Shapelet-based sparse image representation for landcover classification of hyperspectral data
Shapelet-based sparse image representation for landcover classification of hyperspectral data
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DOI：
10.1109/prrs.2014.6914277
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发表时间：
2014-10
期刊：
影响因子：
--
通讯作者：
R. Roscher;B. Waske
中科院分区：
文献类型：
--
作者：
R. Roscher;B. Waske
本文提出了一种新颖的基于稀疏表示的分类器，用于高光谱图像数据的土地覆盖绘图。每个图像块都被分解为分割模式（也称为 shapelet）和特定于块的光谱特征。两者的组合在特定于块的空间光谱字典中表示，该字典用于图像块的重建和分类的稀疏编码过程。因此，每个图像块都由字典中元素的线性组合稀疏表示。为了捕获图像结构，以无监督的方式专门为每个图像学习该 shapelet 集合。假设光谱特征是训练数据。实验表明，与不使用或仅使用有限空间信息的基于稀疏表示的分类器相比，所提出的方法显示出更好的结果，并且比利用空间信息和基于核化稀疏表示的分类器的最先进的分类器具有竞争力或更好。
This paper presents a novel sparse representation-based classifier for landcover mapping of hyperspectral image data. Each image patch is factorized into segmentation patterns, also called shapelets, and patch-specific spectral features. The combination of both is represented in a patch-specific spatial-spectral dictionary, which is used for a sparse coding procedure for the reconstruction and classification of image patches. Hereby, each image patch is sparsely represented by a linear combination of elements out of the dictionary. The set of shapelets is specifically learned for each image in an unsupervised way in order to capture the image structure. The spectral features are assumed to be the training data. The experiments show that the proposed approach shows superior results in comparison to sparse-representation based classifiers that use no or only limited spatial information and behaves competitive or better than state-of-the-art classifiers utilizing spatial information and kernelized sparse representation-based classifiers.