Identification and spectral evaluation of agricultural crops on hyperspectral airborne data

Csendes Bálint and Mucsi László: Identification and spectral evaluation of agricultural crops on hyperspectral airborne data. In: Journal of environmental geography, (9) 3-4. pp. 49-53. (2016)

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Hyperspectral remote sensing combined with advanced image processing techniques is an efficient tool for the identification of agricultural crops. In our study we pursued spectral analysis on a relatively small sample area using low number of training points to examine the potential of high resolution imagery. Spectral separability measurements were applied to reveal spectral overlapping between 4 crop species and for the discrimination we also used statistical comparisons such as plotting the PC values and calculating standard deviation of single band reflectance values on our classes. These statistical results were proven to be good indicators of spectral similarity and potential confusion of data samples. The classification of Spectral Angle Mapper (SAM) had an overall accuracy of 72% for the four species where the poorest results were obtained from the test points of garlic and sugar beet. Comparing the statistical analyses we concluded that spectral homogeneity does not necessarily have influence on the accuracy of mapping, whereas separability scores strongly correlate with classification results, implying also that preliminary statistical assessments can improve the efficiency of training site selection and provide useful information to specify some technical requirements of airborne hyperspectral surveys.

Item Type: Article
Journal or Publication Title: Journal of environmental geography
Date: 2016
Volume: 9
Number: 3-4
ISSN: 2060-467X
Page Range: pp. 49-53
Language: English
Place of Publication: Szeged
Related URLs:
DOI: DOI: 10.1515/jengeo-2016-0012
Uncontrolled Keywords: Képalkotó spektrometria, Mezőgazdasági monitorozás
Additional Information: Bibliogr.: 52. p. ; ill. ; összefoglalás angol nyelven
Subjects: 01. Natural sciences
01. Natural sciences > 01.05. Earth and related environmental sciences
Date Deposited: 2017. Jun. 20. 14:41
Last Modified: 2022. Jun. 27. 15:07

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