Feature fusion methods in remote sensing

最新推荐文章于 2024-02-26 10:51:59 发布

银木荷

最新推荐文章于 2024-02-26 10:51:59 发布

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分类专栏： matlab

本文链接：https://blog.youkuaiyun.com/qq_41300185/article/details/87924478

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本文介绍了几种特征融合方法，包括后期精炼、直接特征融合和分类后比较。直接特征融合中，重点讨论了自组织映射（SOM）和最小噪声分数（MNF）两种方法，它们分别通过考虑邻居集群的影响和改进PCA方法来提高特征融合的效果。

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Today, I learned some feature fusion methods as follows (refer to the literature of qinrongjun 3D CD reviews):

1. Post-refinement

2. Direct feature fusion: considering all channels of information simultaneously

3. Post-classification comparison: first, conducting objects extration or classification by changeing the spectral values or feature values into label images. Then, compare labels.

The considering part is part 2.

1) SOM: self-organizing map (a kind of clustering methods like k-means) it considers not only its own cluster, but also has an influence on neighbouring clusters. ( code: refer to MATLAB)

2) MNF: minimum noise fraction which improves PCA methods by replacing the variance-based order with noise-based one, with an increase of noise robustness.

date: 20190225