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On relationship among orthogonal subspace projection, constrained energy minimization and rx-algorithm

On relationship among orthogonal subspace projection, constr...

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Conference on algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery VIII

作者： Chang, CI Univ Maryland Baltimore Cty Remote Sensing Signal & Image Proc Lab Dept Comp Sci & Elect Engn Baltimore MD 21250 USA

ISBN: (纸本)0819444758

Orthogonal Subspace Projection (OSP) has been shown a successful technique for hyperspectral image analysis. It requires a linear mixture model with complete target knowledge to per-form subpixel detection and mixed classification. Constrained energy minimization (CEM) has been also shown to be effective in subpixel detection and mixed pixel classification which only needs the knowledge of targets of interest. rx-algorithm which has been widely used for anomaly detection in signal processing does not require any prior target information. Interestingly, these three techniques are closely related from an aspect of information being used in these three techniques. They all perform some sort of matched filter with different levels of information used in the filter. This paper investigates and explores their relationship that sheds light on their algorithm design.

关键词： constrained energy minimization (CEM) matched filter Orthogonal Subspace Projection (OSP) rx-algorithm

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Implementation of the rx algorithm in TensorFlow for high performance computing 29

Implementation of the RX algorithm in TensorFlow for high pe...

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Conference on Image and Signal Processing for Remote Sensing XXIX

作者： Dhanday, R. Pearson, J. Willis, C. J. BAE Syst Appl Intelligence Labs Chelmsford CM2 8HN England

ISBN: (纸本)9781510666955;9781510666962

We present a TensorFlow implementation of the rx-algorithm for anomaly detection in multi-spectral and hyperspectral imagery. In this paper, we perform a runtime performance comparison of the algorithm, implemented using: NumPy, SciPy and TensorFlow libraries on a CPU, a GPU (where applicable) as well as on edge hardware (Jetson TX2). The rx detection algorithm makes use of either local or global background statistics, such as the mean and covariance, to find anomalous pixels. In the approach examined here, the statistics are estimated using local background samples from the area neighbouring the pixel under test. Such algorithms are typically implemented in Python using the NumPy library for numerical operations;however, a preliminary literature review found no formal investigations have been made into the suitability of alternative frameworks to optimise the performance on edge hardware. Our TensorFlow (and SciPy) implementations involve the use of a convolutional operations to calculate the required statistics. The use of these libraries significantly reduces the algorithm's run time. We evaluate the implementation using a range of hardware, in order to get a diverse set of results and to highlight the differences in run times on each. We also show a comparative set of implementations of a Matched Filter algorithm for target detection. This algorithm uses a very similar approach to the rx algorithm, but is provided with a template target spectrum to detect within the image. Notable improvements (similar to 98% reduction in run time) in performance can be seen through the use of a TensorFlow implementation on GPU. Results are demonstrated by trialing on multispectral imagery for ship detection.

关键词： rx-algorithm high performance computing edge hardware target detection Matched Filter algorithm

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Non-Negative Matrix Factorization for Hyperspectral Anomaly Detection 26

Non-Negative Matrix Factorization for Hyperspectral Anomaly ...

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Conference on algorithms, Technologies, and Applications for Multispectral and Hyperspectral Imagery XXVI

作者： Aizenshtein, Sofia Abergel, Ido Mailler, Moshe Segal, Gili Rotman, Stanley R. Ben Gurion Univ Negev Sch Elect & Comp Engn Beer Sheva Israel Portland State Univ Dept Elect & Comp Engn Portland OR 97207 USA

ISBN: (数字)9781510635623

ISBN: (纸本)9781510635623

A common anomaly detection algorithm for hyperspectral imagery is the rx algorithm based on the Mahalanobis distance of each pixel from the image mean. This is a benchmark algorithm which can be applied either directly on a hyperspectral image or on a dimensionality-reduced hyperspectral image. Recent work on Non-Negative Matrix Factorization (NNMF) provides a fast-iterative algorithm for decomposing a hyperspectral cube and achieving dimensionality reduction. In this paper, we study the implementation of the NNMF algorithm on a hyperspectral data cube and propose two new anomaly detection algorithms, based on combining the NNMF and the rx algorithms. In the first version, we apply the NNMF algorithm on a hyperspectral image reducing the dimensionality;we then apply the rx algorithm. In the second version, we segment and cluster the dataset after applying the NNMF algorithm. Anomaly detection is then performed on this dataset. Using either of these algorithms overcomes a weakness of the rx algorithm in handling background clusters which are close to each other. The algorithm was tested on the RIT blind test dataset. From our results, we conclude that the two versions of the algorithm are sensitive to different types of anomalies;a two-dimensional scatterplot of the data comparing the rx values to either of the NNMF algorithms enables us to distinguish between the anomaly types. The ground truth shows that we have achieved high accuracy and less false alarms.

关键词： Hyperspectral Anomaly Detection rx-algorithm Non-Negative Matrix Factorization

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