MINIMAL LEARNING MACHINE IN ANOMALY DETECTION FROM HYPERSPECTRAL IMAGES Article Swipe
I. Pölönen
,
Kimmo A. Riihiaho
,
Anna-Maria Hakola
,
Leevi Annala
·
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.5194/isprs-archives-xliii-b3-2020-467-2020
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.5194/isprs-archives-xliii-b3-2020-467-2020
Anomaly detection from hyperspectral data needs computationally efficient methods to process the data when the data gathering platform is a drone or a cube satellite. In this study, we introduce a minimal learning machine for hyperspectral anomaly detection. Minimal learning machine is a novel distance-based classification algorithm, which is now modified to detect anomalies. Besides being computationally efficient, minimal learning machine is also easy to implement. Based on the results, we show that minimal learning machine is efficient in detecting global anomalies from the hyperspectral data with low false alarm rate.
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Concepts
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.5194/isprs-archives-xliii-b3-2020-467-2020
- https://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLIII-B3-2020/467/2020/isprs-archives-XLIII-B3-2020-467-2020.pdf
- OA Status
- diamond
- Cited By
- 2
- References
- 15
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3080512163
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W3080512163Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.5194/isprs-archives-xliii-b3-2020-467-2020Digital Object Identifier
- Title
-
MINIMAL LEARNING MACHINE IN ANOMALY DETECTION FROM HYPERSPECTRAL IMAGESWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2020Year of publication
- Publication date
-
2020-08-21Full publication date if available
- Authors
-
I. Pölönen, Kimmo A. Riihiaho, Anna-Maria Hakola, Leevi AnnalaList of authors in order
- Landing page
-
https://doi.org/10.5194/isprs-archives-xliii-b3-2020-467-2020Publisher landing page
- PDF URL
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https://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLIII-B3-2020/467/2020/isprs-archives-XLIII-B3-2020-467-2020.pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
diamondOpen access status per OpenAlex
- OA URL
-
https://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLIII-B3-2020/467/2020/isprs-archives-XLIII-B3-2020-467-2020.pdfDirect OA link when available
- Concepts
-
Hyperspectral imaging, Anomaly detection, Computer science, Constant false alarm rate, Artificial intelligence, Data cube, Pattern recognition (psychology), Machine learning, Data miningTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
2Total citation count in OpenAlex
- Citations by year (recent)
-
2022: 1, 2021: 1Per-year citation counts (last 5 years)
- References (count)
-
15Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| referenced_works | https://openalex.org/W2165447611, https://openalex.org/W7064111672, https://openalex.org/W2246162424, https://openalex.org/W6697265569, https://openalex.org/W6679539681, https://openalex.org/W3013515824, https://openalex.org/W2954514467, https://openalex.org/W2047870694, https://openalex.org/W1998651154, https://openalex.org/W2750586932, https://openalex.org/W2296719434, https://openalex.org/W2101234009, https://openalex.org/W2591466624, https://openalex.org/W2122646361, https://openalex.org/W2131904035 |
| referenced_works_count | 15 |
| abstract_inverted_index.a | 20, 23, 31, 43 |
| abstract_inverted_index.In | 26 |
| abstract_inverted_index.in | 79 |
| abstract_inverted_index.is | 19, 42, 49, 62, 77 |
| abstract_inverted_index.on | 68 |
| abstract_inverted_index.or | 22 |
| abstract_inverted_index.to | 10, 52, 65 |
| abstract_inverted_index.we | 29, 71 |
| abstract_inverted_index.for | 35 |
| abstract_inverted_index.low | 88 |
| abstract_inverted_index.now | 50 |
| abstract_inverted_index.the | 12, 15, 69, 84 |
| abstract_inverted_index.also | 63 |
| abstract_inverted_index.cube | 24 |
| abstract_inverted_index.data | 5, 13, 16, 86 |
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| abstract_inverted_index.from | 3, 83 |
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| abstract_inverted_index.novel | 44 |
| abstract_inverted_index.rate. | 91 |
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| abstract_inverted_index.anomaly | 37 |
| abstract_inverted_index.machine | 34, 41, 61, 76 |
| abstract_inverted_index.methods | 9 |
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| abstract_inverted_index.results, | 70 |
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| abstract_inverted_index.detection | 2 |
| abstract_inverted_index.efficient | 8, 78 |
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| abstract_inverted_index.distance-based | 45 |
| abstract_inverted_index.computationally | 7, 57 |
| cited_by_percentile_year.max | 94 |
| cited_by_percentile_year.min | 89 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 4 |
| citation_normalized_percentile.value | 0.67896326 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |