Shen-En Qian

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In this paper, a new noise reduction algorithm is introduced and applied to the problem of denoising hyperspectral imagery. This algorithm resorts to the spectral derivative domain, where the noise level is elevated, and benefits from the dissimilarity of the signal regularity in the spatial and the spectral dimensions of hyperspectral images. The(More)
A fast search method for vector quantization is proposed in this paper. It makes use of the fact that in the generalized Lloyd algorithm (GLA) a vector in a training sequence is either placed in the same minimum distance partition (MDP) as in the previous iteration or in a partition within a very small subset of partitions. The proposed method searches for(More)
In this paper, we propose a new nonlinear dimensionality reduction method by combining Locally Linear Embedding (LLE) with Laplacian Eigenmaps, and apply it to hyperspectral data. LLE projects high dimensional data into a low-dimensional Euclidean space while preserving local topological structures. However, it may not keep the relative distance between(More)