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Title
Remote sensing for resources development and environmental management
Author
Damen, M. C. J.

Phase II: Satellite data analysis and process
The adopted scheme for the process of satellite data follows classic
criteria: the supervised classification and a maximum-likelihood classifier have
been selected because of the possibility of a strict control on the activities in
progress. The spectral and spatial resolution of the Thematic Mapper data at
European latitudes, the particular accuracy in the training stage and In the
preparation of the 'ground truth " and the availability of a sophisticated image
analysis system (hardware plus software), lead to very good results. The
processing scheme consists of six main steps:
a) - Image selection and preliminary process.
The selection of the satellite scenes on which to operate heavily depends
on the availability of cloud-free images In the requested season. Previous studies
show that, for forest mapping from Thematic Mapper data, the period from
mid-June to mid-August offers a good compromise between appearance of the
vegetation (phenology) and scene illuminance (due to sun azimuth and elevation).
For classifying coniferous forest cover, winter data are much better than any
other season, but the possibility of a spectral discrimination Is also good enough
In summer, as shown in the accuracy evaluation section.
Satellite scenes are acquired in the raw format stored on CCT. Different
radiometric and geometric corrections are applied in the phases of the process.
For classification purposes, the geometric correction doesn't include resampling
with cubic convolution methods, which may affect the radiometric response.
When necessary, athmosferic refraction and scattering effect (haze) can be
locally removed with statistical algorithms. For the preliminary photographic
output, oriented to the photointerpretation, more complex algorithms are used.
b) - False colour image production
False-colour images of the Landsat Thematic Mapper data for the entire
area of interest are produced and output on photographic support for preliminary
photointerpretation and area stratification. Among the possible Thematic Mappei
band combinations the following have been selected (respectively in red, green,
blue shadows):
- 5-3-1 to enhance terrain morphology
- 4-3-2 for vegetation monitoring
- 7-4-3 for land-use.
Geometric correction, with cubic convolution resampling and North-South
image rotation, and edge enhancement is applied to these images to improve the
readability and the allocation of the test areas.
c) - Scene stratification
In order to increase the accuracy of the classification, the image is divider
in small zones of defined characteristics. This procedure is called
"stratification" and the single zones "strata ". The aim is to divide the area intc
subzones relatively homogeneous with respect to the spectral response; this
should grant the extension of spectral signatures within the smaller area. The
validity of a spectral signature is reduced to few kilometers when processing
Thematic Mapper data over a montainous area. For each stratum a separate
classification Is executed, using different signatures and different test areas. A
post-classification analysis aggregates spectral classes that are
stratum-dependent. Aerial photos, topographic maps and Landsat false colour
Images (low and medium scale) are used as control data for breaking out these
strata. Percentage of vegetation cover, percentage of bare soils, land features arc
also used as stratification factors. The stratification is applied to the digital
data stored on disk using a bit-map description language which masks the
portions of image to be excluded from the process. This procedure increases the
cost of processing phase because of the number of separate classifications and o'
the final aggregation, but the stratification, when working over large areas,
minimizes the "variance", or error due to sampling, in the final estimates.
d) - Test area registration
The portions of area for which "ground truth ' is available ( aerial photos ot
tophographic maps interpretation, direct survays Information) are marked as
"test areas". The allocation of such areas on the image is done manually on the
available false colour prints, and interactively on the video display, where the
image can be presented at the right scale and projection.
e) - Stratum classification
The classification stratum by stratum follows a classic supervised
scheme, and consists of three passes:
- selection of training sets over a test area, local evaluation and
refinement;
- test of the training set over other test areas of the stratum, evaluation
and eventual iteration of first pass;
- classification of the entire stratum and accuracy assessment.
The procedure requires several iterations of the passes; a special
emphasis has been therefore put, when designing and developing the Image
analysis software package, in the contort of the man-machine interaction and ir
the power of training set handling facilities. A maximum-likelihood classifier
has been selected and used for the present application. The training areas were
delineated on the display in a interactive manner. Decisions to merge or delete
training sets were based upon the analysis of statistical parameters, of
two-dimensional histograms and confusion matrices (see Tab. 3). A powerful
software tools helped in individuating pixels In the training sets causing
misclassifications and in their exclusion; this leads to spectral signatures thal
’■locally" are as "pure" as possible. Several training sets for each class are
extracted, trying to reproduce the various spectral aspects of a given category
within the stratum. Density of the vegetation, sun exposition, terrain slope, haze
presence are taken into account; they heavily affect the response of the
vegetation. The result is a large number of spectral classes used for the
classification and grouped later. The use of a powerful computer reduces the
impact of such approach on process time. All the reflective Thematic Mapper
bands (1 through 5 and 7) are used for the spectral analysis; the thermal channe
(band 6) has been excluded because of the difficulty in the extraction of usefu
information.
f) - Class aggregation and final output
The final aggregation of the classes across the strata, i.e. over the entire
area will be performed using a computer look-up table procedure. The final image
pixels, when classified, are assigne to one of the possible expected category.
The classification tecnique is 'point-by-point", in which each pixel is
treated individually. This approach produces maps which may contain even more
detail than is actually needed. To avoid a "salt and pepper " effect in the present
application isolate dots have been assigned to closer classes, according to a
prevalence algorithm.
The final classification is output on a film recorder, and printed at the
scale of 1:100,000. Pixels assigned to the same class have the colour assigned tc
the category bye the final legenda. Non-classified pixels are presented in
Known
category
Number of
pixels
Percent
correct
Number of pixels
assigned to category
1 2 3 4 5 6 7 8
1
12
91.6
11
0
0
1
0
0
0
0
2
22
90.9
1
20
0
0
0
1
0
0
3
28
96.4
0
0
27
1
0
0
0
0
4
11
100
0
0
0
11
0
0
0
0
5
16
87.5
0
0
0
0
14
0
0
2
6
20
100
0
0
0
0
0
20
0
0
7
16
100
0
0
0
0
0
0
16
0
8
22
81.8
1
0
0
3
0
0
0
18
TAB. 3 - Training sets confusion matrix over a test area
1 conifers ,2-3 mixed, 4-7 conifers , 8 mixed