Abstract
In this paper we describe a multi-temporal classification procedure for crops in LANDSAT scenes. The method involves the creation of crop signatures which characterize multi-spectral observations as functions of phenological growth states. The phenological signature models spectral reflectance explicitly as a function of crop maturity rather than a function of observation date. This means that instead of stacking spectral vectors of one observation on another, as is usually done for multi-temporal data, we establish for each possible crop category a correspondence of time to growth state which minimizes the smallest difference between the given multi-spectral multi-temporal vector and the category mean vector indexed by growth state. The results of applying this procedure to winter wheat show that the method is capable of discrimination with about the same degree of accuracy as more traditional multi-temporal classifiers. It shows some potential to label degree of maturity of the crop with out crop condition information in the training set.
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Claims 2
Claim · model-written summary
The growth-state classification of winter wheat reaches about the same discrimination accuracy as traditional multi-temporal classifiers.
- Taxon
- winter wheat
Triticum aestivume1 - Outcome
- discriminatione1
- Comparator
- more traditional multi-temporal classifierse1
- Setting
- LANDSAT scenese0
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“In this paper we describe a multi-temporal classification procedure for crops in LANDSAT scenes”
e1“The results of applying this procedure to winter wheat show that the method is capable of discrimination with about the same degree of accuracy as more traditional multi-temporal classifiers”
Claim · model-written summary
The growth-state method shows some potential to label the degree of maturity of winter wheat without crop condition information in the training set.
- Taxon
- winter wheatTriticum aestivume1
- Outcome
- degree of maturity of the crope2
- Qualifier
- with out crop condition information in the training sete2
Not reportedComparatorValue
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e1“The results of applying this procedure to winter wheat show that the method is capable of discrimination with about the same degree of accuracy as more traditional multi-temporal classifiers”
e2“It shows some potential to label degree of maturity of the crop with out crop condition information in the training set”