Segmented principal components transformation for efficient hyperspectral remote-sensing image display and classification
Segmented principal components transformation for efficient hyperspectral remote-sensing image display and classification
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DOI：
10.1109/36.739109
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发表时间：
1999-01-01
影响因子：
8.2
通讯作者：
Richards, JA
中科院分区：
文献类型：
--
作者：
Jia, XP;Richards, JA
在本文中，提出了一种分段的、可能是多级的主成分变换（PCT），用于高效的高光谱遥感图像分类和显示。该方案最初需要将完整的频带集划分为几个高度相关的子组。对每个子组进行单独变换后，以单带可分离性为指导进行特征选择。然后可以再次转换所选特征以实现令人满意的数据缩减率并生成用于彩色显示的三个最重要的分量。与传统的PCT相比，该方案显着减少了特征提取的计算量。减少特征数量还将显着加速最大似然分类过程，并且该过程不会受到在训练样本有限时尝试使用全套高光谱数据所遇到的限制。使用两个机载可见光/红外成像光谱仪 (AVIRIS) 数据集，在分类精度、速度和彩色图像显示质量方面取得了令人鼓舞的结果。
In this paper, a segmented, and possibly multistage, principal components transformation (PCT) is proposed for efficient hyperspectral remote-sensing image classification and display. The scheme requires, initially, partitioning the complete set of bands into several highly correlated subgroups. After separate transformation of each subgroup, the single-band separabilities are used as a guide to carry out feature selection. The selected features can then be transformed again to achieve a satisfactory data reduction ratio and generate the three most significant components for color display. The scheme reduces the computational load significantly for feature extraction, compared with the conventional PCT. A reduced number of features will also accelerate the maximum likelihood classification process significantly, and the process will not suffer the limitations encountered by trying to use the full set of hyperspectral data when training samples are limited. Encouraging results have been obtained in terms of classification accuracy, speed, and quality of color image display using two airborne visible/infrared imaging spectrometer (AVIRIS) data sets.