Hyperspectral target detection with sparseness constraint

Ma, Ben; Du, Qian

doi:10.1109/igarss.2013.6721346

Cited by 4 publications

(3 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The residual between the test sample and the reconstruction in the feature space is then computed according to (15) by…”

Section: B Kernelization Of Srbbhdmentioning

confidence: 99%

“…To strengthen the performance of the LMM, sparsity representation-based detection methods have been developed, including the sparsity-based target detector (STD) [13], the joint sparse detector (JSD) [14], and the algorithm in [15]. These detection methods linearly model a test sample as a linear mixture of a number of training samples from target and background dictionaries.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A Nonlinear Sparse Representation-Based Binary Hypothesis Model for Hyperspectral Target Detection

Zhang

Wang

2015

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

View full text Add to dashboard Cite

The sparsity model has been employed for hyperspectral target detection and has been proved to be very effective when compared to the traditional linear mixture model. However, the state-of-art sparsity models usually represent a test sample via a sparse linear combination of both target and background training samples, which does not result in an efficient representation of a background test sample. In this paper, a sparse representation-based binary hypothesis (SRBBH) model employs more appropriate dictionaries with the binary hypothesis model to sparsely represent the test sample. Furthermore, the nonlinear issue is addressed in this paper, and a kernel method is employed to resolve the detection issue in complicated hyperspectral images. In this way, the kernel SRBBH model not only takes the nonlinear endmember mixture into consideration, but also fully exploits the sparsity model by the use of more reasonable dictionaries. The recovery process leads to a competition between the background and target subspaces, which is effective in separating the targets from the background, thereby enhancing the detection performance.Index Terms-Binary hypothesis, hyperspectral imagery (HSI), kernel, sparse representation, target detection.

show abstract

“…The residual between the test sample and the reconstruction in the feature space is then computed according to (15) by…”

Section: B Kernelization Of Srbbhdmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Nonlinear Sparse Representation-Based Binary Hypothesis Model for Hyperspectral Target Detection

Zhang

Wang

2015

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

View full text Add to dashboard Cite

show abstract

“…Due to this remarkable characteristic, it has been investigated for various applications, such as target detection, mineral exploration, environmental monitoring, medical detection, etc. [1][2][3][4][5][6] In the analysis of hyperspectral imagery, spectral unmixing, which can identify physical construction of each pixel [7][8] , has gained great attention. Spectral unmixing can be either linear or non-linear.…”

Section: Introductionmentioning

confidence: 99%

Region-based collaborative sparse unmixing of hyperspectral imagery

2016

SPIE Proceedings

Self Cite

View full text Add to dashboard Cite

Sparse unmixing (SU) has been investigated to select a small number of endmembers from a large spectral library, which is a pixel-based technique. In image-based collaborative sparse unmxing (CSU) techniques, pixels are forced to select the same small set of endmembers. In reality, the same small set of endmembers may be responsible for pixel construction within a homogeneous area. For an entire image, the endmember sets are often different. So, in this paper, we propose a region-based collaborative sparse unmixing (RCSU) algorithm, and the region may include nonlocal areas as long as they belong to the same type of homogeneous segments. Experimental results show that the overall performance of the proposed RCSU algorithm is better than that of image-based CSU or pixel-based SU.

show abstract

Parallel discriminative subspace for city target detection from high dimension images

Zhang

et al. 2020

Geoinformatica

View full text Add to dashboard Cite

Hyperspectral target detection with sparseness constraint

Cited by 4 publications

References 7 publications

A Nonlinear Sparse Representation-Based Binary Hypothesis Model for Hyperspectral Target Detection

A Nonlinear Sparse Representation-Based Binary Hypothesis Model for Hyperspectral Target Detection

Region-based collaborative sparse unmixing of hyperspectral imagery

Parallel discriminative subspace for city target detection from high dimension images

Contact Info

Product

Resources

About