Learning Sparse CRFs for Feature Selection and Classification of Hyperspectral Imagery
Learning Sparse CRFs for Feature Selection and Classification of Hyperspectral Imagery
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DOI：
10.1109/tgrs.2008.2001921
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发表时间：
2008-12
影响因子：
8.2
通讯作者：
P. Zhong;Runsheng Wang
中科院分区：
文献类型：
--
作者：
P. Zhong;Runsheng Wang
特征选择是高光谱数据分析中的一项重要任务。本文提出了一种稀疏条件随机场（SCRF）模型，选择相关的功能，高光谱图像的分类，同时，利用上下文信息的形式在图像中的空间依赖性。稀疏性源于对CRF参数使用拉普拉斯先验，这促使参数估计值显着较大或恰好为零。为了将特征选择和分类器设计结合起来，本文提出了一种有效的稀疏训练方法，该方法将SCRF的训练分为两个简单分类器的稀疏训练。在真实高光谱图像上的实验证明了该模型的准确性、稀疏性和有效性。
Feature selection is an important task in hyperspectral data analysis. This paper presents a sparse conditional random field (SCRF) model to select relevant features for the classification of hyperspectral images and, meanwhile, to exploit the contextual information in the form of spatial dependences in the images. The sparsity arises from the use of a Laplacian prior on the CRF parameters, which encourages the parameter estimates to be either significantly large or exactly zero. To joint the feature selection and classifier design, this paper develops an efficient sparse training method, which divides the training of SCRF into the sparse trainings of two simpler classifiers. Experiments on the real-world hyperspectral image attest to the accuracy, sparsity, and efficiency of the proposed model.