Ms. Feifei Xu Profile

Feifei Xu

at Shanghai Institute of Technical Physics

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Publications (5)

Proceedings Article | 31 January 2023 Paper

Method to identify defective detectors of thermal focal plane array based on its frequency domain characteristics

Xiaoxian Huang, Feifei Xu, Yutian Fu

Proceedings Volume 12505, 125050H (2023) https://doi.org/10.1117/12.2665554

KEYWORDS: Staring arrays, Fourier transforms, Target detection, Capacitance, Thermal imaging cameras, Nonlinear response, Correlation coefficients, Signal detection, Frequency response, Cameras

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Proceedings Article | 31 January 2023 Paper

Non-uniformity correction of infrared image for wide field with camera radiation constraint

Feifei Xu, Xiaoxian Huang, Ying Zhou, Yutian Fu

Proceedings Volume 12505, 125051S (2023) https://doi.org/10.1117/12.2664751

KEYWORDS: Nonuniformity corrections, Infrared imaging, Cameras, Infrared radiation, Infrared cameras, Detection and tracking algorithms, Signal detection, Calibration, Target detection, Imaging systems

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Infrared focal plane linear array detector is the core element to achieve high spatial resolution and high radiometric resolution infrared imaging. Due to the difference of detector response rate caused by non-uniform materials and the high sensitivity of camera to background signal changes, the complex non-uniformity of the image is eventually caused. In order to identify targets effectively and quickly, real-time non-uniformity correction must be performed on infrared camera images. The most commonly used method is to obtain the correction coefficients of each pixel based on variable temperature blackbody calibration before each image. However, it is difficult and costly to design full-aperture calibration blackbody for infrared camera with wide field of view and high resolution, and the feasibility is not high. In order to correct the non-uniformity of the acquired image, this paper proposes a non-uniformity correction algorithm for single-scene infrared image based on finite constraints. According to the principle that the pixel response rate is basically unchanged, the algorithm establishes a constraint relationship between the response rate of each probe element obtained by laboratory calibration and the response rate of the central standard pixel to iteratively screen the samples involved in the correction coefficient calculation, so as to realize the non-uniformity correction of the image. The proposed algorithm was verified by using the location imaging data of the medium-wave infrared camera. The non-uniformity of local uniform image was 44.1% before correction, and decreased to 4.4% after correction, while the uniformity of conventional scene correction was 13.5% after correction. Therefore, the non-uniformity of the proposed algorithm is reduced by 9.13% compared with the conventional scene correction method. The algorithm successfully breaks through the design limitations of no star calibration device of the wide-field high-resolution infrared camera, improves the target recognition efficiency of infrared image, and is conducive to implement in engineer. In addition, it only takes about 1min to correct an image of 30000 pixel *30000 lines by testing.

Proceedings Article | 31 January 2023 Paper

A practical target radiance estimating method on near-surface long-range

Fei Fei Xu, Xiao Xian Huang

Proceedings Volume 12505, 125051L (2023) https://doi.org/10.1117/12.2664528

KEYWORDS: Target detection, Atmospheric modeling, Ultraviolet radiation, Atmospheric sensing, Sensors, Transmittance, Black bodies, Ultraviolet detectors, Signal detection, Solar radiation models

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UV detection technology has great advantages for the effective detection of space targets. Due to the immediacy of the space detection target, it is difficult for the detection system to capture the target. When detecting the space target, it is necessary to select appropriate bands and set effective instrument parameters to increase the capture probability of the space target. At the same time, the field experiment process of space target detection by detector is relatively complex and difficult, so it is difficult to obtain such data. In the absence of test data, in order to make the UV detection system can set effective detection band, and ensure the good test and quickly obtain the radiation characteristics of the longrange target, simulation method is used in this paper. In an experimental way, in the case of unknown atmospheric conditions and composition, a model for calculating the near-surface long-range target radiance in UV band is constructed, which simplifies the radiative transmission process of the signal in the atmosphere. In this paper, the radiation of target in UV band is studied, and the mathematical model of radiation calculation is established. The research results have certain engineering application value. In the field test, it is difficult to determine the atmospheric transmittance between the camera and the target due to the lack of estimation of the atmospheric composition at the launch time, which makes it difficult to accurately estimate the radiation of the long-range target at near-surface in the test site. To solve this problem, the ultraviolet (UV) band was divided into two parts, that were target band (240nm~280nm) and background band (300nm~400nm). By simulation, the estimating models of atmospheric transmissivity were separately established in the two bands. And then the long-distance target radiance was estimated only according to the distance for successive two times between the detector and target in a short time. Compared with the radiance of blackbody simulated, the relative error is about 9.87% in the target band, but only 0.11% in the background band. The research can provide technical support for UV detector to effectively detect the long-range target radiance.

Proceedings Article | 11 September 2013 Paper

High-temperature target infrared spectrum radiance study

Feifei Xu, Yong Hu, Qiu Yin, Cailan Gong, Lingya Zhu, Hua-ying He

Proceedings Volume 8907, 89072Q (2013) https://doi.org/10.1117/12.2033134

KEYWORDS: Atmospheric modeling, Atmospheric sensing, Sensors, Infrared radiation, Target recognition, Data modeling, Statistical analysis, Satellites, Radiative transfer, Remote sensing

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Proceedings Article | 30 August 2013 Paper

The Hughes phenomenon in hyperspectral classification based on the ground spectrum of grasslands in the region around Qinghai Lake

Weiwei Ma, Cailan Gong, Yong Hu, Peng Meng, Feifei Xu

Proceedings Volume 8910, 89101G (2013) https://doi.org/10.1117/12.2034457

KEYWORDS: Neodymium, Remote sensing, Principal component analysis, Vegetation, Distance measurement, Statistical analysis, Spectral resolution, Spectroscopy, Hyperspectral imaging, Mahalanobis distance

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Hyperspectral data, consisting of hundreds of spectral bands with a high spectral resolution, enables acquisition of continuous spectral characteristic curves, and therefore have served as a powerful tool for vegetation classification. The difficulty of using hyperspectral data is that they are usually redundant, strongly correlated and subject to Hughes phenomenon where classification accuracy increases gradually in the beginning as the number of spectral bands or dimensions increases, but decreases dramatically when the band number reaches some value. In recent years，some algorithms have been proposed to overcome the Hughes phenomenon in classification, such as selecting several bands from full bands, PCA- and MNF-based feature transformations. Up to date, however, few studies have been conducted to investigate the turning point of Hughes phenomenon (i.e., the point at which the classification accuracy begins to decline). In this paper, we firstly analyze reasons for occurrence of Hughes phenomenon, and then based on the Mahalanobis classifier, classify the ground spectrum of several grasslands which were recorded in September 2012 using FieldSpec3 spectrometer in the regions around Qinghai Lake，a important pasturing area in the north of China. Before classification, we extract features from hyperspectral data by bands selecting and PCA- based feature transformations, and In the process of classification, we analyze how the correlation coefficient between wavebands, the number of waveband channels and the number of principal components affect the classification result. The results show that Hushes phenomenon may occur when the correlation coefficient between wavebands is greater than 94%，the number of wavebands is greater than 6, or the number of principal components is greater than 6. Best classification result can be achieved (overall accuracy of grasslands 90%) if the number of wavebands equals to 3 (the band positions are 370nm, 509nm and 886nm respectively) or the number of principal components ranges from 4 to 6.

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