Detection of Amorphously Shaped Objects Using Spatial Information Detection Enhancement (SIDE)

Grant, Cameron S.; Moon, Todd K.; Gunther, Jacob H.; Stites, Matthew; Williams, Gustavious P.

doi:10.1109/jstars.2012.2186284

Cited by 15 publications

(16 citation statements)

References 46 publications

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“…In this case, they incorporated a prior which encourages both contiguity and sparsity in the solution. This prior is based on the parameter referred to as ΣΔ, which is a measure of contiguity [19]. Though this hierarchical model works well, it suffers from not being a fast algorithm.…”

Section: Stopping Rulementioning

confidence: 99%

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On the block-sparsity of multiple-measurement vectors

Shekaramiz

Moon

Gunther

2015

2015 IEEE Signal Processing and Signal Processing Education Workshop (SP/SPE)

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Based on the compressive sensing (CS) theory, it is possible to recover signals, which are either compressible or sparse under some suitable basis, via a small number of non-adaptive linear measurements. In this paper, we investigate recovering of block-sparse signals via multiple measurement vectors (MMVs) in the presence of noise. In this case, we consider one of the existing algorithms which provides a satisfactory estimate in terms of minimum mean-squared error but a non-sparse solution. Here, the algorithm is first modified to result in sparse solutions. Then, further modification is performed to account for the unknown block sparsity structure in the solution, as well. The performance of the proposed algorithm is demonstrated by experimental simulations and comparisons with some other algorithms for the sparse recovery problem.

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Section: Stopping Rulementioning

confidence: 99%

“…For this purpose, we incorporate an additional term, ΣΔ, to the acceptance probability of each entry of s. Drawing from [15,19], the contiguity measure of the support vector s is defined as follows. At each iteration, we first compute the absolute sum of the differences in s (the "sigma-delta") via…”

Section: New Algorithm For Block-sparse Mmvsmentioning

confidence: 99%

On the block-sparsity of multiple-measurement vectors

Shekaramiz

Moon

Gunther

2015

2015 IEEE Signal Processing and Signal Processing Education Workshop (SP/SPE)

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“…In other words, we assume that the columns of the solution matrix are jointly sparse and each of such vectors, x n , might have groups of clumps i.e., groups of adjacent non-zero terms. Drawing from [14], we measure contiguity of a support-learning vector s as follows. We first compute the absolute sum of the differences (the "sigma-delta") Shekaramiz Under this modification, the complete model becomes (6) Inference on the variables which are modified by the changed model is described as follows.…”

Section: Bayesian Approach For Contiguous Jointly-sparse MMVmentioning

confidence: 99%

“…Our Bayesian model is extended with a prior on the sparsity which encourages both contiguity and sparsity. The prior makes use of the ΣΔ measure of contiguity introduced in [14]. This paper is organized as follows.…”

Section: Introductionmentioning

confidence: 99%

Hierarchical Bayesian approach for jointly-sparse solution of multiple-measurement vectors

Shekaramiz

Moon

Gunther

2014

2014 48th Asilomar Conference on Signals, Systems and Computers

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It is well-known that many signals of interest can be well-estimated via just a small number of supports under some specific basis. Here, we consider finding sparse solution for Multiple Measurement Vectors (MMVs) in case of having both jointly sparse and clumpy structure. Most of the previous work for finding such sparse representations are based on greedy and sub-optimal algorithms such as Basis Pursuit (BP), Matching Pursuit (MP), and Orthogonal Matching Pursuit (OMP). In this paper, we first propose a hierarchical Bayesian model to deal with MMVs that have jointly-sparse structure in their solutions. Then, the model is modified to account for clumps of the neighbor supports (block sparsity) in the solution structure, as well. Several examples are considered to illustrate the merit of the proposed hierarchical Bayesian model compared to OMP and a modified version of the OMP algorithm.

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“…Another approach for plume detection utilizes temporal information by capturing multiple images. HVSs reveal the spectral properties of the scene and record its evolution over time at the cost of processing a large data hypercube [10][11][12][13].…”

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confidence: 99%

Low-Rank Decomposition and Total Variation Regularization of Hyperspectral Video Sequences

Chanussot

et al. 2018

IEEE Trans. Geosci. Remote Sensing

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Abstract-Hyperspectral video sequences (HVSs) are well suited for gas plume detection. The high spectral resolution allows the detection of chemical clouds even when they are optically thin. Processing this new type of video sequences is challenging and requires advanced image and video analysis algorithms. In this paper, we propose a novel method for gas plume detection recorded in HVSs. Based on the assumption that the background is stationary and the gas plume is moving, the proposed method separates the background from the gas plume via a low-rank and sparse decomposition. Furthermore, taking into consideration that the gas plume is continuous in both spatial and temporal dimensions, we include total variation regularization in the constrained minimization problem, which we solve using the augmented Lagrangian multiplier method. After applying the above process to each extracted feature, a novel fusion strategy is proposed to combine the information into a final detection result. Experimental results using real data sets indicate that the proposed method achieves very promising gas plume detection performance.

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Detection of Amorphously Shaped Objects Using Spatial Information Detection Enhancement (SIDE)

Cited by 15 publications

References 46 publications

On the block-sparsity of multiple-measurement vectors

On the block-sparsity of multiple-measurement vectors

Hierarchical Bayesian approach for jointly-sparse solution of multiple-measurement vectors

Low-Rank Decomposition and Total Variation Regularization of Hyperspectral Video Sequences

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