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

Document type:: Multivolume work

Structure type:: Chapter

into the model, it is most likely that the radio- metric calibration data provided by Moniteq are too high for all of the bands. Another normaliza tion approach should, therefore, be used to convert the raw data directly into reflectances without using the calibration data. For this purpose, different parameters have to be included in the correction procedure; namely, the reflectance of the water body in the scene plus the extinction coefficients derived from the atmospheric model. Other options available in the ISDA software are relative normalization techniques such as flat field normalization, equal energy normalization, and the apparent reflectance approach (Roberts et al., 1986; Jet Propulsion Laboratory, 1985; Iqbal, 1983). For our classification purposes for this paper, however, it was not necessary to normalize the data. Additional problems were encountered applying the radiometric calibration values to the data set. A substantial increase of the noise level in the spectral domain could be detected, especially within the first 100 bands (430 - 560 nm). A reduction of the noise can be achieved due to the utilization of a cubic spline algorithm or the averaging of the signal in adjacent bands. The second method results in a noise reduction of /2, but it allows the possibility that some informa tion in the spectral domain is lost, depending upon the band characteristics (sampling interval, bandwidth) and the data analysis to be performed. Averaging of two adjacent bands is not that critical for the PMI data because of the narrow sampling interval (1.3 nm) and the overlapping of the bands (1.3 nm). Aircraft Attitude Variations Geometric distortions of the PMI data caused by aircraft motion (pitch, roll, and yaw) could not be compensated for due to the lack of navigation data (an inertial platform was not available) and the nature of the data acquisition process. As mentioned earlier, the PMI sensor works in a rake mode providing forty profiles and not a contiguous image. Thus, for example, it is not possible to shift an image line for a specific number of pixels in order to account for the distortion caused by the roll parameter. For the purposes of this study, a geometrically rectified data set was not necessary in order to combine the ground information with the PMI data because of the obvious location of the agricultural objects in the image. CLASSIFICATION Classification of high-resolution spectral data is a challenging task due to the large number of bands (up to 288) involved. Analysis methods are required that take advantage of the high spectral resolution to extract the most information while minimizing the computation time. Two possible classification methodologies are: (1) full spectrum classification; and (2) feature selection, followed by classification. An example of the first method (Piech and Piech, 1989; Mazer et al., 1987) combines techniques for a symbolic representation of the spectrum derived for each pixel with similarity measures used for classification purposes. The second method uses techniques and transformations for data reduction and extraction of the most useful information (Rundquist and Di, 1989). This approach permits one to use image classification software already implemented in existing image analysis systems. A data reduction procedure, band-moment (Rundquist and Di, 1989), was applied to band PMI data set in order to reduce the dimensionality. The formulae for the case are: Band moments: 1 N M„ = — E [i*> * f(i)] i=l analysis the 268- spectral discrete (1) Mean: M, M„ Central band moments: 1 N P* = E [(i-i) p *f(i)] i=l Skewness : Yi = P 3/2 Kurtosis: Y* = P* Concentrated-band moment : 1 N P~ = -5- E [ (i- I i-i I T*f(i)] i=l (2) (3) (4) (5) (6) where p = 0,1,2, ... is the moment order; N is the number of bands; i = 1, ... N, is the band number; and f(i) is the pixel intensity in DN for band i. Since the bands are evenly spaced, one can use either band number or the wavelength for this calculation. This technique was applied in the spectral domain on each pixel resulting in eight central band moments as follows: (1) mean, (2) ordinary moment (Mo), (3) variance (p 2 ), (4) 3rd moment, (5) 4th moment, (6) skewness, (7) kurtosis, and (8) band-concentrated moment(p 2ci ). The computed real values of these eight moments were then transformed linearly into an 8-bit data set. In five of the eight bands, the images seem to be similar. However, an improvement in the information content, especially for the agricul tural objects, could be detected when compared with original single-band or three-band data sets. The images for the moments 4, 6, and 7 are different and appear visually to have less infor mation content. A feature selection procedure, the branch and bound algorithm as described by Goodenough et al. (1978), was used to determine the globally best subset of bands involving the following classes (sizes in pixels): wheat (230), barley (155), corn (232), potatoes (94), fallow (151), mixed grass (239), water (212), and forest (234). The results for these training data are shown in Table 3. The best four-band subset was (1,6,7,8), corresponding, respectively, to the following moments: mean, skewness, kurtosis, and the band concentrated moment. The average pairwise trans formed divergence of the selected classes was used to select the best subset. For each subset, a maximum likelihood classification was carried out, resulting in the weighted mean classification accuracy and standard error of the mean (s.e.m.) given in Table 3. The classification accuracies were weighted by the frequencies of occurrence of each class. 554

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