M'Œdk
reflectance in certain spectral bands, as recorded by
a remote sensor channel (e.g., Landsat TM-4);
altitude of a target (e.g., derived from a digital
elevation model);
ground truth data, such as temperature or humidity
measured in situ-,
derived attributes, such as the normalized vegetation
index (Lillesand, 1987);
requested attributes, such as the age of a forest or the
degree of damage to a culture.
Target attributes describe the general properties of this
data and the paths or methods to access them. Target
attributes are characterized by the following properties:
- the role of the attribute with respect to an analysis,
such as primary, intermediate, or final;
- the possible values that may be assigned to the
attribute;
- the stability of the values of the attribute over time.
(The elevation of a location may be considered stable
over centuries, whereas other attributes such as
temperatures may change within hours.)
Target attributes are related to other knowledge-base
entries, such as the target classes for which they are
defined, or the processing models suited to compute the
respective target attribute.
Geographic Data
The case-specific manifestations of target types and
target attributes are represented by dynamically created
objects, called geographic data. Geographic data are
dependent on the geographic location and on the date of
the analysis. The most prominent example of this kind of
knowledge is the raster imagery recorded by remote
sensors or derived by image-processing operations. But if
these raster images were used as the only input into the
data analysis, the results would be unsatisfactory. It has
been shown that additional geographic information has to
be taken into account as well (McKeown, 1987). There
fore, other geographic data from the region to be analyzed
must also be represented. Examples are:
- geographic data describing the positions, shapes, and
target classes of geographic units;
- layers of a topographic map, converted into a compu
ter readable format by a raster scanner;
- a digital elevation model of the area of interest;
- ground truth data;
- results of former analyses.
The geographic data relevant to remote sensing tasks in
the environmental domain may be divided into two
categories: classifications and attributions. Classifica
tions and attributions may be considered as concrete
manifestations of abstract target classes and target attri
butes:
- A classification may be characterized as a mapping
(in the mathematical sense) defined on a part of the
earth’s surface that associates locations with target
classes.
An attribution may be characterized as a mapping
defined on a part of the earth’s surface that associates
locations with values of a certain target attribute.
Geographic data are represented by knowledge-base items
that, in addition to a file of physical data, provide meta
information for efficiently using the file. Geographic data
include the following:
- the data category (i.e., classification or attribution);
- the related target classes or target attributes expres
sed by the geographic data;
- the name of a data file, usually containing the physi
cal data in a raster-coded format as a matrix of data
bytes. The raster format was chosen for most kinds of
geographic data because it makes it easier to merge
remote sensor data and ancillary data in the analysis;
- encoding of the data bytes: a look-up table or formula
that maps the interval between 0 and 255 onto the
range of the data;
- information about the coordinate system and the grid
size of the raster data;
- processing state: raw, histogram equalized, calibra
ted, etc.;
- a timestamp.
Maps and images are similar to geographic data. They
are stored in raster files as well, and may be computed
using image-processing and spatial data handling proce
dures. Unlike geographic data, however, they combine
attributions, classifications and graphic elements in a
form that is to be understood by a human rather than in
terpreted by a computer.
Processing Models
Processing models are abstract descriptions of computa
tions. They are static knowledge-base objects that do not
change during a consultation. A model provides meta
information for the use of traditional data processing
operators, and it describes a set of input and output data
and an algorithm. The algorithm is represented by a
program which contains control structures and calls to the
image processing and geographic database subsystems.
Some of these models (such as the model for computing
the vegetation index as a simple indicator for vegetation)
are able to operate on pure remote sensor data. Other
models (such as those for computing agricultural land-
use classes by a supervised classification) need extra
information (e.g., training areas), which may be provided
by a geographic database or supplied by the user explicit
ly. Some models describe the computation of geographic
data to be used by a geographic information system, and
other models describe how to visually enhance data to
produce maps and images.
149
W'ï ,