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Dimensionality reduction and segmentation have been used as methods to reduce the complexity of the representation of hyperspectral remote sensing images. In this study, a new object-oriented mapping approach is proposed based on nonlinear subspace feature analysis of hyperspectral remote sensing images. Nonlinear local manifold learning approaches for feature extraction were utilized to obtain subspace feature representation of hyperspectral remote sensing images. Afterwards, with a proper selection of parameters, the extracted subspace feature images were fed into the object-oriented system. Texture features derived from gray level co-occurrence matrix and wavelet filter with the use of SVM classifier at the pixel level of the feature images were also used to evaluate the proposed algorithm. Experiments are conducted on the AVIRIS dataset with 220 spectral bands, covering an agricultural area. Classification results show that the proposed object-oriented subspace analysis approach can give significantly higher accuracies than the traditional pixel-level and texture-based subspace feature classification.

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