734 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
Unsupervised spectral sub-feature learning for hyperspectral image classification
Spectral pixel classification is one of the principal techniques used in hyperspectral image (HSI) analysis. In this article, we propose an unsupervised feature learning method for classification of hyperspectral images. The proposed method learns a dictionary of sub-feature basis representations from the spectral domain, which allows effective use of the correlated spectral data. The learned dictionary is then used in encoding convolutional samples from the hyperspectral input pixels to an expanded but sparse feature space. Expanded hyperspectral feature representations enable linear separation between object classes present in an image. To evaluate the proposed method, we performed experiments on several commonly used HSI data sets acquired at different locations and by different sensors. Our experimental results show that the proposed method outperforms other pixel-wise classification methods that make use of unsupervised feature extraction approaches. Additionally, even though our approach does not use any prior knowledge, or labelled training data to learn features, it yields either advantageous, or comparable, results in terms of classification accuracy with respect to recent semi-supervised methods
Spatial-Spectral Manifold Embedding of Hyperspectral Data
In recent years, hyperspectral imaging, also known as imaging spectroscopy,
has been paid an increasing interest in geoscience and remote sensing
community. Hyperspectral imagery is characterized by very rich spectral
information, which enables us to recognize the materials of interest lying on
the surface of the Earth more easier. We have to admit, however, that high
spectral dimension inevitably brings some drawbacks, such as expensive data
storage and transmission, information redundancy, etc. Therefore, to reduce the
spectral dimensionality effectively and learn more discriminative spectral
low-dimensional embedding, in this paper we propose a novel hyperspectral
embedding approach by simultaneously considering spatial and spectral
information, called spatial-spectral manifold embedding (SSME). Beyond the
pixel-wise spectral embedding approaches, SSME models the spatial and spectral
information jointly in a patch-based fashion. SSME not only learns the spectral
embedding by using the adjacency matrix obtained by similarity measurement
between spectral signatures, but also models the spatial neighbours of a target
pixel in hyperspectral scene by sharing the same weights (or edges) in the
process of learning embedding. Classification is explored as a potential
strategy to quantitatively evaluate the performance of learned embedding
representations. Classification is explored as a potential application for
quantitatively evaluating the performance of these hyperspectral embedding
algorithms. Extensive experiments conducted on the widely-used hyperspectral
datasets demonstrate the superiority and effectiveness of the proposed SSME as
compared to several state-of-the-art embedding methods
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