Yi-ming Cao

Scattering near specular direction for horizontally oriented ice discs

Scattering phase function on horizontally oriented ice particles near the specular reflective direction is analytically modeled using a mixed method combining direct reflection and Fraunhofer diffraction components, where particles are simply treated as circular facets and the effect of fluttering is introduced under the assumption of Gauss distribution. The obtained model expression reveals that the essence of far-field scattering around specular direction is the diffraction pattern modulated by fluttered geometric reflection. Four groups of experiments are designed to validate this model at different wavelengths and incidence angles, and the calculated phase functions present good agreement both in distributions and peak values with that of T-matrix method in conjunction with a Monte Carlo stochastic process.

Modeling and simulation for optical sensor imaging in space rendezvous and docking

In order to realize the dynamic imaging simulation for optical sensor in space rendezvous and docking, in this paper, a dynamic imaging modeling and simulation method for optical sensor is presented based on the general idea of surface mesh-creating. The simulation models include geometry and optical characteristics models of space object and star background, imaging model of space based optical sensor, and the noise model during the process of imaging are given out. Taking advantage of these models, dynamic imaging simulation of optical sensor is finally accomplished. For two satellite objects, the imaging simulations are realized with this method in different conditions. Results show that the method could accurately and efficiently simulate the images of optical sensor, so this method could be applied in the development of space rendezvous and docking system.

Modeling of image matching accuracy with image metrics based on least squares matching algorithm

The determination of conjugate points in a stereo image pair, i.e. image matching, is the critical step to realize automatic surveying and recognition in digital photogrammetric processing. The accuracy of image matching is closely related to specific matching algorithm as well as images. In this paper, the qualitative and quantitative relationships between the matching accuracy and the image metrics are studied at the basic of Least Squares Image Matching algorithm (LSIMA). Firstly, the algorithm is deduced mathematically, and then the main image metrics affecting the matching accuracy are presented, including total variation (TV) metric and difference of signal-to-noise ratio (DSNR) metric. Subsequently, variations of matching accuracy with TV and DSNR are analyzed, and mathematical model between them is developed. Studies show that the matching accuracy presents the natural exponential rule along with TV and DSNR of image pairs. Besides, parameters of the model are estimated and the model is verified by simulation experiments. Finally, the correctness of the model is verified using real remote sensing images. Experimental results demonstrate the robustness and accuracy of the proposed model.

Accuracy assessment for infrared camera laboratory radiometric calibration

An accuracy assessment method of infrared camera laboratory radiometric calibration was studied for the sake of validation of space infrared camera measured data. Firstly, image process of infrared camera was analyzed and modeled on laboratory radiometric calibration, a model of linear radiometric calibration coefficient synthesized impact chain was built; secondly, based on the model, a model of uncertainty of linear radiometric calibration coefficient was built; finally, An experiment verified the validity of the model. The results of experiment indicate that the difference between the assessment value and maximum experiment value is 0.08% and the difference between the assessment value and average of experiment values is 0.79% at gain uncertainty assessment, the difference between the assessment value and maximum experiment value is 0.7%, the difference between the assessment value and average of experiment values is 0.18% at offset uncertainty assessment, the uncertainty of coefficients of radiometric calibration get from experiments is basically consistent with the assessment. The method of accuracy assessment of radiometric calibration combined with chain factors uncertainty can avoid omitting and repetition in uncertainty estimation; optimization of camera used for quantification can be designed on the method.

Bad pixel replacement based on spatial statistics for IR sensor

IR focal plane arrays typically contain bad pixels. Bad pixels have to be corrected because those can significantly impair the performance of target-detection algorithms. On the other hand, particularly as an aid to visual interpretation, it is desirable to replace the bad pixels. IR image contains spatial information and is correlative in spatial domain. In spatial statistics the semivariogram is an important function that relates semivariance to sampling lag. This function can characterize the spatial dependence of each point on its neighbor and provide a concise and unbiased description of the scale and pattern of spatial variability. One of the main reasons for deriving semivariogram is to use it in the process of estimation. Kriging is an interpolation and estimation technique that considers both the distance and the degree of variation between known data points when estimating values in unknown areas. In this paper a new technique based on spatial statistics is developed for bad pixel replacement. The main objective of the technique is to replace bad pixels through Kriging estimation. Theory analysis and experiments show that the method is reasonable and efficient.

Modeling and analysis for infrared clutter radiance of atmospheric absorption band sensor

An extended analytic model for atmospheric clutter radiance in absorption bands is developed. In this model, clutter radiance is mainly due to temperature and reflectance fluctuations of atmospheric, cloud and earth. A simplified line-of-sight (LOS) radiance model for short-wave infrared (SWIR) and mid-wave infrared (MWIR) absorption bands is introduced, based on the one-dimensional radiation transfer equation (RTE) and Youngs semi-empirical model for diffuse reflectance of clouds. Under the assumption that atmospheric temperature fluctuations are isotropic horizontally, the relations between clutter radiance and temperature fluctuations as well as other factors are obtained. The clutter radiance characteristics are analyzed in 2.7μm and 4.3μm absorption bands, the long wavelength wing of 2.85μm and 4.35μm shows a much larger clutter contribution from earth and cloud. The models present here are efficient and reasonable by comparing the results of MODTRAN and data from SPIRIT-III radiometer.

Variable-step constant statistics algorithm for removing residual fixed pattern noise of infrared images as second non-uniformity correction

Regarding the appearance of fixed pattern noise (FPN) in the IR images of an IR observation system introduced by errors in assembly, environment, etc. Non-Uniformity Correction (NUC) is an important technique for IRFPA. Because the real radiation response of pixels in the given dynamic range is nonlinear and the existence of 1/f noise, especially the high temperature scaling point changes the thermal balance of the IR observation system, using the traditional linear approximate method (temperature scaling method) is hard to obtain the perfect corrective images. On the other hand, because of Scene-Based Non-Uniformity Correction (SBNUC) does not rely on specialized hardware, SBNUC is very attractive alternative to radiometric calibration for infrared sensors, thereinto, Constant Statistics (CS) is the best known approach, but it lies on the scene content and has intimate correlation with the sample quantity. So, in this paper, we present a novel approach which inherits the rapidity of temperature scaling method and also consider the astringency of CS, using variable-step constant statistics (VSCS) as second non-uniformity correction in the spatial and time domains of infrared images to eliminate the residual fixed pattern noise which resulted from the theoretical and methodological errors of temperature scaling method. The experimental result for the real infrared images data is a solution which effectively eliminates the residual fixed pattern noise, and at the same time, it proved the effectiveness of this algorithm.

A simulation method of 3D cirrus radiance images for space-based missile warning detectors

High-altitude cirrus clouds are main clutter sources for space-based missile warning in 2.7μm band, their radiance images can serve as an important guide in designing and evaluating early warning systems. First of all, the 3D finite element model of atmosphere (include clouds) was constructed, and its corresponding discrete radiative transfer calculation method was derived as well. Then, the geometry and scattering properties of cirrus were given for the special application in 2.7μm band. With the auxiliary calculation of MODTRAN, the primary and secondary factors in the radiative transfer process were distinguished leading to a simplified radiance calculation means. Finally, a new method was proposed to generate radiance images by integrating source functions in the chief ray pathway of each camera pixel. The simulated results were given and compared with the results of SHDOMPP (Spherical Harmonic Discrete Ordinate Method for Plane-Parallel Atmospheric Radiative Transfer) program, which prove the correct projecting relationship between the 3D cirrus sense and its image on detector focal plane, and validate the rationality and credibility of the proposed method in quantitative radiance calculation.

3D Stochastic cloud generation for performance evaluation of space-based optical system

A three-dimensional (3D) stochastic cloud generation architecture is developed to simulate radiative scenes for performance evaluation of space-based optical system. The rescale-and-add fractal algorithm is employed to generate internal and external structure data of clouds. The Spherical Harmonic Discrete Ordinate (SHDOM) code is selected to solve the 3D radiative transfer equation numerically. The flowchart of the simulation system is shown. The methods for generating 3D grid data of cloud spatial structure and liquid water content (LWC), as well as the radiative quantities, are presented. Two types of clouds are generated as illustrations. Radiative cloud scenes under different imaging conditions could be conveniently available using this framework. The simulation results demonstrate the effectiveness of the proposed methods and architecture.

Infrared non-uniformity correction algorithm based on fast independent component blind separation

Going with Infrared Focal Plane Array (IRFPA) development, the application of infrared imaging system is more and more extensive, its well known that the Non-Uniformity Correction (NUC) is the only necessary data soft processing in the whole infrared imaging data link, it will be seen from this that the NUC quality stand or fall influences the final imaging product quality directly, for target detection and identification system, it increases the complexity and timeliness of the target detection and identification algorithm undoubtedly. Currently, the Non-Uniformity Correction (NUC) algorithm can be divided two classifications: the one is that Non-Uniformity Correction based on calibration source, this algorithm assumes the infrared system response characteristic is linear, takes the dark current and gain as the two correction parameters, but for nonlinear, especially for the response drift characteristic and the ambient temperature change, the higher the system sensibility is, the greater the influence is and the higher the design requirements for system stray radiation are. The correction effectiveness is limited seriously; the another is adaptive correction algorithm based on scene (SBNUC), it can be subdivided time domain, space domain and motion estimation processing algorithms, although it do not need physical calibration source and also reduces the influence of system response drift to a certain degree, but the requirement is rigorous for statistics specimen and size, and the rapidity of convergence and stability are different. In this paper, according to blind information source decomposition technique, the infrared image is divided to signal and noise as two information sources, a new Non-Uniformity Correction method based on Fast Independent Component (FastICA) blind separation is put forward. By means of the experimental contrast analysis for the linear correction algorithm and constant statistics algorithm of real infrared image, by this new algorithm, the influence of the system response drift and the ambient temperature change for the linear correction algorithm based on physical calibration source is not only suppressed, but also the shortages of the scene-based Non-Uniformity correction (SBNUC) in statistics specimen and size are overcome partly. The experimental result proved the effectiveness of this algorithm in the paper which effectively separated the signal and noise of the infrared image. At the same time, the algorithm in the paper supplied a new solution of Non-Uniformity Correction (NUC) by the experiment.