.png){kind=icon}
返回
Active Learning Methods for Remote Sensing Image Classification
.png){kind=icon}
PRE
AI
DOI：10.1109/TGRS.2008.2010404.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
In this paper, we propose two active learning algorithms for semiautomatic definition of training samples in remote sensing image classification. Based on predefined heuristics, the classifier ranks the unlabeled pixels and automatically chooses those that are considered the most valuable for its improvement. Once the pixels have been selected, the analyst labels them manually and the process is iterated. Starting with a small and nonoptimal training set, the model itself builds the optimal set of samples which minimizes the classification error. We have applied the proposed algorithms to a variety of remote sensing data, including very high resolution and hyperspectral images, using support vector machines. Experimental results confirm the consistency of the methods. The required number of training samples can be reduced to 10% using the methods proposed, reaching the same level of accuracy as larger data sets. A comparison with a state-of-the-art active learning method, margin sampling, is provided, highlighting advantages of the methods proposed. The effect of spatial resolution and separability of the classes on the quality of the selection of pixels is also discussed.
Keyword:
Active learning
entropy
hyperspectral imagery
image information mining
margin sampling (MS)
query learning
support vector machines (SVMs)
very high resolution (VHR) imagery
期刊
IF:
8.6
论文数:
2.1W
被引数:
10.7W
机构
引用论文
Surgical management of entero and colocutaneous fistulae in Crohn?s disease: 17�year?s experience
.png){kind=icon}
PREAI
分享收藏分享收藏The variation in the number of roots and canals morphology of permanent mandibular first molar teeth by using cone beam computed tomography imaging in a sample of Erbil city使用锥形束计算机断层扫描成像技术对埃尔比勒市样本中恒下颌第一磨牙的根和根管形态数量变化的研究
PREAI
分享收藏
查看更多内容