## 4 Radiometric Processing Workflow
### Sensor-Related Calibration
#### 4.2.1 Relative Radiometric Calibration  
Relative radiometric calibration transforms the output of the sensor to normalized DNs (DN\({}_{\text{n}}\)), which have a uniform response over the entire image during the time of operation [121]. This transformation includes dark signal correction and photo response and optical path non-uniformity normalization.  
The dark signal noise mainly consists of the read out noise and thermal noise, which are related to sensor temperature and integration time [121] and is corrected by estimating the dark signal non-uniformity (DSNU). Practical approaches for DSNU compensation are the thermal characterization of the DSNU in the laboratory at multiple integration times, the correction based on continuous measurement of dark current during operation utilizing so-called \"black pixels\" within the sensor, or taking closed shutter images. When no dark pixels are available but temperature readings are, the DSNU can be characterized at multiple temperatures and integration times [22; 126]. For sensors where neither dark pixels nor temperature readings are available, the DSNU might be estimated by taking pictures with blocking the lens under the same conditions as during the image capture [40; 43; 68]. Preferably, this should be combined with an analysis of the DSNU variability during operation [127] or with integration time [50].  
The optical path of a camera alters the incoming radiant flux (vignetting, c.f. [132; 133]), and different pixels transform it non-uniformly to an electric signal. To normalize these effects, modeling the optical pathway or image-based techniques can be performed. For the latter approach, both a simpler and more accurate approach [134], a uniform target such as an integration sphere or homogeneously illuminated Lambertian surface is measured, and a look-up-table (LUT) or sensor model is created for every pixel. [PERSON] et al. [40] and [PERSON] et al. [38] performed non-uniformity normalization for their pushbroom systems by taking a series of images of a large integrating sphere illuminated with a quartz-tungsten-halogen lamp. [PERSON] and [PERSON] [50] and [PERSON] et al. [135] determined a per-pixel correction factor look-up-table (LUT) using a uniform, spectrally homogeneous, Lambertian flat field surface for the mini-MCA and the MAIA multispectral cameras, respectively. [PERSON] et al. [127], [PERSON] and [PERSON] [126], and [PERSON] et al. [129] used an integrating sphere to perform the non-uniformity normalization and determined the sensor's linear response range by measuring at different integration times. [PERSON] et al. [43] and [PERSON] et al. [129] determined the vignetting correction with a Lambertian panel in the field. [PERSON] et al. [86] presented a simplified approach to the non-uniformity and photo response normalization by using computer vision techniques.  
Finally, while dark signal correction is relatively easy as long as a sensor provides temperature readings, the vignetting correction might be challenging in practice. Large integration spheres are expensive, and small spheres might not provide homogeneous illumination across the sensor's FOV. On the other hand, when using a Lambertian surface, such as a radiometric reference panel, it is challenging to illuminate the whole target homogenously.