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Digital Image Processing
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Combining The Spectral and Spatial Signature of Information Classes using Artificial Neural Network Based Classifier for Remote Sensing of Spatially Heterogeneous Land-Use/Land Cover System in the Tropics
Results and Discussion
Table 1 shows the summary of the classification accuracy of each of the information classes and the overall classification accuracies using the two classification methods. Evidently, the agreement between the classification results and the reference data is high using the ANN classification approach compared to the MLC. The maximum likelihood classifier approach compared to the MLC. The maximum likelihood classifier yielded 76% overall accuracy compared to 86% using the artificial neural network.
Most of the information classes in the study area have high intra-class variability due to the relied and the broad range of land cover types composing a single information class. For the forested areas, the relief contributed much to the high intra-class variability. In agricultural fields on the other hand, the variable land cover composition for each information class contributed much to this variability. A good example is the information settlement wherein the area can be composed of the land cover types bare soil, concrete roads and vegetation. Obviously, the higher the number of land cover type composition for each information class, the higher is the possibility for two or more information classes to have a common land cover composition. Rice factors resulted to the low classification accuracy when the parametric statistically based single pixel classifier was used.
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| | Accuracy (%) |
| Class | MLC | Artificial Neural Network |
| Lowland rice | 41 | 64 |
| Coconut | 81 | 86 |
| Forest | 89 | 98 |
| Grassland | 92 | 87 |
| Mangrove | 97 | 99 |
| Settlement | 70 | 78 |
| Beach sand | 31 | 75 |
| River | 39 | 70 |
| Sea water | 100 | 100 |
| Overall | 76 | 86 |
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Table 1. Summary of the classification accuracy of each of the information classes and the overall classification accuracy using the MLC and the artificial neural network classifier.
Another problem facing the single pixel classifier is the spectral response similarities between information classes. The flooded rice fields and shallow rivers are good examples of information classes which have similar spectral response pattern. Thus, it is not surprising that using the MLC rice field classification just resulted to 41% accuracy compared to 62% using the ANN classifier. This is due to the fact that even though these two classes have similar spectral response class signature. Similar results can be observed between the information classes beach sand settlement.
Figures 2a and 2b showed the classified images using the MLC and the designed ANN, respectively. The "sat and pepper" effect is observed in the classified image using the MLC. The ANN classified image does not have this problem. Furthermore, the ANN classified image has more distinct information class boundaries and appears more homogeneous.
Conclusion
The results showed that combining the spectral and spatial signatures of the information classes in satellite images using the designed artificial neural network greatly increased the classification accuracy. Furthermore, the problem of low classification accuracy due to a high intra-class variability and spectral response similarities between information classes was solved by using the designed ANN classifier. On of the drawbacks of an ANN classifier is the long time needed for the iterative training of the network. Nevertheless, once the training is accomplished is straightforward and very fast.
Figure 2. Classified images using a) the maximum-likelihood classifier (MLC) and b) the designed artificial neural network (ANN).
Reference
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