Systemtheoretische Grundlagen optoelektronischer Sensoren
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In this work, the nonlinear seven-dimensional hypersphere is expanded into a linear Taylor-series and a multidimensional linear approach is used to solve the equation systems. The second one is the navigation in a multidimensional data cube. The incessantly calcula- tion of the address of an appropriate data set is a very time consuming process. So procedures for a fast data access are neces- sary. The result of the module described above is the at-sensor-radiance for each spatial pixel of each image line in each spectral channel.
It is divided into an optical and an electronic part. The aim is the calculation of digital numbers out of the at-sen- sor-radiance given either by the radiative transfer module of SENSOR or by radiance values provided by other hyperspectral remote sensing systems. Each spectral APEX channel is characterized by a spectral response function.
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It describes the sensitivity of the channel regard- ing to the energy in a certain range of the electromagnetic spectrum. In order to model a continuous response function, SEN- SOR defines the response function of each channel as a set of discrete samples sampling distance 1 nm. The response function of the entire remote sensing system must be given for each pixel on the detector CCD spatial and spectral direction and is the result of a calibration process.
All other parameters are fixed per simulated flight. Most approaches end on this stage, ignoring important noise sources and processing steps. A quasi-convolution in the spectral direction is performed by sum- ming up the generated electrons of the sub-channels into one spectral APEX channel Fig.
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It includes certain noise sources e. The output is one digital number per spatial and spectral pixel, per image line. The transfer of the at-sensor-radiances to digital numbers corresponds to an inverse radiometric calibration. This procedure is performed for each spatial pixel of each line of each spectral channel. The influence of the point spread function PSF of the system and blurring caused by the flight motion during integration time is considered by applying convolution kernels across and along flight track.
This is performed for each spectral image, consid- ering neighboring pixels.
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The result is a simulated APEX data cube. The cube also includes data of the planned in-flight calibration.
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Before and after each data acquisition, the system looks into a calibration sphere and a few lines will be scanned with the shutter closed. Black pixels on the outer border of the CCD are used, in order to estimate the dark current of the electronics Schaepman, b. All these data are introduced in the simula- tion process as well.
Smile and frown keystone , caused by the optical components of APEX, are of special interest. If needed, the simulation of these effects with SENSOR may be performed the following way: For each pixel on the CCD spatial and spectral , separate geometric calibration data are provided simulated or measured.
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After that, the radiation transfer and system hardware modules can be applied the way described above. The simulation of distortion effects using SENSOR is possible, but results in a memory and computional efficiency problem. This time would yet increase by applying enhanced convolution algorithms or by using narrower sub-channels. The image gives an unnatural impression due to a coarse subdivision of the object classes.
The first and last lines are the in-flight calibration data Schaepman, b. The black pixels on the left and right border are used for dark current measurements. The review of the geometric simula- tion shows no problems and is performed by comparing the original elevation model with all calculated intersection points between pixel rays and DEM. The result should be the input reflectance spectra. Spectrum after simulation and atmospheric correction Reflectance [-] Original reference spectrum Wavelength [nm] Fig.
Ignoring these parts of the spectrum, the absolute mean deviation between the input and the output spectrum amounts to 0. It describes the remote sensing system itself and essen- tial parts of its environment. Using this concept, the interactions between parameters of the complex model, retrieval algo- rithms, and any output, such as data accuracy and costs, can be evaluated. In principle, it is possible to model all optoelectronic remote sensing systems since merely the part concerning the hardware has to be changed.
All parts of the complex sensor-environment system are considered.
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Remarkable fea- tures are implemented, e. One of the main arguments against a software model is the complete reliance on a selection of known parametrized effects. However, this approach is often the only way to obtain any information on the interaction between the elements mentioned above. Consequently, one important task during the construction of a complex software model such as SENSOR is to find the essential parameters influencing the output values and to include them into the model. The simulation results in an artificial image data cube.
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