×

You are using an outdated browser that does not fully support the intranda viewer.
As a result, some pages may not be displayed correctly.

We recommend you use one of the following browsers:

Full text

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

Table 1. Definitisn of cover classes
2.6 Preprocessing
514
Cover classes Description Area-1 Area-2
Forest
Land covered by
trees (woody plant
with single stem
and more than 5m.
high), minimum
area 2 ha.
Closed forest
Mixed broad-lea- V'
ved forest, ca
nopy closure>50%
V
Open forest
Canopy closure, V
20%-50%
V
Dense mixed
bamboo forest
Broad-leaved spe
cies mixed with
bamboos, canopy
closure 750%
V
Scrub
Vegetation type,
the main woody
elements of which
are scrubs of
more that 50cm.
and less than 5m.
height. They are
woody plants with
multiple stems of
branching near
the ground.
V"
Grassland
Land covered by ч/
grass and her
baceous plants,
maximum height
lm.
Shifting
Area which is v"
cultivation under active
cultivation,
freshly burned
or abandoned
but regrowth
is not more
than two years
old.
Regrowth Areas left fallow
long enough after
shifting cultiva
tion for vegeta
tion to regene
rate .
Bare soil Land surface
devoid of vege
tation cover<20%.
It includes built
areas.
Vindicates cover class belonging to either
Area-1 or Area-2.
2.5 Digital image processing
Digital image processing was done on a
microcomputer based interactive image proce
ssing system housed in the Department of
Geography, University ©f Reading,England
with indigeneously developed software.
Preprocessing of Landsat-2 MSS data was un
dertaken in order to improve the image qua
lity. 'Destriying' of the images was carried
out by a histogram normalisation technique.
Corrections for 'bit-slips' were also applied,
3 CLASSIFICATION ANALYSIS
3.1 Supervised classification
A supervised multispectral image classifi
cation procedure based on the minimum dis
tance to means (Euclidean distance) algori
thm was used in the study. In this classi
fier a distance is computed for each pixel
vector from the class means and the pixel
is assigned to the class with the nearer
means. Since this classifier is a special
case of a more general maximum likelihood
classifier and computationally can be pro
grammed effecièntly it was thought ta be
most suitable for implementing on the micro
computer system.
3.2 Reclassification
In the classified image, usually, there are
many isolated pixels whose classification
is different from that of their neighbours.
However, one would expect some degree of
spatial dependence in land cover from pixel
to pixel, if this spatial information can
be incorporated in the classification pro
cedure it would have the potential benefit
of improving classification by removal of
isolated inliers within homogeneous areas
(Justice and Townshend 1982). One classifi
cation smoothing algorithm is the majority
filter which is illustrated in Fig.l. A
spatial window of specified size (3x3, 5x5)
is passed through the classified image and
the center pixels classification is changed
to the majority class of the surrounding
pixels in the window. In this study the cla
ssified images were reclassified using a
3x3 majority filter.
(a) Central pixel changed
Original classification Reclassification
Class Pixels
AAA A 6 AAA
ACB B 1 A A B
CAA C 2 CAA
(b) Central pixel unchanged
Original classification Reclassification
Class Pixels
AAB A 3 A A B
CCB B 3 CCB
C B A c 3 C B A
Figure 1. The 3-by -3 majority filter
(Source: Schowengerdt 1983)
4 ACCURACY ASSESSMENT
After the classification, the results were
evaluated to get an expression of its accu
racy. In this study the accuracy assessment
was conducted using 'test sets' and confu
sion matrices. A confusion matrix is a square
V"
V \/