Proceedings of the Symposium on Global and Environmental Monitoring: Proceedings of the Symposium on Global and Environmental Monitoring

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Persistent identifier:: 856665355

Title:: Proceedings of the Symposium on Global and Environmental Monitoring

Sub title:: techniques and impacts ; September 17 - 21, 1990, Victoria Conference Centre, Victoria, British Columbia, Canada

Year of publication:: 1990

Place of publication:: Victoria, BC

Publisher of the original:: [Verlag nicht ermittelbar]

Identifier (digital):: 856665355

Language:: English

Document type:: Multivolume work

Volume

Persistent identifier:: 856669164

Title:: Proceedings of the Symposium on Global and Environmental Monitoring

Sub title:: techniques and impacts; September 17 - 21, 1990, Victoria Conference Centre, Victoria, British Columbia, Canada

Scope:: XIV, 912 Seiten

Year of publication:: 1990

Place of publication:: Victoria, BC

Publisher of the original:: [Verlag nicht ermittelbar]

Identifier (digital):: 856669164

Illustration:: Illustrationen, Diagramme, Karten

Signature of the source:: ZS 312(28,7,1)

Language:: English

Usage licence:: Attribution 4.0 International (CC BY 4.0)

Editor:: International Society for Photogrammetry and Remote Sensing, Commission of Photographic and Remote Sensing Data

Publisher of the digital copy:: Technische Informationsbibliothek Hannover

Place of publication of the digital copy:: Hannover

Year of publication of the original:: 2016

Document type:: Volume

Collection:: Earth sciences

Chapter

Title:: [THP-1 HIGH SPECTRAL RESOLUTION MEASUREMENT]

Document type:: Multivolume work

Structure type:: Chapter

Chapter

Title:: AIRBORNE IMAGING SPECTROMETER DATA ANALYSIS APPLIED TO AN AGRICULTURAL DATA SET. K. Staenz and D. G. Goodenough

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radiometric preprocessing steps which are described in more detail in the following sections. Sensor Performance The PMI sensor consists of five cameras, each with an area detector array consisting of 385 pixels by 288 colour elements (bands). Each of the cameras provide eight profiles for which each profile element is an average over 10 spatial pixels with a 38-pixel gap between profiles. Due to problems with the first camera during the overflight, the data from this camera could not be incorporated for further processing. Furthermore, each third profile of the remaining four cameras was lost with respect to the transcription of the data from the high density digital tape to CCTs. The remaining 28 profiles, consisting of 773 lines, were examined for bad data in the spectral and spatial domains. Figure 2 shows some examples of spectra with bad data. Bad bands were located in profiles 9 (bands 113-137), 14 (279), 16 (281- 288), and 21 (268) and replaced with the average of the adjacent bands. A correction, however, was not applied for profiles 9 and 16 which had 5 or more contiguous bad bands. These particular bands were not included for data interpretation purposes. The next step was to replace bad data lines within a specific camera by the average of the adjacent lines. In addition, the first 20 bands of each of the remaining profiles were eliminated from further processing due to bad data, leaving 268 bands for subseguent analysis. An estimate of the random noise which is image independent resulted in a signal-to-noise ratio (SNR) for band 247 (751.21 nm) of 140:1 for a low signal level (DN=500; 100.4 x 10" 8 W cm" 2 nm" 1 sr" 1 ) and of 1380:1 for a high signal level (DN=15000; 3013.1 x 10“ 8 W cm" 2 nm" 1 sr" 1 ). Similar SNRs were obtained for the other bands. A "laboratory" method was used for SNR calculations as described in Borstad et al. (1985). Preliminary investiga tions indicate that fixed pattern noise could not be detected within the image, especially for bands with low signal amplitude as reported in the aforementioned reference. A further study, however, will be conducted in the future using a fast Fourier transform for each band to reveal potential noise pitch and 'notch filtering' in the frequency domain to remove major noise components if necessary (Curran and Dungan, 1988; Hlavka, 1986). The central wavelength (band) locations were checked using the position of the atmospheric absorption features calculated with the 5S radiative transfer code (Tanre et al., 1986). Major and minor absorption features, for example at 590 nm (0 : ,, H 2 0), 690 (H^O, O*), 720 (H*0), and 760 nm (HzO, Oa), could be detected in terms of the wavelength position at 589.53 nm, 687.32, 718.61, and 760.34 nm for the different PMI cameras. The disagreement in the location of the absorption features is due to the minimum 5 nm wavelength interval allowed in 5S compared to a 1.3 nm sampling interval for the PMI sensor. Calibration An integrating sphere is used as a standard calibration source for the PMI sensor, resulting in a set of multipliers used to convert DN values into radiances in terms of W cm" 2 nm" 1 sr“ 1 . A total of 288 radiometric calibration values are provided (one for each band) along with the image data. Besides the removal of the dark current from the PMI data, a uniformity (radiometric) correction, assuming detector linearity, was also applied for the alignment of the different cameras (Borstad et al., 1985). The compensation for the different sensitivities of the cameras was not fully achieved with the uniformity technique because the detector responses are, in general, non-linear. Further corrections were necessary using a polynomial regression approach for a total radiometric adjustment of the cameras. This method is based on using targets which cover two or more cameras. Such a so-called standard target should be homoge neous. At least 10 such homogeneous, standard targets encompassing the DN range of the scene to be corrected were selected in order to generate the polynomial equation for each band (Figure 3). Thus, a separate correction was applied to each of the 16-bit DN levels for a compensation of the non-linear behaviour of the DN differences between cameras in roost of the bands. A second degree polynomial was used to remove the DN differences between the 'nadir' camera 3 and cameras 2, 4, and 5 for each of the bands. Satisfactory results could be achieved with this method as shown in Figure 4 for a sugarbeet field, located within cameras 4 and 5, before and after radiometric alignment. The effectiveness of this adjustment technique depends on the ability to select enough standard targets for the generation of the polynomial curve as described in detail by Staenz (1990). This camera adjustment technique also addresses some of the viewing angle effects which are quite severe for a scan angle range of approximately 72.5 degrees. This effect is demonstrated in Figure 5 using corn data acquired with a Spectrascan spectrometer on the ground over a viewing angle range of ±24.3 degrees. The varia tion caused by the viewing angle in one specific camera (14.5 degrees FOV), however, could not be removed with the correction of the radiometric misalignment of the cameras. An assessment of the eight profiles within a specific camera show various degrees of data variation (especially in camera 4), caused not only by the viewing angle phenomenon, but also by sensor related effects (CCD responsivity). An algorithm will be imple mented in the ISDA software package in the future to deal with these within-camera effects. The removal of scene-related effects, such as those due to the atmosphere or viewing geometry, are important preprocessing steps for a normalization of the image data in an absolute sense. Such steps are necessary for a spectro scopic analysis of the data, especially to incorporate the generated spectra into a database for comparison with laboratory spectra, field spectra and spectra derived from other imaging spectrometers. An absolute normalization procedure consisting of an airborne version of the 5S radiative transfer code was applied to the PMI data set (Tanre et al., 1986; Teillet, 1989). This procedure begins with a conversion of the raw data into radiances using the radiometric calibra tion values delivered with the data set. A comparison with ground-based spectra of similar targets, however, indicate that the resulting reflectances are, on average, approximately a factor of two too high. The performance of this procedure is satisfactory for Airborne Visible/ Infrared Imaging Spectrometer (AVIRIS) data as demonstrated in Staenz and Goodenough (1989). Based on the results usually achieved with the atmospheric model and extensive investigations

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