| for class 
  
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A COMPARISON OF CONVENTIONAL CLASSIFICATION METHODS AND A NEW | aed un 
INDICATOR KRIGING BASED METHOD USING HIGH-SPECTRAL RESOLUTION | SAUCES 
IMAGES (AVIRIS) | GEOL 
| SURFA! 
FREEK D. VAN DER MEER | 
| The are: 
International Institute for Aerospace Survey and Earth Sciences (ITC) | quatre 
Department of Earth Resources Surveys | ere d 
Research Scientist in Geological Survey | any 
350 Boulevard 1945, P.O. Box 6, 7500 AA, Enschede, The Netherlands. | Y 1g 
ISPRS Technical Commission VII | xposur 
| extent \ 
metals v 
ABSTRACT. High spectral resolution images (AVIRIS) providing detailed information on | The es 
the surface mineralogy have been used to evaluate a new indicator kriging based | area Or 
classification technique. This technique directly uses spectral information derived from of rhyol 
AVIRIS data instead of information from training areas studied in the field. A small study | rocks ar 
area of an imaging spectrometer data set covering the Cuprite mining district was selected opalizec 
for its known occurrences of both kaolinite and alunite. Three "conventional" classification | contain! 
methods were applied as well as the new indicator kriging based technique and results were | irregula 
evaluated using shape characteristics of the classes and by comparison with local field | of the : 
geologic information. Indicator kriging performed better than the conventional methods. | much a: 
Furthermore, the new indicator kriging based method provides information on the reliability | soft, pc 
of the classification which is lacking with the conventional methods. | i dd 
| rocks, p 
| to opal 
Key Words: Image Classification, Indicator Kriging, AVIRIS, Cuprite Mining District, | kaolinit 
Nevada | is treat 
| E 
INTRODUCTION the simultaneous collection of images in 224 contiguous | L 
Remote sensing of earth's surface from aircraft and from 
spacecraft provides information not easily acquired by 
surface observations. Until recently, the main limitations of 
remote sensing were that no subsurface information could be 
obtained and that surface information lacked specification. 
Conventional scanners (e.g. Landsat MSS and TM, and 
SPOT) acquire information in a few separate spectral bands 
of various widths, thus filtering to a large extent the 
reflectance characteristics of the surface (Goetz & Rowan, 
1981). Therefore, new scanner types were developed with 
high spectral resolution yielding new image processing 
techniques to cope with the increased amount of data. The 
use of indicator kriging as classification routine is discussed 
in this paper using high spectral resolution imagery although 
the technique is also valid for conventional scanner data. 
IMAGING SPECTROMETRY 
The use of high spectral resolution remotely sensed imagery 
for mineralogic mapping was first demonstrated in spectral 
laboratory studies (e.g. Hunt, 1977). In 1981, airborne 
spectrometer data were acquired using a sensor developed by 
the GER corporation for one-dimensional profiling along a 
flight line. The first imaging device was the Airborne 
Imaging Spectrometer (AIS), developed at the Jet Propulsion 
Laboratory. This instrument acquired data in 128 spectral 
bands in the range of 1200-2400 nm with a field-of-view of 
3.7 degrees (Vane & Goetz, 1985). In 1987 NASA began 
data acquisition with an improved version of AIS called the 
Airborne Visible/Infrared Imaging Spectrometer (AVIRIS; 
see Macenka & Chrisp, 1987). This scanner makes possible 
72 
bands resulting in a complete reflectance spectrum for each 
20*20 m. picture element (pixel) in the 400 to 2500nm 
region with a sampling interval of 10 nm (Goetz et al., 
1985; Vane & Goetz, 1988; Porter & Enmark, 1987). The 
field-of-view of the AVIRIS scanner is 30 degrees resulting 
in a ground field-of-view of 10.5 km. The signal-to-noise 
ratio is 100:1 at 700nm and 50:1 at 2200nm. The value of 
this scanner lies in its ability to acquire a complete 
reflectance spectrum for each pixel. Many surface materials 
have diagnostic absorption features that are 20-40nm in 
width (Hunt, 1979). Therefore, spectral imaging systems 
which have 10nm wide bands can produce data with 
sufficient resolution for resolving these features and 
subsequent direct identification of those materials (Goetz, 
1991). On the contrary, Landsat scanners, which have band 
widths between 100 and 200nm cannot resolve these spectral 
features. Analysis of high spectral resolution imagery for 
mineral identification involves three steps: (1) the pre- 
processing of the data to convert raw spectra into reflectance 
spectra corrected for atmospheric influences, (2) extraction 
of absorption features characterizing surface materials of 
interest, and (3) evaluating for each pixel whether the 
absorption feature is present or absent at the wavelength 
(Okada et al., 1991). 
This paper shows the potential use of indicator kriging based 
techniques for image classification (the third processing step 
mentioned above) of remotely sensed imagery in general and 
high spectral resolution data in particular. AVIRIS data from 
the Cuprite mining district were used to detect occurrences 
of kaolinite and alunite based on their spectral 
characteristics. Four bands defining the key absorption 
features from these minerals are subsequently used as input