Empirically Comparing Two Dimensionality Reduction Techniques - Pca And Fft: A Settlement Detection Case Study In The Gauteng Province Of South Africa
2019 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM (IGARSS 2019)(2019)
摘要
In this paper we present a class label agnostic dimensionality reduction comparison framework. We illustrate the usefulness of this framework at the hand of a case study. For our case study, we consider two prominent land cover classes in the Gauteng province, namely natural vegetation and settlement using an 8 year MODIS dataset. We use the framework to compare two feature extraction techniques, namely PCA and FFT. For the case study we considered in this paper, the PCA technique produced a reduced feature space which was 15% more separable than the feature space produced by the FFT method.
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关键词
Principal Component Analhysis (PCA), harmonic analysis, hypertemporal remote sensing
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