19 February 2018 Discriminant analysis in morphological feature space for high-dimensional image spatial–spectral classification
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The spectral and spatial information fusion can significantly improve the performance of hyperspectral image classification. A spectral–spatial feature extraction method, which is called morphological-based feature space discriminant analysis (MBFSDA), is proposed. In contrast to conventional feature extraction methods such as linear discriminant analysis and its different versions, which maximize the class discrimination in the spectral feature space, the proposed MBFSDA method maximizes the class discrimination in the spatial–spectral feature space. MBFSDA uses the spatial information (shape and size) of morphological profiles (MPs) in the FSDA. In the MBFSDA method, after applying the principal components analysis transformation on the hyperspectral image, an MP is created from each principal component. Each MP is considered as a multiband image that contains the spatial information instead of the spectral features. Then, in each MP, the between spatial bands scatters are maximized. As a result, different spatial features, which contain the minimum redundant information, are extracted. After that, the between-class and within-class scatter matrices are estimated using the extracted spatial features. Then, the Fisher criterion is used to maximize the class discrimination and to extract useful features for classification. The features extracted from each MP are given to a classifier. This process is repeated for each MP and eventually, the majority voting rule is used to provide the final classification map. The experimental results on three real hyperspectral images show the superiority of MBFSDA compared with some spectral–spatial classification methods in terms of classification accuracy.
© 2018 Society of Photo-Optical Instrumentation Engineers (SPIE)
Maryam Imani, Hassan Ghassemian, "Discriminant analysis in morphological feature space for high-dimensional image spatial–spectral classification," Journal of Applied Remote Sensing 12(1), 016024 (19 February 2018). https://doi.org/10.1117/1.JRS.12.016024 . Submission: Received: 17 October 2017; Accepted: 25 January 2018
Received: 17 October 2017; Accepted: 25 January 2018; Published: 19 February 2018

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