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首页> 外文期刊>Photogrammetric Engineering & Remote Sensing: Journal of the American Society of Photogrammetry >Hyperspectral Data Dimensionality Reduction and the Impact of Multi-seasonal Hyperion EO-1 Imagery on Classification Accuracies of Tropical Forest Species
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Hyperspectral Data Dimensionality Reduction and the Impact of Multi-seasonal Hyperion EO-1 Imagery on Classification Accuracies of Tropical Forest Species

机译：高光谱数据降维及多季节Hyperion EO-1影像对热带森林物种分类准确性的影响

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摘要

Synchronizing hyperspectral data acquisition with phenological changes in a tropical forest can generate comprehensive information for their effective management. The present study was performed to identify a suitable dimensionality reduction method for better classification and to evaluate the impact of seasonality on classification accuracy of tropical forest cover. EO-1 Hyperion images were acquired for three different seasons (summer (April). monsoon (October), and winter (January)). Spectral signatures of pure patches of Teak. Bamboo, and mixed species covers are significantly different across the three seasons indicating distinctive phenology of each cover. Kernel Principal Component Analysis (k-PcA) is more suitable for dimensionality reduction for these covers. The three vegetation covers classified using images of three seasons achieved the best classification accuracies using k-PcA with maximum likelihood classifier for the monsoon season with overall accuracies of 83 to 100 percent for single species, 74 to 81 percent for two species, and 72 percent for three species respectively
机译：在热带森林中将高光谱数据采集与物候变化同步可以为有效管理提供全面的信息。进行本研究是为了确定合适的降维方法以实现更好的分类，并评估季节性对热带森林覆盖物分类精度的影响。在三个不同的季节（夏季（4月），季风（10月）和冬季（1月））采集了EO-1 Hyperion图像。柚木纯色斑的光谱特征。竹子和混合物种的覆盖物在三个季节中有显着差异，表明每种覆盖物的物候特征都不同。内核主成分分析（k-PcA）更适合于降低这些封面的尺寸。使用三个季节的图像进行分类的三个植被覆盖度使用k-PcA和季风季节的最大似然分类器实现了最佳分类精度，单个物种的总体精度为83％至100％，两个物种的总体精度为74％至81％，而72％分别针对三种

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