Full text: Proceedings, XXth congress (Part 7)

ul 2004 
International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol XXXV, Part B7. Istanbul 2004 
Two scenes HRV SPOT from May 27, 1986 and respectively 
April 27 ,1993 used has been. Their characteristics in table 1. 
are presented. 
  
  
  
  
  
Acquired date May 27, 1986 | April 27, 1993 
Bands 1] 2 3 ] 23.3 
K /J 98 / 259 98 / 259 
Resolution (meters) 20 x 20 20 x 20 
Rectification (pixels) i 0.391 + 0.275 
  
  
  
  
  
Table 3. The satellites data characteristics 
3. METHOD 
The change detection realised with aids of multispectral data 
need: the preliminary processing, classification and specifically 
proceedings post-classification (Jensen et al, 1995) In this 
study was applied two methods: normalized difference 
vegetation index (NDVI) image differencing and a princial 
component analysis. 
3.1 Preliminary processing 
One from important temporal reason for change-detection is the 
acquiring month of the imagery. Images acquired during the 
period with power sun light, present a very good contrast 
between various details (Jansen, 1993). For example, in this 
case, the contrast from covered with vegetation soil and 
uncovered is very strongly. Using the scene acquired in the 
same years period are suggested to change-detection with 
purpose to reduce the problems which appear because sun-angle 
difference, vegetation-phenology changes and differences in 
soil-moisture. 
In this study for radiometric processing the technique based on 
reference image are used (Hall et al., 1991). Image of 1986 has 
been thought, because its minimum values are weakly. These 
techniques apply the standardisation in respect with reference 
image and adjust the calibration sensor errors. Also she 
adjusting the effects due to atmospheric differences and lighting 
between images. For this the fifteen pixels in each zone more 
darkness (near to level 0), respectively more brightness (near to 
255) has been choose. Radiometric transformation which tie the 
two values of two images have the form y(i) = a(i) + b(i) * x (i), 
where y (i) is pixels radiometric value from reference image 
and x(i) is the correspondent radiometric value pixels of 
corrected image. 
After the radiometric correcting, the geometrically rectified 
images have been, so that the same pixel at one date overlaps 
the same pixel for the other date. The accuracy of change- 
detection is directly conditioned of geometric rectification. For 
this operation twenty ground control points obtained by 
aerotriangulation was used. The aerotriangulation is performed 
on aerial photos at 1:20,000 scale acquired in 1990. The root- 
mean-square (RMS) error at rectification of the two images was 
under 0.20 pixels. 
For resampling of images was applied the method of the nearest 
neighbour. 
3.2 Composite images generation 
In principal component analysis of images stand out restrain of 
first principal component of each image. In means this is 7696 
of input data variance. Also, and the second principal 
765 
component was retained, which the value represent 2196 of 
variance. For each image at this two principal component was 
added the Normalized Difference Vegetation Index (NDVI), 
calculated with ecuation: 
NDVI « ( XS3— XS2)/( XS34 XS241)* 128 (1) 
Using of this allowed clear to distinguish of vegetation covered 
zones of the uncovered terrain zones. This index are 
advantageously for separation between the areas with very rich 
vegetation and the areas with the mobile and quasi-mobile sand 
correspondingly to river and sea banks (Letea, C.A.Rosetti, 
Schiopu, Räducului, Caraorman, Lumina, Lat, Rosu, Puiu, 
[vancea, Säräturile, Cerbului and Câsla Vädanei) or sea 
beaches. Also, facilitated the separation of very little and fine 
town structures from locality zones of vegetation (Sulina, Letea, 
C.A. Rosetti, Sfistofca, Crisan, Mila 23, Sf. Gheorghe, 
Murighiol, Dunavätu de sus and Dunavätu de Jos). 
3.3 The post-clasification comparison and change-detection 
Using of the post-classification comparison methods is 
advantageously, because the image acquired at different times 
are separately classified. This method allow minimising the 
effects dues to different atmospherically conditions and using 
different sensors for multispectral images acquiring (Singh, 
1989). Thus different studies showed that by association of 
mode filtering with classification procedures are possible the 
improve accuracy of change-detection (Jansen et al., 1993). 
Mode filter applied in 3 by 3 neighbouring with a threshold 
value in generally three, allows to suppress the isolated pixels 
or poor classified or the pixels dues to noise (Jansen et al., 
1993). It replaces central value pixels by a majority value. 
Majority threshold corresponds to threshold of which going, 
majority value replace central value pixels. 
For classification was applied a hibryd classification process. In 
this technique by clustering are optimised defining the sample 
classes, which will be used in the supervised classification 
process. 
For spectral signature file establishing first was appllied a 
clustering method (Isodata from Erdas). Afterwards, the file to 
supervised classification realised by maximum likelihood has 
been. 
The classification of composite images from 1986, respectively 
1993 was performed in accordance with seven classes: 
uncovered soil, stagnant water in lakes, running water, sea 
water, compactly common reed, mixture common reed-mace 
reed, town structures. The mode filtering technique was applied 
to the two classified images in a 3 by 3 neighbouring. 
The accuracy classification from two date was estimated by 
using for each the standard, single-date, qualitative accuracy 
assessment procedures (i. e., an error matrix and Kappa 
analysis) (Congalton, 1991). 
The reference data was extracted of pedologychal map, 
vegetation's map and topographical map of the Danube Deltas. 
The images classification from 1986 have been one generally 
accuracy of 79% and 0.46 Khat value, while for 1993 images 
was optained 76% and respectively 0.41. 
For change-detection the classified images was compare by 
differencing. 
 
	        
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