The State-of-the-Art Review on Applications of Intrusive Sensing,Image Processing Techniques,and Machine Learning Methods in Pavement Monitoring and Analysis

In modern transportation,pavement is one of the most important civil infrastructures for the movement of vehicles and pedestrians.Pavement service quality and service life are of great importance for civil engineers as they directly affect the regular service for the users.Therefore,monitoring the health status of pavement before irreversible damage occurs is essential for timely maintenance,which in turn ensures public transportation safety.Many pavement damages can be detected and analyzed by monitoring the structure dynamic responses and evaluating road surface conditions.Advanced technologies can be employed for the collection and analysis of such data,including various intrusive sensing techniques,image processing techniques,and machine learning methods.This review summarizes the state-ofthe-art of these three technologies in pavement engineering in recent years and suggests possible developments for future pavement monitoring and analysis based on these approaches.

Wavelet Analysis of Space Solar Telescope Images

The scientific satellite SST (Space Solar Telescope) is an important research project strongly supported by the Chinese Academy of Sciences. Every day, SST acquires 50 GB of data (after processing) but only 10GB can be transmitted to the ground because of limited time of satellite passage and limited channel volume. Therefore, the data must be compressed before transmission. Wavelets analysis is a new technique developed over the last 10 years, with great potential of application. We start with a brief introduction to the essential principles of wavelet analysis, and then describe the main idea of embedded zerotree wavelet coding, used for compressing the SST images. The results show that this coding is adequate for the job.

Example-based super-resolution for single-image analysis from the Chang'e-1 Mission

Due to the low spatial resolution of images taken from the Chang＇e-1 （CE-I） orbiter, the details of the lunar surface are blurred and lost. Considering the limited spatial resolution of image data obtained by a CCD camera on CE-1, an example-based super-resolution （SR） algorithm is employed to obtain high- resolution （HR） images. SR reconstruction is important for the application of image data to increase the resolution of images. In this article, a novel example-based algorithm is proposed to implement SR reconstruction by single-image analysis, and the computational cost is reduced compared to other example-based SR methods. The results show that this method can enhance the resolution of images using SR and recover detailed information about the lunar surface. Thus it can be used for surveying HR terrain and geological features. Moreover, the algorithm is significant for the HR processing of remotely sensed images obtained by other imaging systems.

Machine Learning-based Identification of Contaminated Images in Light Curve Data Preprocessing

Attitude is one of the crucial parameters for space objects and plays a vital role in collision prediction and debris removal.Analyzing light curves to determine attitude is the most commonly used method.In photometric observations,outliers may exist in the obtained light curves due to various reasons.Therefore,preprocessing is required to remove these outliers to obtain high quality light curves.Through statistical analysis,the reasons leading to outliers can be categorized into two main types:first,the brightness of the object significantly increases due to the passage of a star nearby,referred to as“stellar contamination,”and second,the brightness markedly decreases due to cloudy cover,referred to as“cloudy contamination.”The traditional approach of manually inspecting images for contamination is time-consuming and labor-intensive.However,we propose the utilization of machine learning methods as a substitute.Convolutional Neural Networks and SVMs are employed to identify cases of stellar contamination and cloudy contamination,achieving F1 scores of 1.00 and 0.98 on a test set,respectively.We also explore other machine learning methods such as ResNet-18 and Light Gradient Boosting Machine,then conduct comparative analyses of the results.

Lossless Compression Method for the Magnetic and Helioseismic Imager(MHI)Payload

The Solar Polar-orbit Observatory(SPO),proposed by Chinese scientists,is designed to observe the solar polar regions in an unprecedented way with a spacecraft traveling in a large solar inclination angle and a small ellipticity.However,one of the most significant challenges lies in ultra-long-distance data transmission,particularly for the Magnetic and Helioseismic Imager(MHI),which is the most important payload and generates the largest volume of data in SPO.In this paper,we propose a tailored lossless data compression method based on the measurement mode and characteristics of MHI data.The background out of the solar disk is removed to decrease the pixel number of an image under compression.Multiple predictive coding methods are combined to eliminate the redundancy utilizing the correlation(space,spectrum,and polarization)in data set,improving the compression ratio.Experimental results demonstrate that our method achieves an average compression ratio of 3.67.The compression time is also less than the general observation period.The method exhibits strong feasibility and can be easily adapted to MHI.

Assessing the Pilling of Fabric by Image Analysis

In this paper, firstly, the image of pilling was pre - pro-cessed by image analysis technique, which based on the image’s gray- scale statistical characters or mathemati-cal morphology. Then, the pilling of fabric was assessed synthetically by the size of pilling A,number of pilling N and morphology of pitting S. At the end, we tested the practicability of this method by testing knitgoods as a sample, and the result was satisfiable.

Calpain mediated cisplatin-induced ototoxicity in mice

Ototoxic drug-induced apoptosis of inner ear cells has been shown to be associated with calpain expression. Cisplatin has severe ototoxicity, and can induce cochlear cell apoptosis. This study assumed that cisplatin activated calpain expression in apoptotic cochlear cells. A mouse model of cisplatin-induced ototoxicity was established by intraperitoneal injection with cisplatin （2.5, 3.5, 4.5, 5.5 mg/kg）. Immunofluorescence staining, image analysis and western blotting were used to detect the expression of calpain 1 and calpain 2 in the mouse cochlea. At the same time, the auditory brainstem response was measured to observe the change in hearing. Results revealed that after intraperitoneal injection with cisplatin for 5 days, the auditory brainstem response threshold shifts increased in mice. Calpain 1 and calpain 2 expression significantly increased in outer hair cells, the spiral ganglion and stria vascularis. Calpain 2 protein expression markedly increased with an increased dose of cisplatin. Results suggested that calpain 1 and calpain 2 mediated cisplatin-induced ototoxicity in BALB/c mice. During this process, calpain 2 plays a leading role.

Analysis of the geomorphology surrounding the Chang'e-3 landing site

Chang＇e-3 （CE-3） landed on the Mare Imbrium basin in the east part of Sinus Iridum （19.51°W, 44.12°N）, which was China＇s first soft landing on the Moon and it started collecting data on the lunar surface environment. To better understand the environment of this region, this paper utilizes the available high-resolution topography data, image data and geological data to carry out a detailed analysis and research on the area surrounding the landing site （Sinus Iridum and 45 km×70 km of the landing area） as well as on the topography, landform, geology and lunar dust of the area surrounding the landing site. A general topographic analysis of the surrounding area is based on a digital elevation model and digital elevation model data acquired by Chang＇e-2 that have high resolution; the geology analysis is based on lunar geological data published by USGS; the study on topographic factors and distribution of craters and rocks in the surrounding area covering 4km^4km or even smaller is based on images from the CE-3 landing camera and images from the topographic camera; an analysis is done of the effect of the CE-3 engine plume on the lunar surface by comparing images before and after the landing using data from the landing camera. A comprehensive analysis of the results shows that the landing site and its surrounding area are identified as typical lunar mare with flat topography. They are suitable for maneuvers by the rover, and are rich in geological phenomena and scientific targets, making it an ideal site for exploration.

Reconstructing the landing trajectory of the CE-3 lunar probe by using images from the landing camera

An accurate determination of the landing trajectory of Chang＇e-3 （CE-3） is significant for verifying orbital control strategy, optimizing orbital planning, accu- rately determining the landing site of CE-3 and analyzing the geological background of the landing site. Due to complexities involved in the landing process, there are some differences between the planned trajectory and the actual trajectory of CE-3. The land- ing camera on CE-3 recorded a sequence of the landing process with a frequency of 10 frames per second. These images recorded by the landing camera and high-resolution images of the lunar surface are utilized to calculate the position of the probe, so as to reconstruct its precise trajectory. This paper proposes using the method of trajectory reconstruction by Single Image Space Resection to make a detailed study of the hov- ering stage at a height of 100 m above the lunar surface. Analysis of the data shows that the closer CE-3 came to the lunar surface, the higher the spatial resolution of im- ages that were acquired became, and the more accurately the horizontal and vertical position of CE-3 could be determined. The horizontal and vertical accuracies were 7.09 m and 4.27 m respectively during the hovering stage at a height of 100.02 m. The reconstructed trajectory can reflect the change in CE-3＇s position during the powered descent process. A slight movement in CE-3 during the hovering stage is also clearly demonstrated. These results will provide a basis for analysis of orbit control strategy, and it will be conducive to adjustment and optimization of orbit control strategy in follow-up missions.

Automatic removal of false image stars in disk-resolved images of the Cassini Imaging Science Subsystem

Taking a large number of images,the Cassini Imaging Science Subsystem(ISS)has been routinely used in astrometry.In ISS images,disk-resolved objects often lead to false detection of stars that disturb the camera pointing correction.The aim of this study was to develop an automated processing method to remove the false image stars in disk-resolved objects in ISS images.The method included the following steps:extracting edges,segmenting boundary arcs,fitting circles and excluding false image stars.The proposed method was tested using 200 ISS images.Preliminary experimental results show that it can remove the false image stars in more than 95%of ISS images with disk-resolved objects in a fully automatic manner,i.e.,outperforming the traditional circle detection based on Circular Hough Transform(CHT)by 17%.In addition,its speed is more than twice as fast as that of the CHT method.It is also more robust(no manual parameter tuning is needed)when compared with CHT.The proposed method was also applied to a set of ISS images of Rhea to eliminate the mismatch in pointing correction in automatic procedure.Experiment results showed that the precision of final astrometry results can be improve by roughly 2 times that of automatic procedure without the method.It proved that the proposed method is helpful in the astrometry of ISS images in a fully automatic manner.

A new segmentation algorithm for lunar surface terrain based on CCD images

Terrain classification is one of the critical steps used in lunar geomorphologic analysis and landing site selection. Most of the published works have focused on a Digital Elevation Model （DEM） to distinguish different regions of lunar terrain. This paper presents an algorithm that can be applied to lunar CCD images by blocking and clustering according to image features, which can accurately distinguish between lunar highland and lunar mare. The new algorithm, compared with the traditional algo- rithm, can improve classification accuracy. The new algorithm incorporates two new features and one Tamura texture feature. The new features are generating an enhanced image histogram and modeling the properties of light reflection, which can represent the geological characteristics based on CCD gray level images. These features are ap- plied to identify texture in order to perform image clustering and segmentation by a weighted Euclidean distance to distinguish between lunar mare and lunar highlands. The new algorithm has been tested on Chang＇e-1 CCD data and the testing result has been compared with geological data published by the U.S. Geological Survey. The result has shown that the algorithm can effectively distinguish the lunar mare from highlands in CCD images. The overall accuracy of the proposed algorithm is satisfactory, and the Kappa coefficient is 0.802, which is higher than the result of combining the DEM with CCD images.

High-resolution Solar Image Reconstruction Based on Non-rigid Alignment

Suppressing the interference of atmospheric turbulence and obtaining observation data with a high spatial resolution are an issue to be solved urgently for ground observations. One way to solve this problem is to perform a statistical reconstruction of short-exposure speckle images. Combining the rapidity of Shift-Add and the accuracy of speckle masking, this paper proposes a novel reconstruction algorithm-NASIR(Non-rigid Alignment based Solar Image Reconstruction). NASIR reconstructs the phase of the object image at each frequency by building a computational model between geometric distortion and intensity distribution and reconstructs the modulus of the object image on the aligned speckle images by speckle interferometry. We analyzed the performance of NASIR by using the correlation coefficient, power spectrum, and coefficient of variation of intensity profile in processing data obtained by the NVST(1 m New Vacuum Solar Telescope). The reconstruction experiments and analysis results show that the quality of images reconstructed by NASIR is close to speckle masking when the seeing is good, while NASIR has excellent robustness when the seeing condition becomes worse. Furthermore, NASIR reconstructs the entire field of view in parallel in one go, without phase recursion and block-by-block reconstruction, so its computation time is less than half that of speckle masking. Therefore, we consider NASIR is a robust and highquality fast reconstruction method that can serve as an effective tool for data filtering and quick look.

A new lunar absolute control point: established by images from the landing camera on Chang'e-3

The establishment of a lunar control network is one of the core tasks in selenodesy, in which defining an absolute control point on the Moon is the most im- portant step. However, up to now, the number of absolute control points has been very sparse. These absolute control points have mainly been lunar laser ranging retrore- flectors, whose geographical location can be observed by observations on Earth and also identified in high resolution lunar satellite images. The Chang＇e-3 （CE-3） probe successfully landed on the Moon, and its geographical location has been monitored by an observing station on Earth. Since its positional accuracy is expected to reach the meter level, the CE-3 landing site can become a new high precision absolute control point. We use a sequence of images taken from the landing camera, as well as satellite images taken by CE-1 and CE-2, to identify the location of the CE-3 lander. With its geographical location known, the CE-3 landing site can be established as a new abso- lute control point, which will effectively expand the current area of the lunar absolute control network by 22%, and can greatly facilitate future research in the field of lunar surveying and mapping, as well as selenodesy.

An accelerated direct demodulation method for image reconstruction using spherical data from the hard X-ray modulation telescope

The hard X-ray modulation telescope （HXMT） mission is mainly devoted to performing an all-sky survey at 1- 250 keV with both high sensitivity and high spatial resolution. The observed data reduction as well as the image reconstruction for HXMT can be achieved by using the direct demodulation method （DDM）. However the original DDM is too computationally expensive for multi-dimensional data with high resolution to be employed for HXMT data. We propose an accelerated direct demodulation method especially adapted for data from HXMT. Simulations are also presented to demonstrate this method.

Improved astrometry of space debris with image restoration

In order to implement an observing strategy, image degradation that occurs during optical observation of space debris is ineluctable and has distinct characteris- tics. Image restoration is presented as a way to remove the influence of degradation in CCD images of space debris, based on assumed PSF models with the same F-WHM as images of the object. In the process of image restoration, the maximum entropy method is adopted. The results of reduction using observed raw CCD images indi- cate that the precision in estimating positions of objects is improved and the effects of degradation are reduced. Improving the astrometry of space debris using image restoration is effective and feasible.

Contour detection in Cassini ISS images based on Hierarchical Extreme Learning Machine and Dense Conditional Random Field

In Cassini ISS(Imaging Science Subsystem)images,contour detection is often performed on disk-resolved objects to accurately locate their center.Thus,contour detection is a key problem.Traditional edge detection methods,such as Canny and Roberts,often extract the contour with too much interior details and noise.Although the deep convolutional neural network has been applied successfully in many image tasks,such as classification and object detection,it needs more time and computer resources.In this paper,a contour detection algorithm based on H-ELM(Hierarchical Extreme Learning Machine)and Dense CRF(Dense Conditional Random Field)is proposed for Cassini ISS images.The experimental results show that this algorithm’s performance is better than both traditional machine learning methods,such as Support Vector Machine,Extreme Learning Machine and even deep Convolutional Neural Network.The extracted contour is closer to the actual contour.Moreover,it can be trained and tested quickly on the general configuration of PC,and thus can be applied to contour detection for Cassini ISS images.

An improved astrometric calibration technique for space debris observation

An optical survey is the main technique for detecting space debris. Due to the specific character- istics of observation, the pointing errors and tracking errors of the telescope as well as image degradation may be significant, which make it difficult for astrometric calibration. Here we present an improved method that corrects the pointing and tracking errors, and measures the image position precisely. The pipeline is tested on a number of CCD images obtained from a 1-m telescope administered by Xinjiang Astronomical Observatory while observing a GPS satellite. The results show that the position measurement error of the background stars is around 0.1 pixel, while the time cost for a single frame is about 7.5 s; hence the relia- bility and accuracy of our method are demonstrated. In addition, our method shows a versatile and feasible way to perform space debris observation utilizing non-dedicated telescopes, which means more sensors could be involved and the ability to perform surveys could be improved.

Chinese sunspot drawings and their digitization–(Ⅱ)accuracy analysis for digitized sunspot hand-drawing records of Yunnan Observatories

Sunspots are the most striking and easily observed magnetic structures of the Sun,and statistical analysis of solar historical data could reveal a wealth of information on the long-term variation of solar activity cycle.The hand-drawn sunspot records of Yunnan Observatories,Chinese Academy of Sciences have been accumulating for more than 60 years,and nearly 16000 images have been preserved.In the future,the observation mode of recording sunspots by hand-drawing will be replaced inevitably by digital images observed either at ground or in space.To connect the hand-drawn sunspot data and the purely digital sunspot data in future,it is necessary to analyze the systematic errors of the data which are observed by the two observation modes in the period of transition.In this paper,we choose 268 round sunspots(Htype in modified Zurich sunspot classification)from the drawing of Yunnan Observatories to compare their positions and areas with the CCD observations made by Helioseismic and Magnetic Imager(HMI)on board Solar Dynamic Observatory(SDO)and Global Oscillation Network Group(GONG).We find that the latitude and longitude accuracy of hand-drawn sunspot are within-0.127 and 2.29 degree respectively,and the area accuracy is about 16.36 sunspot unit(μHem).Systematic errors apparently decrease with large sunspot.

Automated classification technique for edge-on galaxies based on mathematical treatment of brightness data

Classification of edge-on galaxies is important to astronomical studies due to our Milky Way galaxy being an edge-on galaxy.Edge-on galaxies pose a problem to classification due to their less overall brightness levels and smaller numbers of pixels.In the current work,a novel technique for the classification of edge-on galaxies has been developed.This technique is based on the mathematical treatment of galaxy brightness data from their images.A special treatment for galaxies’brightness data is developed to enhance faint galaxies and eliminate adverse effects of high brightness backgrounds as well as adverse effects of background bright stars.A novel slimness weighting factor is developed to classify edge-on galaxies based on their slimness.The technique has the capacity to be optimized for different catalogs with different brightness levels.In the current work,the developed technique is optimized for the EFIGI catalog and is trained using a set of 1800 galaxies from this catalog.Upon classification of the full set of 4458 galaxies from the EFIGI catalog,an accuracy of 97.5% has been achieved,with an average processing time of about 0.26 seconds per galaxy on an average laptop.