Space-based infrared technology has the potential to detect aerial targets globally, for which strong atmospheric absorption of the background radiometric signal provides spectral detection windows. However, the atmospheric content varies in time and space, affecting its attenuation of the background. It is therefore necessary to assess the impact of the variation in the atmospheric content of absorption gas on the target detection. Based on the aerial target space-based observation characteristics model, the target radiation contribution ratio and the target-background contrast were calculated for different atmospheric contents with reference to the typical atmospheric profiles. The variation of the target-background relative differences due to changes in gas content was discussed. Furthermore, the range of change in the target-background contrast and signal-to-noise ratio(LSNR) for different atmospheric contents was calculated, considering instrument response characteristics. The effect of atmospheric content and full width at half maximum (FWHM) variation on the stability of the target-background relative relationship was analyzed. The findings highlight that the target radiation contribution ratio and the target-background contrast are more significantly affected by changes in water vapor, while changes in carbon dioxide concentration have little effect due to its large content base. Spectral bands where water vapor and carbon dioxide interact can reduce the effects of water vapor changes, but require narrow FWHM. But the too narrow FWHM can lead to changes in the target-background relative relationship and reduce its stability due to the influence of instrument noise. This research helps to understand the impact of changes in atmospheric condition on space-based infrared detection of aerial targets and the challenges faced in developing detection algorithms.
Coded aperture imaging spectrometer is a new type of hyperspectral imaging instrument. The space-borne hyperspectral imager makes images by pushing and sweeping. In the ideal imaging model, it is assumed that one pixel is separated between two adjacent frames so that the target information can be accurately reconstructed. When coding aperture imaging is performed under motion compensation, the moving distance of the object image on the focal plane at each imaging time is different, and there is an amount of dislocation, resulting in decoding error of the decoded and restored data along the direction of the orbit, and the phenomenon of ground object "double shadow" and spectral decoding distortion appear in the simulation image. The amount of misalignment under different compensation modes is different, resulting in different decoding errors. The mathematical model of target data encoding and decoding in push-sweep coded aperture imaging and the mathematical model of field of view optical axis angular velocity in motion compensation mode were constructed. The simulation method of coded aperture imaging hyperspectral data under motion compensation was established, and the simulation data quality was analyzed. Through data quality analysis, it can be seen that under the uniform angular velocity mode, the uniform ground velocity mode and the uniform integral time mode, the cumulative amount of dislocation decreases successively, which is 5.7 m, 0.7 m and 0 m, respectively. The "double shadow" phenomenon of the simulated image becomes less and less obvious, and the image quality becomes clearer and clearer. Meanwhile, the restoration and reconstruction accuracy of the coding aperture imaging improves successively.
The atmosphere calibrated airborne and space borne hyperspectral images are the HDRF of canopy. The spatial nonuniformity of HDRF may result in inversion errors of the heavy metal stressing. In this paper, the HDRF of copper stressed plant samples under different illumination conditions was acquired with the laboratory hyperspectral simulation system called MHRS2F. The difference between the HDRF of canopy and the BCRF of leaves was firstly discussed. Then the changes of spatial distribution of the HDRF for different copper concentrations and illumination conditions were discussed. At last, the sensitivity of various vegetation indices to illumination and observation directions was compared. By comparing the prediction accuracy of different vegetation indices on different observation directions and illumination conditions, the HVI and mRENDVI were found to be more stable and accurate.
In order to widen the spectral coverage of imaging spectrometer based on Acousto-optic tunable filer (AOTF) from 400 nm to 2500 nm, a new configuration of all reflective fore-optics is proposed. The primary and secondary mirrors are used as common objective for visible and short-wave infrared channels, and then there are two independent tertiary mirrors in each channel. This configuration not only solves the problems caused by multi-view observation in traditional systems, but also meets the aperture requirements of both AOTFs to make them work in peak performance. The initial structure parameters is calculated by simultaneous Seidel aberration equations. The final design results validate the availability of this configuration in the AOTF based imaging spectrometer with two channels.
The acousto-optic tunable filter (AOTF)-based spectrometer has been widely used in hyperspectral imaging applications. The sidelobe phenomenon which is the result of sinc2 shape of the spectral response reduces the quality of the spectral data critically. Especially when a laser appears in the scene, the image point of the laser will be present in images of several bands with varying positions and intensity. This paper discussed the sidelobe phenomenon using the phase mismatching theory and proposed a sidelobe model based on the three-surface AOTF model(TSAM) which was a previous related work. This model provided a simplified method to trace laser light in the AOTF imaging system. A verification experiment was demonstrated, in which a dual-channel AOTF imaging system was introduced. The laser pixel coordinate and DN value were extracted from the panchromatic image and were put into the proposed model, which gave the predicted positions and DN values in different bands. The measured values were extracted from the spectral images. Results showed that the predicted values were in g
FOV separation (between VNIR sensor and SWIR sensor) and motion compensation imaging modes are introduced into the pushbroom imaging spectrometer to increase the SNR of the imaging data sometimes. Besides the higher SNR, the two imaging modes result in some bad effects on the imaging data, such as the additional misregistration. In the paper, a digital simulator for pushbroom Offner hyperspectral imaging spectrometer is used to analyze the misregistration caused by the FOV separation and the motion compensation imaging modes. Based on the imaging process, the simulator consists of a spatial response module, a spectral response module, and a radiometric response module. The FOV separation is simulated in the imaging position calculation process of the spatial response module, and the motion compensation is considered in both the imaging position simulation and the radiometric response module. Using the simulator, the imaging position data is created to quantify the misregistration. The result shows that the imaging track deviation, caused by the FOV separation, between the VNIR sensor and SWIR sensor keeps a constant quantity in the latitude direction. However, the deviation will increase along with the imaging time in the longitude direction. When the two imaging modes are both considered, the deviation is symmetrical relative to the nadir point in the latitude direction. However, the deviation is not symmetrical in the longitude. In order to analyze the misregistration effect on the imaging data, simulation data with different imaging modes on Dongtianshan remote sensing testing field is created using the simulator. And the misregistration effect on the spectra of flat ground pixel and rugged ground pixel are analyzed.
Hyperspectral imaging instrument performance, especially spectral response parameters, may change when the sensors work in-flight due to vibrations, temperature and pressure changes compared with the laboratory status. In order to derive valid information from imaging data, accurate spectral calibration accompanied by uncertainty analysis to the data must be made. The purpose of this work is to present a process to estimate the uncertainties of in-flight spectral calibration parameters by analyzing the sources of uncertainty and calculating their sensitivity coefficients. In the in-flight spectral calibration method, the band-center and bandwidth determinations are made by correlating the in-flight sensor measured radiance with reference radiance. In this procedure, the uncertainty analysis is conducted separately for three factors: (a) the radiance calculated from imaging data; (b) the reference data; (c) the matching process between the above two items. To obtain the final uncertainty, contributions due to every impact factor must be propagated through this process. Analyses have been made using above process for the Hyperion data. The results show that the shift of band-center in the oxygen absorption (about 762nm), compared with the value measured in the lab, is less than 0.9nm with uncertainties ranging from 0.063nm to 0.183nm related to spatial distribution along the across-track direction of the image, the change of bandwidth is less than 1nm with uncertainties ranging from 0.066nm to 0.166nm. This results verify the validity of the in-flight spectral calibration process.
Traditional Wiener filtering has been widely used to restore single-band images. However, it has not been discussed yet how to specially use Wiener filtering to get a spectral restoration effect for a 3-Dimensional hyperspectral image. Modeling the measured spectrum to be the result of a convolution with the Spectral Response Function (SRF) and noise-adding process, a method to apply spectral Wiener filtering to hyperspectral images is proposed. Spectral Wiener filtering aims to get an optimal estimation of real spectrum which considers the effect of both noise and SRF. For doing this, the spectral signal-to-noise ratio (SNR) is calculated using a decorrelation method. In an experiment based on simulated hyperspectral image cube, spectral Wiener filtering in a pixel by pixel way achieved a 1.38% increase in the average depth of spectral signature and a 15.4% increase in image sharpness. As a comparison, spatial Wiener filtering band by band achieved a 0.49% decrease in the average depth of spectral signature and a 21.6% increase in image sharpness. The results suggest that spatial and spectral degradation of hyper-spectral image are inter-coupled, and spectral Wiener filter is more suitable to restore spectrum while the spatial Wiener filter is more suitable to restore single-band image.
The motion blur simulation technique is widely used in remote sensing of an image chain simulation. However, the traditional method, which models the motion blur through a point spread function (PSF), is not precise enough when the imaging area is rugged or the motion of the platform is unstable. A physically based simulation model of motion blur is proposed. The model uses an image motion vector (IMV) field to describe the relative motion presented on the image plane during the exposure time. Based on the IMV field, the opto-electrons blurring model is built to simulate the blurring effect. A physical experiment was made to validate the model. The experiment result demonstrates that the simulation result generated by the model provided is more precise than the traditional PSF method, and a more complex motion status can be presented by the proposed method.
Image simulation plays an important role in remote sensing system design and data processing algorithm development, supposing that the fidelity of the simulated images is high enough. Many remote sensing image simulation models generate the geometric characteristics of the images through a georeferencing, convolution, and resampling process. In the georeferencing and resampling steps, each pixel is taken as a point, meanwhile a shift-invariant detector point spread function (PSF) is used in the convolution step. It omits the footprint size variation caused by the ground relief, earth curvature, and oblique viewing. To improve the fidelity of the simulated images, a pixel-size-varying (PSV) method was proposed: the four corners of each detector in a whiskbroom, pushbroom, or staring imaging sensor are separately considered in the georeferencing step, the sensor detector PSF is abandoned from the convolution step, and then the PSV sampling is simulated using an overlapping-area-weighted sum of the oversampled pixels. A validation experiment was conducted in simulating EO-1 Hyperion L1R data from georeferenced HyMap reflectance data. It showed that the PSV method outperforms the traditional method in the spectral aspect and is equal to the traditional method in other aspects, by comparing the simulated images with the actual one.
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