Active learning for classification of remote sensing images
2009
Conference Paper
ei
This paper presents an analysis of active learning techniques for the classification of remote sensing images and proposes a novel active learning method based on support vector machines (SVMs). The proposed method exploits a query function for the inclusion of batches of unlabeled samples in the training set, which is based on the evaluation of two criteria: uncertainty and diversity. This query function adopts a stochastic approach to the selection of unlabeled samples, which is based on a function of uncertainty estimated from the distribution of errors on the validation set (which is assumed available for the model selection of the SVM classifier). Experimental results carried out on a very high resolution image confirm the effectiveness of the proposed active learning technique, which results more accurate than standard methods.
Author(s): | Bruzzone, L. and Persello, C. |
Pages: | III-693-III-696 |
Year: | 2009 |
Month: | July |
Day: | 0 |
Publisher: | IEEE |
Department(s): | Empirical Inference |
Bibtex Type: | Conference Paper (inproceedings) |
DOI: | 10.1109/IGARSS.2009.5417857 |
Event Name: | IEEE International Geoscience and Remote Sensing Symposium (IGARSS 2009) |
Event Place: | Cape Town, South Africa |
Address: | Piscataway, NJ, USA |
Digital: | 0 |
ISBN: | 978-1-4244-3394-0 |
Links: |
Web
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BibTex @inproceedings{BruzzoneP2009_2, title = {Active learning for classification of remote sensing images }, author = {Bruzzone, L. and Persello, C.}, pages = {III-693-III-696 }, publisher = {IEEE}, address = {Piscataway, NJ, USA}, month = jul, year = {2009}, doi = {10.1109/IGARSS.2009.5417857 }, month_numeric = {7} } |