Joint solution of urban structure detection from hyperion hyperspectral images



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Hyperspectral remote sensing has shown great potential for disaster analysis. In post-disaster urban damage assessment, residential areas and buildings must be accurately identified in the images before and after the disaster. However, the traditional spectral-only or spatial-only solutions prove ineffective for residence detection from low resolution hyperspectral images, such as Hyperion data. To solve this problem, a joint solution of residential area classification, based on both spectral signature and spatial texture, is proposed in this thesis. Correlations between every pixel spectrum and the selected endmembers’ spectra and the most significant PCA (Principle Component Analysis) components of the spectral data provide spectral features of every pixel. A hierarchical Fourier Transform – Co-occurrence Matrix approach is designed to help capture spatial textures. Eight second order texture measures are calculated based on the co-occurrence matrix, and K-fold cross validation is performed on the training data to select the best combination of features for the proposed algorithm. Compared with most existing methods that focus exclusively on spectral or spatial information and rely on high spatial resolution hyperspectral images that are usually taken by airborne sensors, our solution makes use of both spectral signature and macroscopic grid patterns of the residential areas and hence works well for low resolution Hyperion imagery.



Hyperspectral image processing, Hyperion, Spectral-spatial feature extraction, Fourier transform, Co-occurrence matrix