Topic
Sparse approximation
About: Sparse approximation is a research topic. Over the lifetime, 18037 publications have been published within this topic receiving 497739 citations. The topic is also known as: Sparse approximation.
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TL;DR: The novel Laplacian-regularized low-rank subspace clustering (LLRSC) algorithm is proposed for HSI band selection and outperforms the other state-of-the-art methods and achieves a very competitive band selection performance for HSIs.
Abstract: Band selection is an effective approach to mitigate the “Hughes phenomenon” of hyperspectral image (HSI) classification. Recently, sparse representation (SR) theory has been successfully introduced to HSI band selection, and many SR-based methods have been developed and shown great potential and superiority. However, due to the inherent limitations of the SR scheme, i.e., individually representing each band with only a few other bands from the same subspace, the SR-based methods cannot effectively capture the global structures of the data, which limit the band selection performance. In this paper, to overcome this obstacle, the novel Laplacian-regularized low-rank subspace clustering (LLRSC) algorithm is proposed for HSI band selection. On the one hand, the low-rank subspace clustering model is introduced to capture the global structure information for the learned representation coefficient matrix and deal with the HSI band selection task in the clustering framework. On the other hand, considering the high correlation between adjacent bands, 1-D Laplacian regularization is utilized to incorporate the neighboring band information and further reduce the representation bias. Lastly, an eigenvalue analysis algorithm based on band mutation information is utilized to estimate the appropriate size of the band subset. The experimental results indicate that the proposed LLRSC algorithm outperforms the other state-of-the-art methods and achieves a very competitive band selection performance for HSIs.
97 citations
TL;DR: The experimental results show that the MDL method is effective in removing clouds from both quantitative and qualitative viewpoints, and could well recover the data contaminated by thin and thick clouds or cloud shadows.
Abstract: Cloud covers, which generally appear in optical remote sensing images, limit the use of collected images in many applications. It is known that removing these cloud effects is a necessary preprocessing step in remote sensing image analysis. In general, auxiliary images need to be used as the reference images to determine the true ground cover underneath cloud-contaminated areas. In this paper, a new cloud removal approach, which is called multitemporal dictionary learning (MDL), is proposed. Dictionaries of the cloudy areas (target data) and the cloud-free areas (reference data) are learned separately in the spectral domain. The removal process is conducted by combining coefficients from the reference image and the dictionary learned from the target image. This method could well recover the data contaminated by thin and thick clouds or cloud shadows. Our experimental results show that the MDL method is effective in removing clouds from both quantitative and qualitative viewpoints.
97 citations
TL;DR: A lower bound is established on the tradeoff between the sparsity of the representation, the underlying distortion and the redundancy of any given frame in a complex vector space of dimension N.
Abstract: We consider approximations of signals by the elements of a frame in a complex vector space of dimension N and formulate both the noiseless and the noisy sparse representation problems. The noiseless representation problem is to find sparse representations of a signal r given that such representations exist. In this case, we explicitly construct a frame, referred to as the Vandermonde frame, for which the noiseless sparse representation problem can be solved uniquely using O(N2) operations, as long as the number of non-zero coefficients in the sparse representation of r is isinN for some 0 les isin les 0.5. It is known that isin les 0.5 cannot be relaxed without violating uniqueness. The noisy sparse representation problem is to find sparse representations of a signal r satisfying a distortion criterion. In this case, we establish a lower bound on the tradeoff between the sparsity of the representation, the underlying distortion and the redundancy of any given frame.
97 citations
TL;DR: This paper proposes a novel algorithm, named sparse patch alignment framework, for the embedding of data lying in multiple manifolds, and proposes an optimization strategy for constructing local patches, which adopt sparse representation to select a few neighbors of each data point that span a low-dimensional affine subspace passing near that point.
97 citations
TL;DR: A similarity induced by joint sparse representation is designed to construct the likelihood function of particle filter tracker so that the color visual spectrum and thermal spectrum images can be fused for object tracking.
Abstract: Currently sparse signal reconstruction gains considerable interest and is applied in many fields. In this paper, a similarity induced by joint sparse representation is designed to construct the likelihood function of particle filter tracker so that the color visual spectrum and thermal spectrum images can be fused for object tracking. The proposed fusion scheme performs joint sparse representation calculation on both modalities and the resultant tracking results are fused using min operation on the sparse representation coefficients. In addition, a co-learning approach is proposed to update the reference templates of both modality and enhance the tracking robustness. The proposed fusion scheme outperforms state-of-the-art approaches, and its effectiveness is verified using OTCBVS database.
97 citations