Multi-scale spatial and spectral feature fusion for soil carbon content prediction based on hyperspectral images Article Swipe
Xueying Li
,
Zongmin Li
,
Huimin Qiu
,
Guangyuan Chen
,
Pingping Fan
,
Yan Liu
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1016/j.ecolind.2024.111843
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1016/j.ecolind.2024.111843
Soil carbon content prediction based on hyperspectral images can achieve large-scale spatial measurement, which has the advantages of wide coverage and fast information collection, is more suitable for field data collection. However, the research on soil carbon content prediction based on hyperspectral images mainly focuses on feature extraction of spectral information, ignoring the spatial information, and cannot well reveal the intrinsic structural characteristics of data. Aiming at the lack of spatial features consideration in hyperspectral images, soil carbon content prediction methods based on multi-scale feature fusion are proposed by hyperspectral image. At the same time of extracting spectral features from hyperspectral images, the spatial information is used for the first time and a multi-scale spectral and spatial feature network (SpeSpaMN) is designed. In the SpeSpaMN, the multi-scale spectral feature network (SpeMN) is constructed to extract spectral features, the multi-scale spatial feature network (SpaMN) is constructed to extract spatial features. The two networks are fused by using the complementary relationship between different scale features to achieve soil carbon content prediction based on multi-scale feature fusion. The results showed that SpeSpaMN had the best results compared to other methods, followed by the method of SpeMN. The RPD of Inland, Aoshan Bay and Jiaozhou Bay samples based on SpeSpaMN were increased by 47.36%, 37.96% and 4.30% respectively. This paper can effectively solve the problem of the deep fusion of spatial and spectral features in the soil carbon content prediction by hyperspectral image, so as to improve the accuracy and stability of soil carbon content prediction.
Related Topics
Concepts
Hyperspectral imaging
Scale (ratio)
Feature (linguistics)
Fusion
Carbon fibers
Content (measure theory)
Remote sensing
Environmental science
Spatial ecology
Soil carbon
Soil science
Pattern recognition (psychology)
Computer science
Artificial intelligence
Soil water
Mathematics
Ecology
Geology
Geography
Cartography
Biology
Composite number
Mathematical analysis
Algorithm
Linguistics
Philosophy
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1016/j.ecolind.2024.111843
- OA Status
- gold
- Cited By
- 9
- References
- 44
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4392366634
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4392366634Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1016/j.ecolind.2024.111843Digital Object Identifier
- Title
-
Multi-scale spatial and spectral feature fusion for soil carbon content prediction based on hyperspectral imagesWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2024Year of publication
- Publication date
-
2024-03-01Full publication date if available
- Authors
-
Xueying Li, Zongmin Li, Huimin Qiu, Guangyuan Chen, Pingping Fan, Yan LiuList of authors in order
- Landing page
-
https://doi.org/10.1016/j.ecolind.2024.111843Publisher landing page
- Open access
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YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://doi.org/10.1016/j.ecolind.2024.111843Direct OA link when available
- Concepts
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Hyperspectral imaging, Scale (ratio), Feature (linguistics), Fusion, Carbon fibers, Content (measure theory), Remote sensing, Environmental science, Spatial ecology, Soil carbon, Soil science, Pattern recognition (psychology), Computer science, Artificial intelligence, Soil water, Mathematics, Ecology, Geology, Geography, Cartography, Biology, Composite number, Mathematical analysis, Algorithm, Linguistics, PhilosophyTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
9Total citation count in OpenAlex
- Citations by year (recent)
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2025: 8, 2024: 1Per-year citation counts (last 5 years)
- References (count)
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44Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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