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.