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Image Processing
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Comparison Of Two Texture Features For Multispectral Imagery Analysis
3. Neural Network Classification
Multi-layer perceptrons are feed-forward networks with one or more layers of nodes between the input and output nodes. These additional layers contain hidden units or nodes that are not directly connected to both the input and output nodes. The capabilities of multi-layer perceptrons stem from the nonlinearities used within nodes. The behavior of a multi-layer network with nonlinear units is complex. A multi-layer network can be trained with a back-propagation learning algorithm (Rumelhart et al., 1986). Learning via back-propagation involves the presentation of pairs of input and output vectors. The actual output for a given vector is compared with the desired or target output. If there is no difference, no weights are changed; otherwise, the weights are adjusted to reduce the difference. This learning method essentially uses a gradient search technique to minimize the cost function that is equal to the mean square difference between the desired and actual outputs. The network is initialized by setting random weights and thresholds, and the weights are updated with each iteration to minimize the mean squared error.
The back-propagation training algorithm is an iterative gradient algorithm designed to minimize the mean square error between the actual output of a multi-layer feed-forward perceptron and the desired output. It requires continuous differentiable non-linearities. (Dulyakarn et al., 2000).
4. Experimental Results
The multispectral image used in this experiment is ADEOS, which composes of 3 bands in the size of 256´256 pixels. This tested area, in Figure 1(a), is a region of Bangkok, Thailand. This image must be removed or decorrelates an interband correlation before using this data to be an input data by using Karhunen Loève Transform (KLT) (Dulyakarn et al., 1999). The transformed image is shown in Figure 1(b). Using the two textural analyses, gray-level co-occurrence matrix and Fourier transform, by giving the detail in the above subheading we can calculate two texture features. The supervised classification, multi-layer perceptron using back-propagation algorithm, is applied for classifying in this research. The classification results considered from two texture features are compared and shown in Table 1. The classified images can be compared by noticing from Table 1 and concluded that the texture feature using gray-level co-occurrence matrix give the result better than Fourier transform.
(a)
(b)
Figure 1 The ADEOS acquired from the region of Bangkok, Thailand.
(a) An original multispectral image.
(b) The first principal component image resulted from KLT.
Table 1 Classification results.
|
Class |
Number of Tested Pixels |
Gray-level Co-occurrence Matrix (%) |
Fourier Transform (%) |
| 1 |
1273 |
85.53 |
81.34 |
| 2 |
773 |
76.45 |
74.16 |
| 3 |
782 |
70.47 |
67.58 |
5. Conclusion
A comparative study between two texture features derived from gray-level co-occurrence matrix and Fourier transform has been proposed. The experimental results displayed in percentage of correct classifications make the evidence that the texture analysis using gray-level co-occurrence matrix method provides the better result than texture feature derived from Fourier transform.
Acknowledgement
The authors wish to thank the National Research council of Thailand (NRCT) for providing the satellite image data.
References
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Dulyakarn, P., Rangsanseri, Y., and Thitimajshima, P., 2000. Textural classification of urban environment using gray-level co-occurrence matrix approach. In 2nd International Conference on Earth Observation and Environmental Information, Cairo, Egypt.
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