Diffusion geometric methods for fusion of remotely sensed data
Diffusion geometric methods for fusion of remotely sensed data
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DOI：
10.1117/12.2305274
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发表时间：
2018-05
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影响因子：
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通讯作者：
James M. Murphy;M. Maggioni
中科院分区：
文献类型：
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作者：
James M. Murphy;M. Maggioni
我们提出了一种新的无监督学习算法，利用图像融合，有效地聚类遥感数据。利用多模态数据的非线性结构，我们设计了一种基于融合特征空间中随机游走的聚类算法。在融合空间上构建随机游走强制像素仅在它们在两种感测模态中接近时才被认为接近。通过这种随机游走学习的结构与密度估计相结合，以标记所有像素。空间信息还可以用于规则化所产生的聚类。我们比较了所提出的方法与几种光谱方法的图像融合的合成和真实的数据。
We propose a novel unsupervised learning algorithm that makes use of image fusion to efficiently cluster remote sensing data. Exploiting nonlinear structures in multimodal data, we devise a clustering algorithm based on a random walk in a fused feature space. Constructing the random walk on the fused space enforces that pixels are considered close only if they are close in both sensing modalities. The structure learned by this random walk is combined with density estimation to label all pixels. Spatial information may also be used to regularize the resulting clusterings. We compare the proposed method with several spectral methods for image fusion on both synthetic and real data.