419 research outputs found
Locality and Structure Regularized Low Rank Representation for Hyperspectral Image Classification
Hyperspectral image (HSI) classification, which aims to assign an accurate
label for hyperspectral pixels, has drawn great interest in recent years.
Although low rank representation (LRR) has been used to classify HSI, its
ability to segment each class from the whole HSI data has not been exploited
fully yet. LRR has a good capacity to capture the underlying lowdimensional
subspaces embedded in original data. However, there are still two drawbacks for
LRR. First, LRR does not consider the local geometric structure within data,
which makes the local correlation among neighboring data easily ignored.
Second, the representation obtained by solving LRR is not discriminative enough
to separate different data. In this paper, a novel locality and structure
regularized low rank representation (LSLRR) model is proposed for HSI
classification. To overcome the above limitations, we present locality
constraint criterion (LCC) and structure preserving strategy (SPS) to improve
the classical LRR. Specifically, we introduce a new distance metric, which
combines both spatial and spectral features, to explore the local similarity of
pixels. Thus, the global and local structures of HSI data can be exploited
sufficiently. Besides, we propose a structure constraint to make the
representation have a near block-diagonal structure. This helps to determine
the final classification labels directly. Extensive experiments have been
conducted on three popular HSI datasets. And the experimental results
demonstrate that the proposed LSLRR outperforms other state-of-the-art methods.Comment: 14 pages, 7 figures, TGRS201
Kernel Multivariate Analysis Framework for Supervised Subspace Learning: A Tutorial on Linear and Kernel Multivariate Methods
Feature extraction and dimensionality reduction are important tasks in many
fields of science dealing with signal processing and analysis. The relevance of
these techniques is increasing as current sensory devices are developed with
ever higher resolution, and problems involving multimodal data sources become
more common. A plethora of feature extraction methods are available in the
literature collectively grouped under the field of Multivariate Analysis (MVA).
This paper provides a uniform treatment of several methods: Principal Component
Analysis (PCA), Partial Least Squares (PLS), Canonical Correlation Analysis
(CCA) and Orthonormalized PLS (OPLS), as well as their non-linear extensions
derived by means of the theory of reproducing kernel Hilbert spaces. We also
review their connections to other methods for classification and statistical
dependence estimation, and introduce some recent developments to deal with the
extreme cases of large-scale and low-sized problems. To illustrate the wide
applicability of these methods in both classification and regression problems,
we analyze their performance in a benchmark of publicly available data sets,
and pay special attention to specific real applications involving audio
processing for music genre prediction and hyperspectral satellite images for
Earth and climate monitoring
Novel gumbel-softmax trick enabled concrete autoencoder with entropy constraints for unsupervised hyperspectral band selection.
As an important topic in hyperspectral image (HSI) analysis, band selection has attracted increasing attention in the last two decades for dimensionality reduction in HSI. With the great success of deep learning (DL)-based models recently, a robust unsupervised band selection (UBS) neural network is highly desired, particularly due to the lack of sufficient ground truth information to train the DL networks. Existing DL models for band selection either depend on the class label information or have unstable results via ranking the learned weights. To tackle these challenging issues, in this article, we propose a Gumbel-Softmax (GS) trick enabled concrete autoencoder-based UBS framework (CAE-UBS) for HSI, in which the learning process is featured by the introduced concrete random variables and the reconstruction loss. By searching from the generated potential band selection candidates from the concrete encoder, the optimal band subset can be selected based on an information entropy (IE) criterion. The idea of the CAE-UBS is quite straightforward, which does not rely on any complicated strategies or metrics. The robust performance on four publicly available datasets has validated the superiority of our CAE-UBS framework in the classification of the HSIs
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