Implementation of the RX algorithm in TensorFlow for high-performance computing

R. Dhanday; J. Pearson; C. J. Willis

doi:10.1117/12.2680369

19 October 2023 Implementation of the RX algorithm in TensorFlow for high-performance computing

R. Dhanday, J. Pearson, C. J. Willis

Proceedings Volume 12733, Image and Signal Processing for Remote Sensing XXIX; 1273310 (2023) https://doi.org/10.1117/12.2680369
Event: SPIE Remote Sensing, 2023, Amsterdam, Netherlands

Conference Poster

Abstract

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 neighboring 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 optimize 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 (approximately 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.

Citation Download Citation

R. Dhanday, J. Pearson, and C. J. Willis "Implementation of the RX algorithm in TensorFlow for high-performance computing", Proc. SPIE 12733, Image and Signal Processing for Remote Sensing XXIX, 1273310 (19 October 2023); https://doi.org/10.1117/12.2680369

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