Multi spectral, Remotely Sensed data compression method using neural networks
3. Results
The Remotely Sensed data (LANDSAT 5 TM) used in our experiment describes and area (approximately 207 km2) in Bangkok. It consists of 480 lines and 480 pixels on screen. Six channels except thermal channel were used. Thermal channel was not used due to its peculiarity in terms of instantaneous field of view. The NN model (in Figure 3) was experimented. We used 480 training data sets (sampling ratio was about 0.2%). The necessary learning time was as much as 30,000 cycle. A natural color image was used as an original image to compare other images with during the work.
3.1 Comparision of NN with pca
There is a difference in meaning between data compressed by NN and ones by pca. So, we compared six dimensional data recompressed from three dimensional compressed data by NN with ones by pca in order to determine which is superior as a data -dimensional compression method. In the NN's case, output data were used as recompressed ones. On the other hand, data recompressed by pca were generated by multiplying three dimensional compressed data by inverse matrix which was used in pca transformation.
First, as a result of calculation of coefficient, the coefficient between original data and data recompressed by NN was 0.988, while pca's one was 0.962.
Next, we generated images by using data recompressed by NN and one by pca in the same way as a natural color image, and compared them with the original image (Figure 4). The image created from the NN recompressed data was much more alike to the original image that the image by pca.
Figure 4 images generated by using data recompressed by NN and by pca and an original image
Therefore, these facts demonstrates that the NN model in this research is superior to pca as a data -dimensional compression method.
3.2 Comparision of an image using data compressed by NN with an original image
We generated an image by using three dimensional data compressed by NN (in Figure 5), then compared it with the original image (in Figure 4). It can be found that the image by NN shows such boundaries as a river and main roads original image. In the comparison. We focused on the area inside the airport (in Figure 6), because it is supposed to have almost the uniform and run length) were applied to effectively carry out the comparision.
Figure 5 Image generated using data compressed by NN
Figure 6 Areas inside an airport in an image by NN and an original image
The image kurtosis means the more data are concentrated around their mean. Shown in Table 1, the area by NN is relatively more homogeneous than the original one.
Table 1 Kurtosis Index
| |
kurtosis |
| NN |
2.593 |
| original |
2.020 |
3.2.2 run length
We examined the sequence of pixel values along the directions of q =0o,45o,135o. As a result, both cases showed the most sequence at q =0o, and the result are presented in Table 2 The indicators (such as s.r.e., etc.) show that the area by NN is more homogeneous than the original one.
Table 2 Run length index
| |
s.r.e. |
1.r.e |
g.1.n. |
r.l.n. |
r.p. |
| NN |
0.936 |
1.444 |
4.980*104 |
1.232*104 |
0.899 |
| original |
0.958 |
1.287 |
5.164*104 |
1.279*104 |
0.931 |
s.r.e. : short runs emphasis
l.r.e. : long runs emphasis
g.l.n. : gray level nonuniformity
r.l.n. : run length nonuniformity
r.p. : run percentage |
It demonstrates that the image compressed by NN is easier to visually interpret than the original one. The NN derived image loses les information from the original data, it is due to the NN method compresses all the channels of data into one three dimensional image reflecting more than three dimensional original data.
4. Conclusion
Based on the comparison, it can be concluded that the NN model constructed in out research is superior to pca as a data compression method. Moreover, images generated from data compressed by NN is more suitable for visual interpretation than its original image.
References
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K.Funahashi, On the Approximate Realization of Identity Mappings by Three-Layer Neural Networks, MBE88-174, pp109-114, 1988.
- B.Lrie and M.Kawato, Acquisition of Internal Representation by Multi-Layered Perceptrons, D-II, No. 8, pp1173-1178, 1990.
- M.Takagi and H.Shimoda, handbook of Image Analysis, University of Tokyo Presets, Tokyo, 1991.
