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首页> 外文会议>2015 International Conference on Energy Systems and Applications >Efficient marker controlled watershed algorithm for classification of hyperspectral images
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Efficient marker controlled watershed algorithm for classification of hyperspectral images

机译：用于高光谱图像分类的高效标记控制分水岭算法

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摘要

Hyperspectral image classification becomes a prominent topic in remote sensing. Hyperspectral image provides in detail spectral and spatial information about earth surface object. With the help of spectral and spatial information, it is highly possible to distinguish spectrally similar objects. But hyperspectral images are with hundreds of spectral bands which lead to lacking the availability labeled samples and high cost of computation. To identify earth surface objects accurately, both issues such as large number of spectral channels and limited availability of training samples should be addressed properly in classification tasks. In this paper, we divide a large dataset into regions with watershed segmentation algorithm and then conducting coarse to fine hypergraph construction. In the first layer, first we compute the pairwise relevance, which fed to the second layer from which hypergraph is constructed in the second layer. Semisupervised learning is employed on hypergraph to obtain a final classification map. In our proposed system segmentation helps to reduce the computation burden while coarse to fine hypergraph based learning helps to tackle issues such as high dimensionality and few training samples.
机译：高光谱图像分类成为遥感领域的一个突出课题。高光谱图像提供有关地球表面物体的详细光谱和空间信息。借助光谱和空间信息，很有可能区分光谱相似的物体。但是高光谱图像具有数百个光谱带，这导致缺乏可用标记的样本和较高的计算成本。为了准确地识别地面物体，应该在分类任务中正确解决诸如大量光谱通道和训练样本数量有限之类的问题。在本文中，我们使用分水岭分割算法将大型数据集划分为多个区域，然后进行从粗到细的超图构造。在第一层中，首先我们计算成对相关性，该对相关性被馈送到第二层，在第二层中从该第二层构造了超图。在超图上采用半监督学习以获得最终的分类图。在我们提出的系统中，分段有助于减少计算负担，而从粗到精细的基于超图的学习有助于解决诸如高维数和少量训练样本之类的问题。

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