Object detection and classification in aerial hyperspectral imagery using a multivariate hit-or-miss transform
Macfarlane, Fraser and Murray, Paul and Marshall, Stephen and White, Henry (2019) Object detection and classification in aerial hyperspectral imagery using a multivariate hit-or-miss transform. Proceedings of SPIE, 10986. 1098619. ISSN 0277-786X (https://doi.org/10.1117/12.2518103)
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Abstract
High resolution aerial and satellite borne hyperspectral imagery provides a wealth of information about an imaged scene allowing for many earth observation applications to be investigated. Such applications include geological exploration, soil characterisation, land usage, change monitoring as well as military applications such as anomaly and target detection. While this sheer volume of data provides an invaluable resource, with it comes the curse of dimensionality and the necessity for smart processing techniques as analysing this large quantity of data can be a lengthy and problematic task. In order to aid this analysis dimensionality reduction techniques can be employed to simplify the task by reducing the volume of data and describing it (or most of it) in an alternate way. This work aims to apply this notion of dimensionality reduction based hyperspectral analysis to target detection using a multivariate Percentage Occupancy Hit or Miss Transform that detects objects based on their size shape and spectral properties. We also investigate the effects of noise and distortion and how incorporating these factors in the design of necessary structuring elements allows for a more accurate representation of the desired targets and therefore a more accurate detection. We also compare our method with various other common Target Detection and Anomaly Detection techniques.
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: 68207 Dates: DateEvent14 May 2019Published14 May 2019Published Online14 May 2019Accepted20 March 2019SubmittedSubjects: Technology > Electrical engineering. Electronics Nuclear engineering Department: Faculty of Engineering > Electronic and Electrical Engineering Depositing user: Pure Administrator Date deposited: 03 Jun 2019 15:21 Last modified: 08 Oct 2024 00:19 Related URLs: URI: https://strathprints.strath.ac.uk/id/eprint/68207