141 research outputs found
A REVIEW ON MULTIPLE-FEATURE-BASED ADAPTIVE SPARSE REPRESENTATION (MFASR) AND OTHER CLASSIFICATION TYPES
A new technique Multiple-feature-based adaptive sparse representation (MFASR) has been demonstrated for Hyperspectral Images (HSI's) classification. This method involves mainly in four steps at the various stages. The spectral and spatial information reflected from the original Hyperspectral Images with four various features. A shape adaptive (SA) spatial region is obtained in each pixel region at the second step. The algorithm namely sparse representation has applied to get the coefficients of sparse for each shape adaptive region in the form of matrix with multiple features. For each test pixel, the class label is determined with the help of obtained coefficients. The performances of MFASR have much better classification results than other classifiers in the terms of quantitative and qualitative percentage of results. This MFASR will make benefit of strong correlations that are obtained from different extracted features and this make use of effective features and effective adaptive sparse representation. Thus, the very high classification performance was achieved through this MFASR technique
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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