Batch-Mode Active-Learning Methods for the Interactive Classification of Remote Sensing Images
By Begüm Demir, Claudio Persello, Lorenzo Bruzzone. IEEE Transactions on Geoscience and Remote Sensing, 2011
325 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Media Technology
View Paper: https://www.semanticscholar.org/paper/ed4f7393305ff6c69bdac9322c922cab088c4d8b
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Lorenzo Bruzzone
- Devis Tuia
- Claudio Persello
- Silvio Savarese
- Ozan Sener
- Liangpei Zhang
- Gustau Camps-Valls
- Caiming Xiong
In Computer Vision and Pattern Recognition
- Liangpei Zhang
- Devis Tuia
- Gui-Song Xia
- Lorenzo Bruzzone
- Junwei Han
- Silvio Savarese
- Luc Van Gool
- Ozan Sener
In Media Technology
- Liangpei Zhang
- Devis Tuia
- Lorenzo Bruzzone
- Gustau Camps-Valls
- Gui-Song Xia
- Claudio Persello
- Jordi Muñoz-Marí
- Jon Atli Benediktsson
Citations in the corpus
- Active Learning for Convolutional Neural Networks: A Core-Set Approach (2017, methodology): “On the other hand, Demir et al. (2011) uses a heuristic to first filter the pool based on uncertainty and then choose point to label using diversity.”
- Combining Active Learning and Semi-supervised Learning Using Local and Global Consistency (2014, background): “– Multiclass-level uncertainty with SVM classifier (MCLU+SVM), which was proposed in [9].”
- A Survey of Active Learning Algorithms for Supervised Remote Sensing Image Classification (2011, methodology): “In [39], the idea of margin sampling is extended to multiclass uncertainty (see [40]).”
- Semisupervised Classification of Remote Sensing Images With Active Queries (2012, background): “2) the selection within a node can be differentiated using diversity criteria as those proposed in [28].”
- Active and Semisupervised Learning for the Classification of Remote Sensing Images (2014, methodology): “In [10], different batch-mode AL techniques for the classification of RS images with SVM are investigated.”
- Accurate Annotation of Remote Sensing Images via Active Spectral Clustering with Little Expert Knowledge (2015, methodology): “To achieve this purpose, there are three common types of strategies for intelligent sampling to select new labeled samples from a candidate pool of unlabeled samples [24]: (1) large-margin-based methods [32], which select candidates lying within the margin of the current support vector machine (SVM)”