Name
Title | ||
|---|---|---|
Sub-Pixel Crop Type Classification Using PROBA-V 100 m NDVI Time Series and Reference Data from Sentinel-2 Classifications. |
Abstract | ||
|---|---|---|
This paper presents the results of a sub-pixel classification of crop types in Bulgaria from PROBA-V 100 m normalized difference vegetation index (NDVI) time series. Two sub-pixel classification methods, artificial neural network (ANN) and support vector regression (SVR) were used where the output was a set of area fraction images (AFIs) at 100 m resolution with pixels containing estimated area fractions of each class. High-resolution maps of two test sites derived from Sentinel-2 classifications were used to obtain training data for the sub-pixel classifications. The estimated area fractions have a good correspondence with the true area fractions when aggregated to regions of 10 x 10 km(2), especially when the SVR method was used. For the five dominant classes in the test sites the R-2 obtained after the aggregation was 86% (winter cereals), 81% (sunflower), 92% (broad-leaved forest), 89% (maize), and 67% (grasslands) when the SVR method was used. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.3390/rs11111370 | REMOTE SENSING |
Keywords | Field | DocType |
crop mapping,Sentinel-2,sub-pixel classification,area fraction images | Reference data (financial markets),Remote sensing,Normalized Difference Vegetation Index,Pixel,Geology | Journal |
Volume | Issue | Citations |
11 | 11 | 0 |
PageRank | References | Authors |
0.34 | 0 | 7 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| petar dimitrov | 1 | 4 | 0.84 |
| Qinghan Dong | 2 | 1 | 1.74 |
| Herman Eerens | 3 | 0 | 0.68 |
| Alexander Gikov | 4 | 0 | 0.34 |
| lachezar hristov filchev | 5 | 4 | 1.18 |
| eugenia roumenina | 6 | 4 | 0.84 |
| georgi jelev | 7 | 4 | 1.18 |