DocumentCode :
484354
Title :
Web Cameras in Automatic Autumn Colour Monitoring
Author :
Astola, Heikki ; Molinier, Matthieu ; Mikkola, T. ; Kubin, E.
Author_Institution :
VTT Tech. Res. Centre of Finland, Espoo
Volume :
3
fYear :
2008
fDate :
7-11 July 2008
Abstract :
The objective of ForSe - Season Monitoring study was to develop an automatic method to analyze web-camera images of nature. As the outcome the image analysis produces indices that indicate the seasonal development stage of the forest (e.g. degree of autumn colour of deciduous trees). IP web-cameras of a pilot camera network were programmed to take one image in 15 minute interval on daylight hours during autumn period. One camera was used as a source of the training data (Enontekio), and one for testing data (Oulanka). The image data was preprocessed to reduce noise and to and spectral angle feature was calculated to compensate the illumination variations between consequential images and within a single image. Selected areas of the training site camera images of autumn season were classified into six classes describing the seasonal status of the leaves (green, light green, yellow, red, brown, fallen). The spectral angle features were calculated for these areas and clustered by K-means into 30 clusters. Class labels were assigned to the cluster centres using k-NN method (k= 5). To see the progress of a certain colour class in the time series of images of a test site camera, the classified pixels within selected regions of interest (ROI) were used to produce a continuous season colour index (SCI). The behaviour of the index was compared with a reference classification supplied by phenology experts from Finnish Forest Research Institute (Metla).
Keywords :
cameras; colour photography; digital photography; forestry; geophysical signal processing; vegetation; Enontekio; Finnish Forest Research Institute; ForSe - Season Monitoring study; IP web cameras; Metla; Oulanka; Web cameras; automatic autumn colour monitoring; deciduous trees; forest; image analysis; k-NN method; season colour index; time 15 min; Cameras; Colored noise; Computerized monitoring; Data acquisition; Image analysis; Image color analysis; Noise reduction; Pixel; Prototypes; Testing; Automatic optical inspection; Data acquisition; Image color analysis; Pattern recognition;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Geoscience and Remote Sensing Symposium, 2008. IGARSS 2008. IEEE International
Conference_Location :
Boston, MA
Print_ISBN :
978-1-4244-2807-6
Electronic_ISBN :
978-1-4244-2808-3
Type :
conf
DOI :
10.1109/IGARSS.2008.4779476
Filename :
4779476
Link To Document :
بازگشت