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首页> 外文期刊>IEEE Transactions on Geoscience and Remote Sensing >Shapelet-Based Sparse Representation for Landcover Classification of Hyperspectral Images
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Shapelet-Based Sparse Representation for Landcover Classification of Hyperspectral Images

机译：基于形状的稀疏表示的高光谱图像土地覆盖分类

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摘要

This paper presents a sparse-representation-based classification approach with a novel dictionary construction procedure. By using the constructed dictionary, sophisticated prior knowledge about the spatial nature of the image can be integrated. The approach is based on the assumption that each image patch can be factorized into characteristic spatial patterns, also called shapelets, and patch-specific spectral information. A set of shapelets is learned in an unsupervised way, and spectral information is embodied by training samples. A combination of shapelets and spectral information is represented in an undercomplete spatial–spectral dictionary for each individual patch, where the elements of the dictionary are linearly combined to a sparse representation of the patch. The patch-based classification is obtained by means of the representation error. Experiments are conducted on three well-known hyperspectral image data sets. They illustrate that our proposed approach shows superior results in comparison to sparse-representation-based classifiers that use only limited spatial information and behaves competitively with or better than state-of-the-art classifiers utilizing spatial information and kernelized sparse-representation-based classifiers.
机译：本文提出了一种基于稀疏表示的分类方法，并提出了一种新颖的词典构建程序。通过使用构造的字典，可以集成有关图像空间性质的高级先验知识。该方法基于这样的假设，即每个图像补丁都可以分解为特征空间模式（也称为小波）和补丁特定的光谱信息。一组小波以无监督的方式学习，而光谱信息则通过训练样本来体现。小波和频谱信息的组合在每个单独的补丁的不完整空间光谱字典中表示，其中字典中的元素线性组合为补丁的稀疏表示。通过表示误差获得基于补丁的分类。对三个众所周知的高光谱图像数据集进行了实验。他们表明，与仅使用有限空间信息的基于稀疏表示的分类器相比，我们提出的方法显示出了更好的结果，该分类器与使用空间信息和带核的基于稀疏表示的分类器的最新分类器相比，表现甚至更好。

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