without the use of high-accuracy control data but only with the 90m-SRTM. Moreover, the internal accuracies of the calibrated images are better than 0.8 pixels. Compared to the conventional method using high-accuracy control data for calibration, the difference between the two methods is only about 0.02 pixels, which verifies the effectiveness of this method.

Traditional methods for constructing brain functional network often need to artificially set a certain threshold, which requires professional and technical personnel to do this work. In order to overcome this deficiency, this study proposed a new method that can automatically construct brain functional network from electroencephalogram (EEG) data, based on positional relations among the vertices and network motif theories. To verify this method, resting state and task state EEG data were converted into brain functional networks with both the new method and traditional methods to explore the discrepancies of network features. The results showed that the mean physical distance increased with the increasing of network edges, evidently suggesting that higher weights of the edges have shorter physical distances, which is the direct model foundation. Besides, consistent results of network features were obtained among these methods, especially in weighted networks, indicating that this new method had the same capacity in accurately characterizing network features compared with the traditional methods. Moreover, this new method can efficiently distinguish the networks that have big differences in the weights, if the network has higher weights, the corresponding network would have more edges, which is in line with one of the traditional methods that using a threshold of weight. We also applied this model in mental fatigue detection, and the results of network characteristics, which obtained from the model and traditional method, have the same variation tendency, approximate values, and similar statistical differences, demonstrating that the proposed model can replace the traditional methods in differentiating similar brain functional states. The new method have potential applications in real-time brain functional networks construction.

Ground control points (GCPs) are generally used to calibrate the installation between the camera and star sensor of a satellite in orbit and improve the geometric positioning accuracy of the satellite. However, the use of GCPs for high-frequency calibration is difficult, and it is particularly difficult to acquire accurate GCPs for the image of a nightlight satellite. In this study, we developed a camera-star sensor installation calibration method that eliminates the need for GCPs. In the proposed method, the camera and star sensor lenses are simultaneously pointed at the star, and the camera-star sensor installation is accurately calibrated by processing the star map obtained by the camera and star sensors. Reference data such as road network and Moon position data were used to verify the proposed method and evaluate its positioning accuracy. The results of the application of the method to the positioning of the Luojia 1-01 satellite indicated an accuracy within 800 m, which is comparable with that of the traditional method.

As the geometric resolution requirements of hyperspectral imaging systems become higher and higher, insufficient energy becomes more of a problem. Motion compensation using "delayed imaging" is a common design method but also brings out some imaging quality and ground-processing problems. This paper described the system design, onboard measuring device and processing techniques of motion compensation for the high-resolution hyperspectral imaging satellite.

During the 5 years following the launch of CBERS-02B in September 2007, China launched a sequence of high-resolution optical satellites that are able to acquire high-resolution images globally. However, most of the satellites launched before 2012 have such poor geometric accuracy, due to both the low technological level of the hardware and design defects, so that their application (e.g., to surveys of land resources) is greatly restricted. Thus, they cannot meet national demands for high-resolution optical images with good geometric accuracy. Following the launch of ZiYuan3-01 (ZY3-01) on January 9, 2012, the retrieval of its images has demonstrated the excellent geometric accuracy that has led Chinese high-resolution optical satellites (CHROSs) to attract much attention from international researchers. As CHROSs suffer from more complex errors than international commercial satellites, such as SPOT and IKONOS, conventional processes widely used in the international field to improve accuracy cannot be applied to them. This paper focuses on the in-orbit active CHROSs and proposes a series of methods for improvement of geometric accuracy. Using high-accuracy ground control data collected from different areas, the accuracy of CHROSs following the improvement measures is assessed. The results indicate that good geometric accuracy could be achieved using the proposed methods, given the conditions of both the low level of the hardware technology and the inherent design defects of CHROSs.

A rational function model (RFM) can be used directly to convert the relationships between image coordinates and object space coordinates without using any physical imaging parameters (such as satellite position and attitude). Thus, RFMs facilitate versatility and high security during geometric processing of optical satellite imagery. Increasingly, RFMs are offered to users as the basic geolocation model for further geometric processing by imagery vendors. However, imagery vendors might perform inadequate in-orbit geometric calibrations, or the calibrated geometric parameters might not be updated in a timely manner. Thus, the RFMs may suffer from high distortion due mainly to interior errors (such as lens distortion). Using the radiometric correction products of Ziyuan1-02C panchromatic and multispectral sensor as examples, the present study addresses the compensation of systematic errors in RFMs. An undistorted RFM can be generated after calibrating the interior error compensation model once, before high-accuracy registration between the panchromatic imagery and multispectral imagery can be achieved using the undistorted RFM. Experimental evaluations based on the positioning accuracy using a few ground control points (GCPs) with an undistorted RFM matched the accuracy of the GCPs. In addition, our approach greatly improves the accuracy of registration (which surpasses 0.7 panchromatic pixels) between panchromatic and multispectral imagery.

At present, without ground control points (GCPs), the positioning accuracy of remote sensing images often fails to meet the growing requirements for mapping accuracy. Multi-load synergy to improve accuracy without GCPs by eliminating the impact of stereo accuracy, which is caused by on-orbit measurement error, is urgently needed to improve large-scale mapping. In this study, we analyzed error sources in stereo imaging mode and found that vertical accuracy depends on the relative accuracy of attitude during symmetric stereoscopic mapping. With the assistance of small matrix charge-coupled device (CCD) images and the block adjustment method, relative accuracy of attitude was improved, allowing for the improvement in vertical accuracy without GCPs. The simulation results show that vertical accuracy in symmetric stereo mode is not affected by attitude system error. After the restoration of imaging attitude processed by a sequence of matrix CCD images, the relative accuracy of the attitude increased, and the accuracy of the elevation without GCPs improved significantly. The results demonstrate the feasibility of small matrix CCD-assisted stereo mapping.

Vibratory signals from a machine, under different faulty conditions, often show themselves different geometric structures, which is the basis of applying principle of fractal geometry to its health condition monitoring and fault diagnosis. An approach for fractal feature extraction from vibratory signals of rolling bearings was studied in this paper. We judged the existence of an early-stage inner race defect by analyzing the trend-changing characteristics on all fractal feature parameters synthetically, determined the window time when the early-stage inner race defect occurs by means of the change points of maximum linearity-mean values on fractal characteristic curves, and made a final diagnosis decision by quantitatively evaluating symptom likelihood on the fractal feature parameters. The results from a case study based on the test-to-failure data showed that the propose approach in this paper is effective and of great application potential to early-stage health condition monitoring and fault diagnosis of rolling bearings.

In the vibration signals of a faulty gearbox, there exist not only the response excited by source of faults such as rotating frequencies of shafts, mesh frequencies of gear pairs, their higher harmonic components and modulated components, but also natural and random vibrations of system, components and parts, and noise interferences. Thus it is difficult to recognize and capture typical fault frequencies. For this purpose, an approach was proposed for fault diagnosis of gearboxes, by combining temporal feature extraction based on independent component analysis (ICA) and fault classification based on forward least squares approximation support vector machine (FLSA-SVM). Multiple ICA neural networks were batch trained by multi-classes vibration data separately, each corresponding to one of fault patterns of gearboxes. A feature extractor was structurally constructed by the trained ICA networks, followed by a post-processing unit of feature extraction based on one second-order measure, i.e. residual total correlation (RTC). Experimental results show that the proposed approach is efficient and effective for fault diagnosis of gearboxes, even if the used train sets are of small size. The integrated ICA-RTC feature extractor is able to extract low-dimensional representative features from time-domain data, and to overcome some difficulties in traditional frequency-domain feature extraction.

Vibratory signals from a machine, under different faulty conditions, often show themselves different geometric structures, which is the basis of applying principle of fractal geometry to its health condition monitoring and fault diagnosis. An approach for fractal feature extraction from vibratory signals of rolling bearings was studied in this paper. We judged the existence of an early-stage inner race defect by analyzing the trend-changing characteristics on all fractal feature parameters synthetically, determined the window time when the early-stage inner race defect occurs by means of the change points of maximum linearity-mean values on fractal characteristic curves, and made a final diagnosis decision by quantitatively evaluating symptom likelihood on the fractal feature parameters. The results from a case study based on the test-to-failure data showed that the propose approach in this paper is effective and of great application potential to early-stage health condition monitoring and fault diagnosis of rolling bearings.

ZiYuan3-02 (ZY3-02) is the first remote sensing satellite for the development of China’s civil space infrastructure (CCSI) and the second satellite in the ZiYuan3 series; it was launched successfully on 30 May 2016, aboard the CZ-4B rocket at the Taiyuan Satellite Launch Center (TSLC) in China. Core payloads of ZY3-02 include a triple linear array camera (TLC) and a multi-spectral camera, and this equipment will be used to acquire space geographic information with high-resolution and stereoscopic observations. Geometric quality is a key factor that affects the performance and potential of satellite imagery. For the purpose of evaluating comprehensively the geometric potential of ZY3-02, this paper introduces the method used for geometric calibration of the TLC onboard the satellite and a model for sensor corrected (SC) products that serve as basic products delivered to users. Evaluation work was conducted by making a full assessment of the geometric performance. Furthermore, images of six regions and corresponding reference data were collected to implement the geometric calibration technique and evaluate the resulting geometric accuracy. Experimental results showed that the direct location performance and internal accuracy of SC products increased remarkably after calibration, and the planimetric and vertical accuracies with relatively few ground control points (GCPs) were demonstrated to be better than 2.5 m and 2 m, respectively. Additionally, the derived digital surface model (DSM) accuracy was better than 3 m (RMSE) for flat terrain and 5 m (RMSE) for mountainous terrain. However, given that several variations such as changes in the thermal environment can alter the camera’s installation angle, geometric performance will vary with the geographical location and imaging time changes. Generally, ZY3-02 can be used for 1:50,000 stereo mapping and can produce (and update) larger-scale basic geographic information products.

Ziyuan1-02C (ZY1-02C) was launched on December 22, 2011, and it is the first civilian high-resolution remote sensing satellite in China. However, the limited precision of the onboard attitude measurement system causes many errors during attitude transfer by ZY1-02C. Thus, there are complex distortions in the images obtained by ZY1-02C, which restricts its application greatly. In this paper, we consider the feasibility of attitude error correction based on parallel observations with high-resolution cameras, and the method is described in detail. To validate the efficiency of the proposed method, several images and corresponding control data were collected from the Henan, Taihang Mountain, Neimeng, and Taiyuan areas in China. The experimental results indicate that seamless mosaic images without distortion can be obtained using our method. Furthermore, the positioning accuracy with a few ground control points (GCPs) was shown to be better than 1.5 pixels and equivalent to the accuracy of the GCPs.

The ZiYuan-3 surveying satellite (ZY-3) is a high-precision civilian satellite imaging sensor. Since its launch on January 9, 2012, it has been in operation for one and a half years. Although the initial postlaunch ZY-3 geometric accuracy was verified during an in-orbit operation period, on-orbit calibration was still necessary from time to time. This on-orbit calibration has vastly improved the location accuracy in planimetry for ZY-3 panchromatic images. This letter briefly describes the principle of on-orbit calibration and production processes of sensor-corrected products. Furthermore, block adjustment based on a rational function model test showed planimetric and vertical accuracy values of 10 m and 5 m, respectively, without ground control points (GCPs). The accuracy values improved to 3 m and 2 m, respectively, with a few GCPs. The statistics results are from ten different regions with independent checkpoints (ICPs). All accuracy values are the root-mean-square error of ICPs. Therefore, ZY-3 can be used for the generation of cartographic maps at the 1 : 50 000 scale and for revision and updates of 1 : 25 000 scale maps. Compared with other mainstream high-resolution satellite images of the same ground resolution, ZY-3's geometric accuracy is almost the same and sometimes even better.

The second batch of Zhuhai-1 microsatellites was successfully launched on 26 April 2018. The batch included four Orbita hyperspectral satellites (referred to as OHS-A, OHS-B, OHS-C, and OHS-D) and one video satellite (OVS-2A), which have excellent hyperspectral data acquisition abilities. For the first time in China, a number of hyperspectral satellite networks have been realized. To ensure the application of hyperspectral remote sensing data, a series of on-orbit geometry processing and accuracy verification studies has been carried out on the &ldquo, Zhuhai-1&rdquo, hyperspectral camera since the satellite was launched. This paper presents the geometric processing methods involved in the production of Zhuhai-1 hyperspectral satellite basic products, including geometric calibration and basic product production algorithms. The OHS images were used to perform on-orbit geometric calibration, and the calibration accuracy was better than 0.5 pixels. The registration accuracy of the image spectrum of the basic product after calibration, the single orientation accuracy, and the accuracy of the regional network adjustment were evaluated. The spectral registration accuracy of the OHS basic products is 0.3&ndash, 0.5 pixels, which is equivalent to the spectral band calibration accuracy. The single orientation accuracy is better than 1.5 pixels and the regional network adjustment accuracy is better than 1.2 pixels. The generated area orthoimages meet the seamless edge requirements, which verifies that the OHS basic product image has good regional mapping capabilities and can meet the application requirements.

ZiYuan-3 (ZY-3) surveying satellite is China's first civilian high-resolution stereo mapping satellite. The satellite's objective is oriented towards plotting 1:50,000 and 1:25,000 topographic maps. Compared with plotting using foreign commercial mapping satellite imagery, establishment of the image geometry model is the core technical problem for different products and various applications of ZY-3 surveying satellite. This article analyses key problems of high-precision geometry processing based on the overall design, and proposes ZY-3 surveying satellite image geometry model with the technology of virtual CCD line-array imaging. In addition, this article utilises the first orbit imagery of ZY-3 satellite with coverage of Dalian region in Liaoning Province, and produces forward, backward and nadir camera calibrated products. Different ground control points are selected for the block adjustment experiment and the digital surface model (DSM), digital ortho-map (DOM) are generated. The accuracy is validated...

The Luojia 1-01 Satellite (LJ1-01) is the first professional night-light remote-sensing satellite in China, and thus, it is of pioneering significance for the development of night-light remote sensing satellites in China and the application of remote sensing in the social and economic fields. To ensure the application of night-light remote-sensing data, several studies concerning on-orbit geometric calibration and accuracy verification have been carried out for the complementary metal oxide semiconductor (CMOS) rolling shutter camera of LJ1-01 since the launch of the satellite. Owing to the lack of high-precision nightlight geometric reference at home and abroad, it is difficult to directly calibrate the nighttime light image of LJ1-01. Based on the principle of rolling shutter dynamic imaging, a rigorous geometric imaging model of the time-sharing exposure of the rolling shutter of LJ1-01 is established, and a geometric calibration method for daytime imaging calibration and compensated nighttime light data is proposed. The global public Landsat digital orthophoto image (DOM) with a 15-m resolution and 90-m Shuttle Radar Topography Mission digital elevation model (SRTM-DEM) are used as control data. The images obtained in England, Venezuela, Caracas, Damascus, and Torreon (Mexico) were selected as experimental data. The on-orbit calibration and accuracy verification of LJ1-01 were carried out. Experiments show that after on-orbit geometric calibration, the daytime calibration parameters can effectively compensate for the systematic errors of night-light images. After compensation, the positioning accuracy of night-light images without geometric control points (GCPs) is improved from nearly 20 km to less than 0.65 km. The internal accuracy of the calibrated night-light images is better than 0.3 pixels, which satisfies the requirement of subsequent applications.

The inner calibration technology of spaceborne SAR, as an effective way of eliminating the error deduced from inaccurate SAR positioning parameters to improve positioning accuracy, has been studied worldwide. Based on the imaging characteristics of spaceborne SAR and its rigorous sensor model, this paper constructs the rigorous geometric model for SAR inner calibration, and then proposes the inner calibrating algorithm. CRS1 images over Inner Mongol are used as test data. The positioning accuracy of the model is improved a lot after inner calibration which verifies the feasibility and accuracy of the algorithm.

Uniform circular array(UCA) based on coupled oscillators is introduced for new phased array. A phase model of the array is proposed and a set of nonlinear phase differential equations are studied. Linear phase distribution among UCA is achieved using York's method for coupled oscillators array, causing error in beam scaning of coupled oscillators UCA, and the inter-element phase shift that can be varied only over the range (−30°, 30°). A new method to establish nonlinear phase distribution among coupled oscillators UCA is introduced which eliminate phase shifter. It is shown that arbitrary phase difference over the range (−90°, 90°) can be established by detuning the free-running frequency of each element, while maintaining mutual synchronization. This method can be used for electronic beam scanning in UCA based on coupled oscillators.

A DDS-driven PLL frequency synthesizer architecture is given in this paper. The AD9854 DDS device is used to generate low frequency reference signal and to drive the PLL and VCO to generate RF signal. Experiments and measurement results showed that this frequency synthesizer had wider bandwidth and low phase noise level, and can be used to generate X-band continuous wave signal, FM chirp signal and frequency agile signal for radar signal simulation.

Frequency agile radar synthesis detection unit works in frequency hopping state in radio frequency (RF) band. For the limitation of the accuracy of instantaneous frequency measurement (IFM) technology, the frequency tracking accuracy can't be examined directly. The system design method proposed by the paper examines the frequency tracking accuracy of the frequency agile radar synthesis detection unit (FARSDU) at intermediate frequency. The method is realized in engineering and good performance has been got.