Detection and tracking of gas plumes in LWIR hyperspectral video sequence data

Gerhart, Torin; Sunu, Justin; Lieu, Lauren; Merkurjev, Ekaterina; Chang, Jen Mei; Gilles, Jérôme; Bertozzi, Andrea L.

doi:10.1117/12.2015155

Cited by 28 publications

(30 citation statements)

References 5 publications

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“…We show the different values from different frames of one fixed pixel in Figure 5. To eliminate the flicker between frames, the Midway equalization method is used in [35]. In this paper, we do not perform this preprocessing and the flicker problem does not affect the classification result.…”

Section: Plume Video Datamentioning

confidence: 99%

“…The approach in [35] uses a combination of dimension reduction and histogram equalization to prepare the hyperspectral video data for segmentation. Principal Component Analysis (PCA) is used for dimension reduction of the hyperspectral video data, and a Midway method for histogram equalization is used to redistribute the intensity values in order to reduce flicker between frames.…”

Section: Plume Video Datamentioning

confidence: 99%

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Hyperspectral Image Classification Using Graph Clustering Methods

Zhang¹,

Merkurjev²,

Koniges³

et al. 2017

Image Processing on Line

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Hyperspectral imagery is a challenging modality due to the dimension of the pixels which can range from hundreds to over a thousand frequencies depending on the sensor. Most methods in the literature reduce the dimension of the data using a method such as principal component analysis, however this procedure can lose information. More recently methods have been developed to address classification of large datasets in high dimensions. This paper presents two classes of graph-based classification methods for hyperspectral imagery. Using the full dimensionality of the data, we consider a similarity graph based on pairwise comparisons of pixels. The graph is segmented using a pseudospectral algorithm for graph clustering that requires information about the eigenfunctions of the graph Laplacian but does not require computation of the full graph. We develop a parallel version of the Nyström extension method to randomly sample the graph to construct a low rank approximation of the graph Laplacian. With at most a few hundred eigenfunctions, we can implement the clustering method designed to solve a variational problem for a graph-cut-based semi-supervised or unsupervised classification problem. We implement OpenMP directive-based parallelism in our algorithms and show performance improvement and strong, almost ideal, scaling behavior. The method can handle very large datasets including a video sequence with over a million pixels, and the problem of segmenting a data set into a pre-determined number of classes. Source CodeThe reviewed source code and documentation for this algorithm are available from the web page of this article 1 . Compilation and usage instructions are included in the README.txt file of the archive.

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Section: Plume Video Datamentioning

confidence: 99%

Section: Plume Video Datamentioning

confidence: 99%

Hyperspectral Image Classification Using Graph Clustering Methods

Zhang¹,

Merkurjev²,

Koniges³

et al. 2017

Image Processing on Line

Self Cite

View full text Add to dashboard Cite

show abstract

“…One hyperspectral image is captured every five seconds. This data set has been studied in other works such as [3], [4], [5]. Prior work on hyper spectral plume detection using other sensors includes [6] (MWIR) and [7] (HYDICE).…”

Section: Introductionmentioning

confidence: 99%

“…One obstacle faced by the authors of [3] is the significant preprocessing needed to accurately detect the plume. Due to the noisy structure of the data set, principal component analysis reduced the data to three main features used to produce a false color video sequence of the plume release, followed by midway equalization to smooth the flicker between frames.…”

Section: Introductionmentioning

confidence: 99%

“…This makes the assessment of the results somewhat difficult. The authors of [3] dealt with this problem by using an adaptive matched subspace detector (AMSD) described in [7] to benchmark their spectral clustering results. AMSD is a probabilistic detection scheme that uses a generalized likelihood ratio test to choose between two hypotheses: target present or absent.…”

Section: Introductionmentioning

confidence: 99%

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Graph MBO method for multiclass segmentation of hyperspectral stand-off detection video

Merkurjev

Sunu

Bertozzi

2014

2014 IEEE International Conference on Image Processing (ICIP)

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We consider the challenge of detection of chemical plumes in hyperspectral image data. Segmentation of gas is difficult due to the diffusive nature of the cloud. The use of hyperspectral imagery provides non-visual data for this problem, allowing for the utilization of a richer array of sensing information. In this paper, we present a method to track and classify objects in hyperspectral videos. The method involves the application of a new algorithm recently developed for high dimensional data. It is made efficient by the application of spectral methods and the Nyström extension to calculate the eigenvalues/eigenvectors of the graph Laplacian. Results are shown on plume detection in LWIR standoff detection.

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Multi-class Graph Mumford-Shah Model for Plume Detection Using the MBO scheme

Sunu

Bertozzi

2015

Lecture Notes in Computer Science

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Abstract. We focus on the multi-class segmentation problem using the piecewise constant Mumford-Shah model in a graph setting. After formulating a graph version of the Mumford-Shah energy, we propose an efficient algorithm called the MBO scheme using threshold dynamics. Theoretical analysis is developed and a Lyapunov functional is proven to decrease as the algorithm proceeds. Furthermore, to reduce the computational cost for large datasets, we incorporate the Nyström extension method which efficiently approximates eigenvectors of the graph Laplacian based on a small portion of the weight matrix. Finally, we implement the proposed method on the problem of chemical plume detection in hyper-spectral video data.

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Detection and tracking of gas plumes in LWIR hyperspectral video sequence data

Cited by 28 publications

References 5 publications

Hyperspectral Image Classification Using Graph Clustering Methods

Hyperspectral Image Classification Using Graph Clustering Methods

Graph MBO method for multiclass segmentation of hyperspectral stand-off detection video

Multi-class Graph Mumford-Shah Model for Plume Detection Using the MBO scheme

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