Name
Title | ||
|---|---|---|
A Batch-Mode Active Learning Algorithm Using Region-Partitioning Diversity for SVM Classifier |
Abstract | ||
|---|---|---|
In this paper, a region-partitioning active learning (AL) technique is proposed for classification of remote sensing (RS) images based on the support vector machines (SVM) classifier. In the batch-mode AL process, diversity information is required to select a batch of informative samples. A new AL technique that aims to introduce diversity information is proposed based on relative positions of candidate samples in the feature space. The proposed technique selects informative samples according to an uncertainty criterion at each iteration. These samples are selected with an extra constraint to guarantee that they are not located in the same region of the feature space. The proposed technique is compared with state-of-the-art methods adopted in the RS community. Experimental tests were performed on three data sets, including one very high spatial resolution multispectral data set and two hyperspectral data sets. The proposed algorithm displays a classification performance that is similar to or even better than the state-of-the-art methods. In addition, the proposed algorithm performs efficiently in terms of computational time. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1109/JSTARS.2014.2302332 | Selected Topics in Applied Earth Observations and Remote Sensing, IEEE Journal of |
Keywords | Field | DocType |
hyperspectral imaging,image classification,learning (artificial intelligence),support vector machines,svm classifier,batch-mode active learning algorithm,feature space,high spatial resolution multispectral data set,hyperspectral data sets,region-partitioning active learning,region-partitioning diversity,remote sensing images,uncertainty criterion,active learning (al),margin sampling (ms),region-partitioning,support vector machines (svm),learning artificial intelligence,remote sensing,uncertainty,labeling,vectors,earth | Structured support vector machine,Data mining,Data set,Computer science,Artificial intelligence,Contextual image classification,Classifier (linguistics),Feature vector,Pattern recognition,Least squares support vector machine,Support vector machine,Algorithm,Margin classifier | Journal |
Volume | Issue | ISSN |
7 | 4 | 1939-1404 |
Citations | PageRank | References |
10 | 0.47 | 30 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Lian-Zhi Huo | 1 | 75 | 5.61 |
| Ping Tang | 2 | 19 | 2.30 |