3,003 research outputs found
Simultaneous Spectral-Spatial Feature Selection and Extraction for Hyperspectral Images
In hyperspectral remote sensing data mining, it is important to take into
account of both spectral and spatial information, such as the spectral
signature, texture feature and morphological property, to improve the
performances, e.g., the image classification accuracy. In a feature
representation point of view, a nature approach to handle this situation is to
concatenate the spectral and spatial features into a single but high
dimensional vector and then apply a certain dimension reduction technique
directly on that concatenated vector before feed it into the subsequent
classifier. However, multiple features from various domains definitely have
different physical meanings and statistical properties, and thus such
concatenation hasn't efficiently explore the complementary properties among
different features, which should benefit for boost the feature
discriminability. Furthermore, it is also difficult to interpret the
transformed results of the concatenated vector. Consequently, finding a
physically meaningful consensus low dimensional feature representation of
original multiple features is still a challenging task. In order to address the
these issues, we propose a novel feature learning framework, i.e., the
simultaneous spectral-spatial feature selection and extraction algorithm, for
hyperspectral images spectral-spatial feature representation and
classification. Specifically, the proposed method learns a latent low
dimensional subspace by projecting the spectral-spatial feature into a common
feature space, where the complementary information has been effectively
exploited, and simultaneously, only the most significant original features have
been transformed. Encouraging experimental results on three public available
hyperspectral remote sensing datasets confirm that our proposed method is
effective and efficient
Generalized Multi-manifold Graph Ensemble Embedding for Multi-View Dimensionality Reduction
In this paper, we propose a new dimension reduction (DR) algorithm called ensemble graph-based locality preserving projections (EGLPP); to overcome the neighborhood size k sensitivity in locally preserving projections (LPP). EGLPP constructs a homogeneous ensemble of adjacency graphs by varying neighborhood size k and finally uses the integrated embedded graph to optimize the low-dimensional projections. Furthermore, to appropriately handle the intrinsic geometrical structure of the multi-view data and overcome the dimensionality curse, we propose a generalized multi-manifold graph ensemble embedding framework (MLGEE). MLGEE aims to utilize multi-manifold graphs for the adjacency estimation with automatically weight each manifold to derive the integrated heterogeneous graph. Experimental results on various computer vision databases verify the effectiveness of proposed EGLPP and MLGEE over existing comparative DR methods
Graph Embedding via High Dimensional Model Representation for Hyperspectral Images
Learning the manifold structure of remote sensing images is of paramount
relevance for modeling and understanding processes, as well as to encapsulate
the high dimensionality in a reduced set of informative features for subsequent
classification, regression, or unmixing. Manifold learning methods have shown
excellent performance to deal with hyperspectral image (HSI) analysis but,
unless specifically designed, they cannot provide an explicit embedding map
readily applicable to out-of-sample data. A common assumption to deal with the
problem is that the transformation between the high-dimensional input space and
the (typically low) latent space is linear. This is a particularly strong
assumption, especially when dealing with hyperspectral images due to the
well-known nonlinear nature of the data. To address this problem, a manifold
learning method based on High Dimensional Model Representation (HDMR) is
proposed, which enables to present a nonlinear embedding function to project
out-of-sample samples into the latent space. The proposed method is compared to
manifold learning methods along with its linear counterparts and achieves
promising performance in terms of classification accuracy of a representative
set of hyperspectral images.Comment: This is an accepted version of work to be published in the IEEE
Transactions on Geoscience and Remote Sensing. 11 page
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