Abstract
An image feature extraction method, generalized 2-dimensional clustering-based discriminant analysis (G2DCDA), is proposed. This method can compress images along both rows and columns, and thus overcoming the drawbacks of too large feature matrices of 2DCDA. In addition, similar to 2DCDA, G2DCDA not only has the computational advantages of the 2D subspace methods available, but can deal with the multimodal distribution problems. Experiments conducted on the moving and stationary target acquisition and recognition (MSTAR) public database demonstrate that G2DCDA is more efficient than some 2D existing subspace methods, such as 2D principal component analysis (2DPCA), 2D linear discriminant analysis (2DLDA) and 2DCDA. Moreover, G2DCDA achieves higher recognition rates and lesser memory requirements.
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Hu, L., Liu, H., Zhou, P. (2010). SAR Images Feature Extraction and Recognition Based on G2DCDA. In: Zeng, Z., Wang, J. (eds) Advances in Neural Network Research and Applications. Lecture Notes in Electrical Engineering, vol 67. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-12990-2_34
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DOI: https://doi.org/10.1007/978-3-642-12990-2_34
Publisher Name: Springer, Berlin, Heidelberg
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