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Hyperspectral Image Classification Based on Segmented Local Binary Patterns

机译：基于分段的局部二进制模式的高光谱图像分类

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摘要

Recently, local binary patterns (LBP) coupled with principal component analysis has been developed for feature extraction of hyperspectral imagery, which has shown success over traditional methods but is limited in physical meaning representation due to the noise bands existing in hyperspectral data. In order to preserve the intrinsic geometrical structure of original data, we propose a segmented LBP (SLBP) to group correlative bands and then extract spatial-spectral features from each band group. The proposed approach employs the LBP operator on independent subspaces to characterize local texture information and distinct spectral signatures, along with a decision fusion system further improving discriminant power. The proposed approach is compared with several traditional and state-of-the-art methods on two benchmark datasets (i.e., the Indian Pines dataset and the Salinas Valley dataset). Experimental results demonstrate that the proposed SLBP strategy can yield superior classification performance (96.8% for the Indian Pines dataset with an improvement of approximately 6.4% and 4.2% when compared with LBP and MELBP, respectively; 98.1% for the Salinas Valley dataset with an improvement of approximately 3.5% and 1.3% compared with LBP and MELBP, respectively).
机译：最近，已经开发了具有主成分分析的局部二进制图案（LBP），用于特征提取超细图像，这对传统方法的成功显示了成功，而是由于高光谱数据中存在的噪声频段而受到限制。为了保留原始数据的内在结构，我们将分段的LBP（SLBP）提出到组相关频带，然后从每个频带组提取空间谱特征。所提出的方法采用独立子空间的LBP运算符，以表征局部纹理信息和不同的光谱签名，以及决策融合系统进一步提高判别权力。将所提出的方法与两个基准数据集（即印度松树数据集和SalinaS Valley DataSet）的几种传统和最先进的方法进行比较。实验结果表明，与LBP和MelBP相比，所提出的SLBP策略可以产生卓越的分类性能（印度地区数据集的96.8％，随着LBP和MELBP的增长，约为6.4％和4.2％;萨利纳斯谷数据集98.1％与LBP和MelBP分别相比，约为3.5％和1.3％）。

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