IAPRS & SIS, Vol.34, Part 7, "Resource and Environmental Monitoring", Hyderabad, India,2002 
  
  
    
    
     
   
  
  
  
  
  
    
  
   
  
    
   
     
   
    
   
  
  
  
  
  
  
     
   
  
  
Finally, for the sample A window (24025m?), Avicennia 
schaueriana Stapf & Leechman coverage was estimated in 
14992m? (62,496) and canopy gaps were estimated in 9033m? 
(37.6%). Figure 1 shows spatial distribution of photointerpreted 
features. 
Figure 3 shows the sample A window final data fusion image 
obtained by application of Kraus and Albertz technique. In this 
figure the thematic differentiation improvement between 
Avicennia schaueriana Stapf & Leechman mangle trees (dark 
green areas) and canopy gap (light green areas) can be seen. 
  
Figure 1: Field-truth representation of the sample A window. 
Green areas are Avicennia schaueriana Stapf & Leechman trees 
and yellow areas are canopy gap. Scale ~1:3700. 
2.3.2. Supervised classifications: Main goals in this fourth 
stage were determination of most accurate classification method 
when compared field-truth and sample A window, and 
automatic classification of Avicennia schaueriana Stapf & 
Leechman and canopy gap with relative coverage determination 
in sample B window. 
Four methods were applied for supervised classification: 
parallelepiped, minimum distance to means and maximum 
likelihood classification. Six Avicennia schaueriana Stapf & 
Leechman and twelve canopy gap training sites were used for 
signature development both in sample windows A and B. 
Software histogram tools allowed the determination of total 
number of pixels per class. 
Parallelepiped procedure used a z-score of 1.96. Raw distance 
and no limits in search distance were used for minimum 
distance to mean procedure. An equal 0.5 probability for each 
feature and all pixels classification were used in maximum 
likelihood routine. 
3. RESULTS 
Fused compositions shown improved outputs for an a priori 
visual photointerpretation. Differentiation between canopy gap 
and Avicennia schaueriana Stapf & Leechman trees was 
optimised when conventional visual photointerpretation was 
considered. 
Figure 2 (left) shows visible high spatial resolution sample A 
window from aerial photograph taken on November 2000 and 
(right) 900nm infrared window from image obtained on 
September 2001. These images were used as input data for data 
fusion techniques. The 900nm infrared window was used as 
brightness channel for the new data fused image while tone and 
saturation channels were extracted from visible high spatial 
resolution aerial photograph taken on November 2000. 
Figure 2: Sample A window. Visible high spatial resolution 
window from aerial photograph taken on November 2000 (left) 
and 900nm infrared window from image obtained on September 
2001 (right) used for data fusion. Scale ~1:3700. 
  
Figure 3: Sample A window. Data fusion image obtained by 
Kraus and Albertz technique. Dark green areas are Avicennia 
schaueriana Stapf & Leechman mangle trees and light green 
areas are canopy gaps. Scale ~1:3700. 
Figure 4 shows results of parallelepiped, minimum distance and 
maximum likelihood classifications based on data fusion image 
for sample A window. Green areas are Avicennia schaueriana 
Stapf & Leechman trees and yellow areas are canopy gap. As 
can be seen, parallelepiped classification was the poorest 
thematic classifier under similar given techniques conditions, 
while minimum distance and maximum likelihood 
classifications show homogeneous outputs. 
Using data fusion image as analysis base, 33.3% of the pixels in 
sample A window, and 14.3% of the pixels in sample B 
window were no classified when parallelepiped procedure with 
z-score of 1.96 was used. So parallelepiped classification 
results were not considered due to expressive percentage of 
non-classified pixels. As minimum distance to means is 
commonly applied when the number of pixels used to define 
signatures is very small better than maximum likelihood,