Investigating the effects of a combined spatial and spectral dimensionality reduction approach for aerial hyperspectral target detection applications
Macfarlane, Fraser and Murray, Paul and Marshall, Stephen and White, Henry (2021) Investigating the effects of a combined spatial and spectral dimensionality reduction approach for aerial hyperspectral target detection applications. Remote Sensing, 13 (9). 1647. ISSN 2072-4292 (https://doi.org/10.3390/rs13091647)
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Abstract
Target detection and classification is an important application of hyperspectral imaging in remote sensing. A wide range of algorithms for target detection in hyperspectral images have been developed in the last few decades. Given the nature of hyperspectral images, they exhibit large quantities of redundant information and are therefore compressible. Dimensionality reduction is an effective means of both compressing and denoising data. Although spectral dimensionality reduction is prevalent in hyperspectral target detection applications, the spatial redundancy of a scene is rarely exploited. By applying simple spatial masking techniques as a preprocessing step to disregard pixels of definite disinterest, the subsequent spectral dimensionality reduction process is simpler, less costly and more informative. This paper proposes a processing pipeline to compress hyperspectral images both spatially and spectrally before applying target detection algorithms to the resultant scene. The combination of several different spectral dimensionality reduction methods and target detection algorithms, within the proposed pipeline, are evaluated. We find that the Adaptive Cosine Estimator produces an improved F1 score and Matthews Correlation Coefficient when compared to unprocessed data. We also show that by using the proposed pipeline the data can be compressed by over 90% and target detection performance is maintained.
ORCID iDs
Macfarlane, Fraser ORCID: https://orcid.org/0000-0002-7411-1446, Murray, Paul ORCID: https://orcid.org/0000-0002-6980-9276, Marshall, Stephen ORCID: https://orcid.org/0000-0001-7079-5628 and White, Henry;-
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Item type: Article ID code: 76141 Dates: DateEvent23 April 2021Published19 April 2021Accepted27 February 2021SubmittedSubjects: Technology > Electrical engineering. Electronics Nuclear engineering Department: Faculty of Engineering > Electronic and Electrical Engineering
Strategic Research Themes > Measurement Science and Enabling Technologies
Technology and Innovation Centre > Sensors and Asset ManagementDepositing user: Pure Administrator Date deposited: 19 Apr 2021 11:47 Last modified: 17 Nov 2024 01:19 Related URLs: URI: https://strathprints.strath.ac.uk/id/eprint/76141