Name
Abstract | ||
|---|---|---|
This paper has presented a hard and soft classification model that based on hard and soft classification technique to mapping vegetation distributions. It chose SVMs class image as hard classification model and LSMM results as soft classification model. Through a new adaptive threshold algorithm which could define pure and mixed regions of vegetation automatically to combine hard classification results and soft classification results. In the agricultural landscapes of Southeast Beijing City, results from the proposed model were assessed at a range of spatial scales. Results of vegetation distributions were compared with hard classification model and soft classification model with RMSE. Accuracy assessment showed that hard and soft classification model could get better results. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1109/IGARSS.2014.6947409 | IGARSS |
Keywords | Field | DocType |
adaptive threshold,soft classification,vegetation distributions,linear spectral mixture models,china,hard classification,agricultural landscapes,image classification,geophysical image processing,vegetation mapping,southeast beijing city,support vector machines,accuracy,materials,remote sensing | Vegetation,Computer science,Remote sensing,Support vector machine,Mean squared error,Beijing | Conference |
ISSN | Citations | PageRank |
2153-6996 | 0 | 0.34 |
References | Authors | |
0 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Tangao Hu | 1 | 15 | 3.24 |
| Wenyuan Wu | 2 | 0 | 0.34 |
| Lijuan Liu | 3 | 0 | 1.35 |