The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. Vol. XXXVII. Part B7. Beijing 2008 
230 
in terms of keeping the extent of intensity variation (RMSE and 
NMSE) small and the structural similarity (SSIM) with the refer 
ence hypercube high. Such elegantly simplified data can be used 
instead of the original noisy ones, improving the performance of 
the succeeding band selection, feature extraction and classifica 
tion procedures, especially the unsupervised ones. The AML, 
naturally, provides a simpler space for statistical modeling and 
interpretation, by preserving distinguishable data features while 
reducing spatial and spectral intensity variation. 
Moreover, the compared filtering techniques were applied in re 
moving noise from an artificially contaminated hypercube. One 
percent of the original hypercube’s pixels were contaminated with 
uncorrelated noise and then ADF, ML and AML of scale n=6 
were applied. The quantitative measures when comparing results 
with the original hypercube are presented in Table 1. The devel 
oped AML scores better in all measures approximating success 
fully the original hypercube’s intensity and structure. Last but not 
least, the AML was tested against watershed’s over-segmentation 
problem. In all performed experiments, a reduction of over a 10% 
was achieved to the number of the output segments. AML man 
aged to decrease the heterogeneity of the initial image (both in 
spectral and spatial directions) by merging pixels which belonged 
to the same object/class, impelling the sensitive watershed trans 
formation to result in fewer output segments. 
5 CONCLUSIONS 
We have introduced a novel morphological scale space repre 
sentation for denoising and simplifying hyperspectral data. Ex 
perimental results and performed quantitative evaluation demon 
strate that the developed AML can enlarge and create new flat 
zones without displacing image contours and can surpass spec 
tral spike-like features outperforming anisotropic diffusion filter 
ing and standard MLs. The algorithm is relative fast and with 
out an optimized coding, can approximately process a hyper 
cube of 200x350 pixels with 160 channels in less than a minute 
(for every scale n) in an ordinary iPentiumM 2GHz,lGB RAM. 
For real-time applications its implementation on a parallel sys 
tem is straightforward and furthermore, the algorithm can be ad 
justed and do not process the thermal infrared bands, other heav 
ily noised or selected ones. The suitable for hyperspectral data 
morphological framework, the resulting, in all our experiments, 
elegant simplification and the adequate algorithm’s performance 
encourage future research. Object-oriented hyperspectral image 
analysis, where the multiscale segmentation and classification is 
constrained by the developed AML is currently under investiga 
tion. 
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