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首页> 外文期刊>Geoscience and Remote Sensing, IEEE Transactions on >Local Binary Patterns and Extreme Learning Machine for Hyperspectral Imagery Classification
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Local Binary Patterns and Extreme Learning Machine for Hyperspectral Imagery Classification

机译：高光谱图像分类的局部二值模式和极限学习机

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摘要

It is of great interest in exploiting texture information for classification of hyperspectral imagery (HSI) at high spatial resolution. In this paper, a classification paradigm to exploit rich texture information of HSI is proposed. The proposed framework employs local binary patterns (LBPs) to extract local image features, such as edges, corners, and spots. Two levels of fusion (i.e., feature-level fusion and decision-level fusion) are applied to the extracted LBP features along with global Gabor features and original spectral features, where feature-level fusion involves concatenation of multiple features before the pattern classification process while decision-level fusion performs on probability outputs of each individual classification pipeline and soft-decision fusion rule is adopted to merge results from the classifier ensemble. Moreover, the efficient extreme learning machine with a very simple structure is employed as the classifier. Experimental results on several HSI data sets demonstrate that the proposed framework is superior to some traditional alternatives.
机译：在利用纹理信息以高空间分辨率对高光谱图像（HSI）进行分类方面，它引起了极大的兴趣。提出了一种利用HSI丰富的纹理信息的分类范式。提出的框架采用局部二进制模式（LBP）来提取局部图像特征，例如边缘，拐角和斑点。将两级融合（即特征级融合和决策级融合）与全局Gabor特征和原始频谱特征一起应用于提取的LBP特征，其中特征级融合包括在模式分类过程之前将多个特征串联在一起，而决策级融合对每个单独的分类流水线的概率输出执行，并且采用软决策融合规则来合并来自分类器集合的结果。此外，采用结构非常简单的高效极限学习机作为分类器。在多个HSI数据集上的实验结果表明，所提出的框架优于某些传统替代方案。

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