Name
Abstract | ||
|---|---|---|
Active learning process represents an interesting solution to the problem of training sample collection for the classification of remote sensing images. In this work, we propose a criterion based on the spatial information that can be used in combination with a spectral criterion in order to improve the selection of training samples. Experimental results obtained on a very high resolution image show the effectiveness of regularization in spatial domain and open challenging perspectives for terrain campaigns planning. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1109/IGARSS.2011.6050089 | Geoscience and Remote Sensing Symposium |
Keywords | Field | DocType |
geophysical image processing,image classification,image resolution,learning (artificial intelligence),remote sensing,active learning,remote sensing image classification,spatial information,spectral criterion,terrain campaign planning,very high resolution image,Active learning,spatial information,support vector machines (SVMs),very-high-resolution (VHR) images | Spatial analysis,Computer science,Terrain,Remote sensing,Regularization (mathematics),Artificial intelligence,Contextual image classification,Computer vision,Active learning,Support vector machine,Image resolution,Machine learning,Support vector machines svms | Conference |
ISSN | ISBN | Citations |
2153-6996 | 978-1-4577-1003-2 | 5 |
PageRank | References | Authors |
0.48 | 9 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Edoardo Pasolli | 1 | 285 | 17.04 |
| Farid Melgani | 2 | 1100 | 80.98 |
| Devis Tuia | 3 | 1715 | 101.88 |
| Pacifici, F. | 4 | 217 | 9.86 |