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摘要
En 中文
Recently, the combination of topic models and random fields has been frequently and successfully applied to image classification due to their complementary effect. However, the number of classes is usually needed to be assigned manually. This letter presents an efficient unsupervised semantic classification method for high-resolution satellite images. We add label cost, which can penalize a solution based on a set of labels that appear in it by optimization of energy, to the random fields of latent topics, and an iterative algorithm is thereby proposed to make the number of classes finally be converged to an appropriate level. Compared with other mentioned classification algorithms, our method not only can obtain accurate semantic segmentation results by larger scale structures but also can automatically assign the number of segments. The experimental results on several scenes have demonstrated its effectiveness and robustness.
Keyword:
Label cost
Markov random field (MRF)
satellite image
topic model
unsupervised classification
期刊
IF:
16.4
论文数:
1.0W
被引数:
5.1K
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An Object-Oriented Semantic Clustering Algorithm for High-Resolution Remote Sensing Images Using the Aspect Model基于Aspect模型的高分辨率遥感影像面向对象语义聚类算法.png)
PREAI
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