4,914 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
Robust Recovery of Subspace Structures by Low-Rank Representation
In this work we address the subspace recovery problem. Given a set of data
samples (vectors) approximately drawn from a union of multiple subspaces, our
goal is to segment the samples into their respective subspaces and correct the
possible errors as well. To this end, we propose a novel method termed Low-Rank
Representation (LRR), which seeks the lowest-rank representation among all the
candidates that can represent the data samples as linear combinations of the
bases in a given dictionary. It is shown that LRR well solves the subspace
recovery problem: when the data is clean, we prove that LRR exactly captures
the true subspace structures; for the data contaminated by outliers, we prove
that under certain conditions LRR can exactly recover the row space of the
original data and detect the outlier as well; for the data corrupted by
arbitrary errors, LRR can also approximately recover the row space with
theoretical guarantees. Since the subspace membership is provably determined by
the row space, these further imply that LRR can perform robust subspace
segmentation and error correction, in an efficient way.Comment: IEEE Trans. Pattern Analysis and Machine Intelligenc
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