Suggestions for Future Work

1. In this thesis, we just employ the support vector machine to avoid the influence of Hughes phenomenon on high dimension data. Dimensionality reduction, including feature extraction and feature selection, is another algorithm for mitigating the influence of Hughes phenomenon. Hence, in the future, researchers may apply the dimensionality reduction technique to avoid this research problem.

2. According to this study, the experiments are shown applying spatial information to the classification process is an effective way to mitigate the influence of similar spectral properties. In the future, applying spatial information into other classification technique to develop a novel algorithm is an important issue.

3. From the chapter 4, the reference algorithm, Spectral–Spatial classification based on partitional clustering techniques, is an effect approach on the classification problem of large spatial structure area, but it can’t overcome the small spatial structure problem. The most important problem is the clustering

techniques can’t work well in the small spatial structure area, and hence researchers, which want to focus on the small spatial structure problem, can develop a clustering algorithm to overcome this problem.

4. To use other high dimensional data with huge dimensionality such as face recognition data to test the completeness and suitability of the automatic algorithm and SCSVM.

APPENDIX A: THE TEST OF “SECTOR” UNIT

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