On the influence of spectral calibration in hyperspectral image classification of leaves
| Autor: | Ronald Criollo, Rodrigo Castro, Daniel Ochoa |
|---|---|
| Rok vydání: | 2017 |
| Předmět: |
010504 meteorology & atmospheric sciences
business.industry Hyperspectral imaging Pattern recognition 04 agricultural and veterinary sciences 01 natural sciences Hyperspectral image processing Close range 040103 agronomy & agriculture Calibration Hyperspectral image classification 0401 agriculture forestry and fisheries Artificial intelligence business Spectral data Classifier (UML) 0105 earth and related environmental sciences |
| Zdroj: | 2017 CHILEAN Conference on Electrical, Electronics Engineering, Information and Communication Technologies (CHILECON). |
| Popis: | Automatic detection of physiological changes in leaves using close range hyperspectral data is becoming a new tool for biologists. Given the geometry of leaves, the reliability of spectral data strongly depends on a careful spectral and geometric calibrations. In this paper, we evaluate the effect of several calibration approaches on automatic classification of leave regions. For our experiments we employ an in-vivo leaf scanning system, then an unsupervised classifier is applied on each calibrated and non-calibrated image and the biological relevance of the output is evaluated using vegetative indexes. Finally, we make recommendations about how to improve the hyperspectral image processing pipeline for this kind of data sets. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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