**5. Results**

8 Will-be-set-by-IN-TECH

° ⊕ ♥ ♦

<sup>0</sup> <sup>50</sup> <sup>100</sup> <sup>150</sup> <sup>200</sup> <sup>250</sup> −1000

Fig. 7. Spectral signatures of different classes: values of selected pixels in Fig. 6 throughout

Fig. 8. Entropy of the wavelet transform of every pixel. Darker pixels indicate lower entropy

Band

Pixel Intensity

the bands.

values.

Results of compressing AVIRIS images with LAIS-LUT and the enhanced version (in which the scaling factor is calculated with pixels belonging to the same class, considering a causal neighbor of 4 × 4 pixels around the one to be predicted) is shown in Table 3.

The name of the wavelet in the table indicates the wavelet used to transform each pixel (and all the bands) followed by the entropy estimation and mean shift classification. Compression ratio results for LAIS-LUT and LUT algorithms were obtained from (Mielikainen & Toivanen, 2008). It can be observed that the enhanced version of LAIS-LUT using Daubechies 4 for classification outperforms the other methods.

We may conclude that the scaling factor *α* plays an important role in the compression performance of LAIS-LUT algorithm. When introduced, it was intended to decrease the deterioration produced by outliers in the original LUT algorithm. We have also been able to make use of the information in the wavelet domain and apply it to develop an efficient classifier. Since hyperspectral images have many bands because of their high spectral resolution, the information of the signal that each pixel represents (in all bands) was well captured by the wavelet transform and was fed into a powerful classifier such as mean-shift, giving good compression results.
