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Figure 3. Endmember selection using n-d visualzer
4. RESULTS
After endmembers have been selected, comparisons can
be made between the endmember spectra ( figure.4) and
various library spectra. ENVI 3.1 provides several
spectral libraries for comparison. In our study, we have
chosen the USGS spectral library which contain a great
number of mineral and vegetation Reflectance spectra.
A pixel of coordinates (x,y) (row, line), presented by a
Reflectance spectra traced in bleu, is identified with a
mineral reflectance spectra traced in black. For example,
we could now identify some mineral components of the
Color-Ratio Composite Image of ratios 5/7, 3/1, and 2/4
represented before in Figure 3 and (Table 1)
Table I. Identification of some components of the color-
ration composite image.
Iron Oxyde
Clays Carbonate Vegetation
Illite 5 Dolomite Cuprites Lawn grass
Glaucophane Cheat grass
For vegetation, we have identified only two Reflectance
spectra, in view of the fact that Laghouat is a sub-Saharan
region (poor in vegetation).
5. CONCLUSION
Spectroscopy by satellite images brings a new conception
in remote sensing that enables the identification of the
major scene components. [t has a great potential to aid
numerous other fields of study. The success of research is
very much dependent on the quality of data, correctness of
data and the analysis techniques used. The employment of
the sequence of MNF, PPI and n-D visualizer in the study
arca allowed the identification of different mineral and
vegetation. This work showed a possible cartography of
soil occupation using objects spectral library and a
Sequential technique in processing image.
REFERENCES :
J.W. , Boardman & F A. , Kruse ; Thematic Coference on
Geologic Remote Sensing, Environmetal Research
Institute of Michigan, Ann Arbor, MI, I: 407-418; (1994);
"Automated spectral analysis: A geologic example using
AVIRIS data, noth Grapevine Mountais, Nevada".
1073
emote Sensing and Spatial Information Sciences, Vol XXXV, Part B4. Istanbul 2004
These spectra were measured on a custom-modified,
computer-controlled Beckman spectrometer at the USGS
Denver Spectroscopy Lab. Wavelength accuracy is on the
order of 0.0005 micron (0.5 nm) in the near-IR and
0.0002 micron (0.2 nm) in the visible. which we had
recourse before, we can identify a great number of
Spectra representing various minerals.
él] n_D Mean: {M1}
File Ed. Options Plot Functien
f
Figure.4. Identification of endmembers reflectance
spectra with a mineral reflectance spectra (using USGS
spectral library
en 1 PPI ({M23)-{M 3}
LIES
A
ES
d 5 T
Glaucophane
Figure 5. Score image for mineral/vegetation
endmember.
JW. , Boardman & F.A. , Kruse& R.O. Green :
Summaries of the 5nd Annual JPL Airborne Geoscience
Workshop, JPL Publication 95-1 Vol.1, pp. 23-26; (1995);
“ Mapping target signatures via partial unmixing of
AVIRIS data".
J.W. , Boardman ; Summaries, Fourth JPL Airborne
Geoscience Workshop, JPL Publication 93-26, v. 1, p. 11
— 14; (1993); "Automated spectral unmixing of AVIRIS
data using convex geometry concepts".
ENVI ® Tutorials Copyright 1993-1998 Better Solutions
Consulting LLC.
A. ‚Fred & Kruse and al. ; Presented at the Fourth
International Conference on Remote Sensing for Marine
and Coastal Environments, Orlando, Florida, 17 - 19
March 1997; "Techniques Developed for Geologic
Analysis of Hyperspectral Data Applied to Near-Shore
Hyperspectral Ocean Data".
O.A., de Carvalho Jr and al. " Sequential Minimum Noise
Fraction Use: An Approach to Noise Elimination",
Departamenteo de Geografia da Universidade de Brasilia.
