| Attributes | Values |
|---|
| rdf:type
| |
| Description
| - The objective of this study is to find algorithms for detection of Cirsium arvense in cereals using airborne high resolution multispectral imaging. The image of ripped winter wheat stand was taken from helicopter using 3-band multispectral imaging system in the altitude of 290 m, which provided a spatial resolution of 0.1 m per pixel. Green (500–580 nm), red (630–710 nm), and NIR (735–865 nm) spectral bands were used. Sample areas of 2 x 2 m were marked in the field using white targets. Ground truth data were collected immediately after the flight. High resolution ground images of the sample areas were taken by digital camera. The plants of C. arvense were manually extracted from these images and the aerial and ground information were compared by the overlay of the images. Various vegetation indices including NDVI were calculated and the accuracy of the classification was tested. The best correlation coefficient (r = 0.800) and also the highest classification accuracy (88.96 %) was reached using DVI
- The objective of this study is to find algorithms for detection of Cirsium arvense in cereals using airborne high resolution multispectral imaging. The image of ripped winter wheat stand was taken from helicopter using 3-band multispectral imaging system in the altitude of 290 m, which provided a spatial resolution of 0.1 m per pixel. Green (500–580 nm), red (630–710 nm), and NIR (735–865 nm) spectral bands were used. Sample areas of 2 x 2 m were marked in the field using white targets. Ground truth data were collected immediately after the flight. High resolution ground images of the sample areas were taken by digital camera. The plants of C. arvense were manually extracted from these images and the aerial and ground information were compared by the overlay of the images. Various vegetation indices including NDVI were calculated and the accuracy of the classification was tested. The best correlation coefficient (r = 0.800) and also the highest classification accuracy (88.96 %) was reached using DVI (en)
- The objective of this study is to find algorithms for detection of Cirsium arvense in cereals using airborne high resolution multispectral imaging. The image of ripped winter wheat stand was taken from helicopter using 3-band multispectral imaging system in the altitude of 290 m, which provided a spatial resolution of 0.1 m per pixel. Green (500–580 nm), red (630–710 nm), and NIR (735–865 nm) spectral bands were used. Sample areas of 2 x 2 m were marked in the field using white targets. Ground truth data were collected immediately after the flight. High resolution ground images of the sample areas were taken by digital camera. The plants of C. arvense were manually extracted from these images and the aerial and ground information were compared by the overlay of the images. Various vegetation indices including NDVI were calculated and the accuracy of the classification was tested. The best correlation coefficient (r = 0.800) and also the highest classification accuracy (88.96 %) was reached using DVI (cs)
|
| Title
| - Detection of Cirsium arvense L. in winter wheat using a multispectral imaging system
- Detekce Cirsium arvense L. v ozimé pšenici pomocí multispektrálního snímkování (cs)
- Detection of Cirsium arvense L. in winter wheat using a multispectral imaging system (en)
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| skos:prefLabel
| - Detection of Cirsium arvense L. in winter wheat using a multispectral imaging system
- Detekce Cirsium arvense L. v ozimé pšenici pomocí multispektrálního snímkování (cs)
- Detection of Cirsium arvense L. in winter wheat using a multispectral imaging system (en)
|
| skos:notation
| - RIV/60460709:41210/08:26227!RIV09-MZE-41210___
|
| http://linked.open...avai/riv/aktivita
| |
| http://linked.open...avai/riv/aktivity
| - P(QH71099), Z(MSM6046070901)
|
| http://linked.open...iv/cisloPeriodika
| |
| http://linked.open...vai/riv/dodaniDat
| |
| http://linked.open...aciTvurceVysledku
| |
| http://linked.open.../riv/druhVysledku
| |
| http://linked.open...iv/duvernostUdaju
| |
| http://linked.open...titaPredkladatele
| |
| http://linked.open...dnocenehoVysledku
| |
| http://linked.open...ai/riv/idVysledku
| - RIV/60460709:41210/08:26227
|
| http://linked.open...riv/jazykVysledku
| |
| http://linked.open.../riv/klicovaSlova
| - airborne imaging; DVI; NDVI; perennial weeds; remote sensing; weed mapping (en)
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| http://linked.open.../riv/klicoveSlovo
| |
| http://linked.open...odStatuVydavatele
| - DE - Spolková republika Německo
|
| http://linked.open...ontrolniKodProRIV
| |
| http://linked.open...i/riv/nazevZdroje
| - Journal of Plant Diseases and Protection
|
| http://linked.open...in/vavai/riv/obor
| |
| http://linked.open...ichTvurcuVysledku
| |
| http://linked.open...cetTvurcuVysledku
| |
| http://linked.open...vavai/riv/projekt
| |
| http://linked.open...UplatneniVysledku
| |
| http://linked.open...v/svazekPeriodika
| |
| http://linked.open...iv/tvurceVysledku
| - Hamouz, Pavel
- Hamouzová, Kateřina
- Soukup, Josef
- Holec, Josef
|
| http://linked.open...n/vavai/riv/zamer
| |
| issn
| |
| number of pages
| |
| http://localhost/t...ganizacniJednotka
| |
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