Hyperspectral imaging (HSI) acquires a three-dimensional dataset called hypercube, with two spatial dimensions and one spectral dimension. HSI is an emerging imaging modality for various medical applications, especially in disease diagnosis of early and biomedical research based on their unique spectral signatures. A visible-near-infrared HSI for microspectroscopy is designed, the measured spectrum is from 450 nm to 900 nm, which is sampled by 256 spectral channels. The spatial resolution is 1.25μm.The modulation transfer function (MTF) value of full spectrum and full field of view is close to the diffraction limit. At last, this microspecroscopy have been fabricated and preliminary tests have been implemented. The results indicated this visible-near-infrared hyperspectral microscopy optical systems have excellent optical performances. This hyperspectral microscopy will be well developed and used in the life sciences fields.
Target detection of hyperspectral image has always been a hot research topic, especially due to its important applications in military and civilian remote sensing. This paper employs the idea of classification and proposes a novel detection framework which incorporates dictionary learning and discriminative information. Due to the fact that target pixels lie in different subspace with background pixels, a novel detection model is proposed. In addition, a linear kernel is applied to project the image data into high-dimensional space, separating the target pixels and background pixels. Synthetic image and popular real hyperspectral image are used to evaluate our algorithm. Experimental results indicate that our proposed detector outperforms the traditional detection methods.
Low-light stereo vision is a challenging problem because images captured in dark environment usually suffer from strong random noises. Some widely adopted algorithms, such as semiglobal matching, mainly depend on pixel-level information. The accuracy of local feature matching and disparity propagation decreases when pixels become noisy. Focusing on this problem, we proposed a matching algorithm that utilizes regional information to enhance the robustness to local noisy pixels. This algorithm is based on the framework of ADCensus feature and semiglobal matching. It extends the original algorithm in two ways. First, image segmentation information is added to solve the problem of incomplete path and improve the accuracy of cost calculation. Second, the matching cost volume is calculated with AD-SoftCensus measure that minimizes the impact of noise by changing the pattern of the census descriptor from binary to trinary. The robustness of the proposed algorithm is validated on Middlebury datasets, synthetic data, and real world data captured by a low-light camera in darkness. The results show that the proposed algorithm has better performance and higher matching rate among top-ranked algorithms on low signal-to-noise ratio data and high accuracy on the Middlebury benchmark datasets.
The influence of adhesive bonding and curing on the accuracy of mirror surface shape was analyzed to realize low-stress assembly of large aperture mirror. Firstly, based on Hooke's law, a curing shrinkage stress equation was deduced, taking deformation of the mirror and support structure into account under the boundary condition of continuous edge bond, and key parameters effecting mirror deformation were obtained. Secondly, for a 514mm ULE spectrometer primary mirror with an inserts structure mosaiced and bonded on mirror-back, an equivalent linear expansion coefficient method was used for finite element modeling. The shrinkage stress at the bond edge of mirror and the mirror surface shape were analyzed. It’s found that adhesive shrinkage has a significant effect on the mirror surface shape. Finally, the inserts structure of mirror assembly was optimized. In contrast to the non-optimum structure, the average stress of adhesive surface caused by adhesive curing shrinkage reduced from 0.28MPa to 0.18MPa, and the mirror surface shape (Root Mean Square, RMS) reduced from 0.029λ to 0.017λ. Finite element analysis results of the mirror assembly were given at last, surface shape accuracy (RMS) of mirror is 0.012λ under a load case of 1g gravity, and the first-order natural frequency of the component is 216 Hz. The obtained results showed that a suitable optimized support structure can effectively relieve adhesive curing stress, and also satisfy the design requirements for both the static and dynamic stiffness.
In case of light absorption and diffusion, the clarity of the underwater images are degraded. The color of the underwater image is distortion. In order to improve the quality of the original underwater image quality, a method based on RGB channels histogram equalization is proposed. The method improves the contrast of the underwater image based on strength the histogram of the RGB channels respectively. The real-world experiment confirms the effectiveness of the method.
Small anomaly detection in ocean evironment is an important problem in airborne remote sensing image processing, especially in hyperspectral data. Traditional algorithms solve this problem by finding the pixels have different appearance pattern with the background. However, these algorithm are not suitable for real-time applications. In this paper, we propose to learn the hyperspectral model of the seawater and localize the targets whose spectral feature do not well fit the trained model. This algorithm only uses historical information and is suitable to be used on airborne line-scanning data. Since hyperspectral property of ocean water is relatively stable, we use Gaussian mixture model to encode the statistical features of the background. Experimental results demonstrated that the proposed algorithm significantly improves processing efficiency in comparison with conventional methods, and maintains high accuracy with regard to other methods.
High concentration of nitrate will cause many problems, such as water eutrophication and compromise of human beings’ health. A fast and stable approach was applied to predict nitrate concentration in solutions using the dual optical active correction continuous spectrum analyzer designed by our research group. Firstly, standard normal variate (SNV) was used to pretreat the spectral data. Then characteristic wavelengths of spectral curve were selected by using successive projections algorithm (SPA) and genetic algorithm (GA) respectively. Finally, partial least-squares regression (PLSR) was applied to build the spectral prediction model to predict nitrate concentration, and coefficient of determination (R<sup>2</sup>) and root mean square error of prediction (RMSEP) were introduced as the evaluation indicators of prediction models. For SNV-GA-PLS model, R<sup>2</sup>=0.9966 and RMSEP=0.1712, which outperformed than SNV-UVE-SPA-PLS model (R<sup>2</sup>=0.9896, RMSEP=0.3952). It demonstrated that he model which selects spectral characteristic wavelengths by GA can decrease the complexity of prediction model building and ensure the accuracy as well. Hence, SNV-GA-PLS model can be used to predict nitrate concentration in water with quick and steady performance.
The study of underwater in-situ detection is an important research trend in underwater detection. In view of the design requirements of the spectral data acquisition system for underwater in-situ detection, this paper used the software and hardware co-design method, from two aspects of software and hardware. A prototype system of spectral data acquisition system based on Xilinx Zynq chip and linear array CCD detector was designed and implemented. Through theoretical analyzing, experimental debugging and verification analyzing, the results shown that the system could collect and store the spectral data in real time. It also had the characteristics of low noise and had a small electronic structure, which laid a foundation for the spectral data acquisition of underwater in-situ detection.
Mineral pigments are widely used in the ancient Chinese painting. Classification and identification of mineral pigments are important for cultural heritages conservation. As a non-destructive method, hyperspectral classification is based on the knowledge that different mineral pigments have distinct reflection spectra. This study acquired the hyperspectral images of 38 mineral pigments and established a reflection spectral library. Then Spectral Angle Mapper (SAM) was used to classify the test data and 0.20 was selected as the optimal threshold. For pigments having similar color and spectra, SAM was unable to classify them correctly. Therefore, decision tree, a machine learning method, was applied to the classification of the pigments misclassified by SAM. For each pigment, 7500 samples were randomly selected as training data and 2500 samples were selected as test data. Though the ID3 algorithm, a decision tree for pigment classification was learned. Then test data was classified by the decision tree. Compared with SAM, the accuracy of classification observed from decision tree was obviously improved. For most pigments, the accuracy of decision tree reached 94%. The results revealed that the SAM combined with decision tree could effectively achieve a discrimination of all the 38 mineral pigments in the experiment, thus providing a new approach for mineral pigments classification.
With the development of computer vision and image processing technology, vision measurement has been paid more and more attention. In the aviation field, estimating the relative attitude of aircraft using computer vision is important in aircraft flight-refueling, target tracking and positioning. However, the existing methods to measure the attitude of aircraft have some problems. In this paper, we propose to use binocular vision measurement method to acquire the attitude data of aircraft. This method has the advantages of simple realization and high practical value, which can also be widely used in visional measurement applications.
Target detection and tracking important in many applications including intelligent monitoring system, defense system and terminal guidance system. Aiming to solve the problem of simulated target tracking, this paper proposes an adaptive algorithm which uses the fusion of the spectral and morphological features of multispectral image to realize the target tracking based on the Particle Filter. Firstly, the target area is manually initialized in the multispectral image and the spectral and texture features of the target are extracted. Secondly, we build the adaptive tracking model of multiple features under the framework of Particle Filter. We validate the effectiveness of the proposed approach on the MATLAB platform. The results show that the proposed approach achieves accurate and stable multispectral target tracking in complex scenes by improving the efficiency of particles usage under defective tracking conditions, which is of great theoretical and practical values for the application of multispectral target tracking technology.
A newly developed real-time infrared signal processing system based on the heterogeneous multi-processor system on chip (MPSoC) is proposed in this paper. The architecture, hardware configuration, image pre-processing algorithms used in the system and the experimental result are presented. Compared to the infrared signal processing system in being, Xilinx Zynq-7000 All Programmable SoC has been used in the proposed system which is more portable, integrated, and has excellent performance during its signal processing.
Haze always exists in hyper-spectral remote sensing imagery, and it is a key reason that influences the effective information extraction of hyper-spectral images. Specially, when the faint haze covers part of the target in remote sensing images, the target still can be detected but not clear. So, how to remove the influence of the haze and improve the applicable efficiency of hyper-spectral images is a popular research point. This paper proposes a dehazing method for hyper-spectral images based on linear unmixing. First, a popular hyper-spectral unmixing method called FUN is used to get the signature of all the end-members and their corresponding abundance. And then, the abundance of the haze end-member is removed and the abundances of the rest end-members are adjusted to satisfy the sum-to-one and non-negative constraint. Lastly, the new abundance and the signature of the end-members are linearly mixed to get the dehazed hyper-spectral images. The experiment result shows that the dehazed hyper-spectral images exhibit better target information and details. The method is effective and available.
In order to realize the effective detection of surface structure targets in hyperspectral images, an improved target detection algorithm was proposed in this paper presents to solve the CEM algorithm problems which the large object extraction efficiency is low .First, the image was preprocessed, including end-member extraction, SAM classification. Second, after the ship pixels were subtracted from all pixels, the correlation matrix of pure background pixels was constructed to detect ship target. Third, the biggest write region was found as sea region by mathematical morphology. Finally, the false target pixels were removed from all target pixels using the characteristics which ship targets were surrounded in seawater, so the final ship targets were selected in the end. Experimental results show that the final max ratio between the energy of detection target and the energy of background increased greatly, the target signal is enhanced and the background signal is suppressed by the improved algorithm.
The High Energy cosmic-Radiation Detection (HERD) facility is one of several space astronomy payloads of the cosmic light house program onboard China's Space Station, which is planned for operation starting around 2020 for about 10 years. Beam test with a HERD prototype, to verify the HERD specifications and the reading out method of wavelength shifting fiber and image intensified CCD, was taken at CERN SPS in November, 2015. The prototype is composed of an array of 5*5*10 LYSO crystals, which is 1/40th of the scale of HERD calorimeter. Experimental results on the performances of the calorimeter are discussed.
Spectral imaging technology has made great achievements in applications of earth observation and space target detection, with the further development of research, the requirement that People tend to get more material properties about target is also improving rapidly, so getting more characters of the target is continuous pursuit goal for the instruments of optical remote sensing. Polarization is one of the four main physical properties of light including intensity, frequency and phase . It has very important significance for remote sensing observations such as improving the accuracy of target recognition. This paper proposes on a spectropolarimeter system based on Sagnac interferometer, and introduces the main aspects related to System components, working principle, optical design, adaptive spectrum extraction algorithm, state of polarization extraction methods. Also get the data of polarization spectral imaging by using the instruments designed by the principle .By processing these data I have got the combined polarization image and target spectral curves, achieved a good result. It is a new attempt to obtain polarization spectral image by integrated measuring system. Then thoroughly solve the traditional shortcoming of spectropolarimeter, such as asynchronous detecting, poor stability and vibration, poor energy efficiency. It can be applied to many kinds of fields. Simultaneously the paper puts forward some relevant new points in the future research for this kind of principle.
In the field of deep space exploration, the detector needs high-speed data real-time transmission and large capacity storage. SATA(Serial advanced technology attachment) as a new generation of interface protocols, SATA interface hard disk has the advantages of with large storage capacity, high transmission rate, the cheap price, data is not lost when power supply drop, so it is suitable for used in high speed large capacity data storage system. This paper by using Kintex-7 XCE7K325T XILINK series FPGA, the data of scientific CMOS CIS2521F through the SATA controller is stored in the hard disk. If the hard disk storage is full, it will automatically switch to the next hard disk.
Diffractive telescope is an updated imaging technology, it differs from conventional refractive and reflective imaging system, which is based on the principle of diffraction image. It has great potential for developing the larger aperture and lightweight telescope. However, one of the great challenges of design this optical system is that the diffractive optical element focuses on different wavelengths of light at different point in space, thereby distorting the color characteristics of image. In this paper, we designs a long-wavelength infrared diffractive telescope imaging system with flat surface Fresnel lens and cancels the infrared optical system chromatic aberration by another flat surface Fresnel lens, achieving broadband light(from 8μm-12μm) to a common focus with 4.6° field of view. At last, the diffuse spot size and MTF function provide diffractive-limited performance.
In order to obtain the far-field distribution of high dynamic range laser focal spot, the mathematical model of schlieren method to measure the far-field focal spot was proposed, and the traditional schlieren reconstructed algorithm was optimized in many aspects in this paper. First of all, the mathematical model which used to measure the far-field focal spot was created, the amplificatory coefficient <i>K</i> of the main lobe intensity and amplificatory coefficient <i>b</i> of the laser spot area were selected ; Secondly, the two important parameters were calibrated and the accurate main lobe spot and side lobe spot were captured by the integrated diagnostic beam fast automatic alignment system; Finally, the schlieren reconstructed algorithm was optimized by circle fitting method to calculate side lobe image center and weighted average method to fuse the joint image edge, and the error of traditional schlieren reconstruction method for side lobe center was reduced and the obvious joint mark of reconstructed image was eliminated completely. The method had been applied in a certain laser driver parameter measurement integrated diagnostic system to measure far-field laser focal spot. The experimental results show that the method can measure the far-field distribution of high dynamic range laser focal spot exactly on the condition that the parameter of mathematical model is calibrated accurately and the reconstructed algorithm of schlieren measure is optimized excellently.
We propose an approach to correct the data of the airborne large-aperture static image spectrometer (LASIS). LASIS is a kind of stationary interferometer which compromises flux output and device stability. It acquires a series of interferograms to reconstruct the hyperspectral image cube. Reconstruction precision of the airborne LASIS data suffers from the instability of the plane platform. Usually, changes of plane attitudes, such as yaws, pitches, and rolls, can be precisely measured by the inertial measurement unit. However, the along-track and across-track translation errors are difficult to measure precisely. To solve this problem, we propose a co-optimization approach to compute the translation errors between the interferograms using an image registration technique which helps to correct the interferograms with subpixel precision. To demonstrate the effectiveness of our approach, experiments are run on real airborne LASIS data and our results are compared with those of the state-of-the-art approaches.
The low light level imaging and ultrafast detection system is a high performance ICCD composed of imaging intensifier and high-frame-rate CCD, the important readout system of the semi-digital 3D-imaging calorimeter for space observation of cosmic ray and dark matter that has the function of intensifying, delaying, imaging and memorizing, making rapid response to the ultrafast low light signals that is transmitted by tens of thousands of wavelength shifting fibers, generated by the semi-digital 3D-imaging calorimeter when cosmic ray is passing through. Using the images of ICCD and the semi-digital information reconstruction method, the particle type, energy and direction of cosmic ray can be obtained. By solving some key technologies such as coupling techniques of optical parts, low noise and high speed imaging of high-frame-rate and large-area CCD, the high speed gating system of imager intensifier, the prototype of high performance ICCD is developed. The prototype of ICCD can meet the requirements: up to 400 frames per second, detection ability for low light about 10 photons, linear dynamic range more than 300.Performances verification of the prototype is carried out by using a single photon test system. In this paper we will describe the requirement of ICCD for the ground cosmic detection system which is used to verify the theory of Herd (High Energy Cosmic-Radiation Detection), the key techniques used to achieve perfect performances, and test method and result of the ICCD.
Image Intensified CCD (ICCD) camera is widely used in the field of low-light-level image detection. The crucial part of
ICCD, coupling component, which realizes the image transmitting between the image intensifier and detector, affects the
final performance of the ICCD camera significantly. There are two means of coupling: relay lens and optical fiber taper
(OFT). OFT has the merits of small volume and relatively high coupling efficiency, therefore it is commonly used in the
portable devices or applications with less precision demands. However, relay lens turns out to be a better solution other
than OFT for the applications with no volume and weight restrictions, since it provides higher resolution, perfect image
plane uniformity and manufacture flexibility. In this paper, we discuss a methodology of high performance relay lens
design and based on the method a solid design is proposed. There are three major merits of the lens design. Firstly, the
lens has large object space numerical aperture and thus the coupling efficiency reaches 5% at the magnification of 0.25.
Secondly, the lens is telecentric in both sides of object space and image space, this feature guarantees uniform light
collection over the field of view and uniform light receiving on the detector plane. Finally, the design can be
conveniently optimized to meet the needs of different type of image intensifier. Moreover, the paper presents a prototype
ICCD camera and a series of imaging experiment as well. The experiment results prove the validity of the foregoing
analysis and optical design.
In order to obtain the exact center of an asymmetrical and semicircular aperture laser spot, a method for laser spot detection method based on circle fitting was proposed in this paper, threshold of laser spot image was segmented by the method of gray morphology algorithm, rough edge of laser spot was detected in both vertical and horizontal direction, short arcs and isolated edge points were deleted by contour growing, the best circle contour was obtained by iterative fitting and the final standard round was fitted in the end. The experimental results show that the precision of the method is obviously better than the gravity model method being used in the traditional large laser automatic alignment system. The accuracy of the method to achieve asymmetrical and semicircular laser spot center meets the requirements of the system.
Spatial heterodyne spectrometers have been used in multiple scientific studies since their invention and early
development. Broadband spatial heterodyne spectrometers also have the advantages of large etendue, high spectral
resolving powers, and high data collection rates as traditional spatial heterodyne spectrometer. Basic theory, design and
performance parameters, breadboard experiment for a broadband, high-resolution spatial heterodyne spectrometer are
reported. The experimental spatial heterodyne spectrometer achieves a design resolution 0.39cm-1. Firstly, it is
demonstrated that broadband spatial heterodyne spectrometer have the advantages of wide spectral coverage and high
spectral resolving power simultaneously; secondly, the effects of optical defects on the system are discussed; thirdly,
Two dimension interference data procession also is mentioned.
This paper presents a very simple circuit of balanced two-stage current amplifier and regulated cascode (RGC) amplifier
to realize a linear current-to-voltage conversion. It can accept very low currents in the nA range. The noise model and
strategies for noise reduction are discussed. The circuit is designed for 0.18μm CMOS technology and verified by