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Integration of full-waveform LiDAR and hyperspectral data to enhance tea and areca classification

机译：整合全波形LiDAR和高光谱数据以增强茶和槟榔的分类

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摘要

Hyperspectral image and full-waveform light detection and ranging (LiDAR) data provide useful spectral and geometric information for classifying land cover. Hyperspectral images contain a large number of bands, thus providing land-cover discrimination. Waveform LiDAR systems record the entire time-varying intensity of a return signal and supply detailed information on geometric distribution of land cover. This study developed an efficient multi-sensor data fusion approach that integrates hyperspectral data and full-waveform LiDAR information on the basis of minimum noise fraction and principal component analysis. Then, support vector machine was used to classify land cover in mountainous areas. Results showed that using multi-sensor fused data achieved better accuracy than using a hyperspectral image alone, with overall accuracy increasing from 83% to 91% using population error matrices, for the test site. The classification accuracies of forest and tea farms exhibited significant improvement when fused data were used. For example, classification results were more complete and compact in tea farms based on fused data. Fused data considered spectral and geometric land-cover information, and increased the discriminability of vegetation classes that provided similar spectral signatures.
机译：高光谱图像和全波形光检测与测距（LiDAR）数据为分类土地覆被提供了有用的光谱和几何信息。高光谱图像包含大量波段，因此提供了土地覆盖判别。波形LiDAR系统记录返回信号的整个时变强度，并提供有关土地覆盖物几何分布的详细信息。这项研究开发了一种有效的多传感器数据融合方法，该方法在最小噪声分数和主成分分析的基础上整合了高光谱数据和全波形LiDAR信息。然后，使用支持向量机对山区的土地覆盖进行分类。结果表明，与单独使用高光谱图像相比，使用多传感器融合数据可获得更高的准确性，对于测试点，使用总体误差矩阵，总体准确性从83％提高至91％。当使用融合数据时，林场和茶园的分类精度显示出显着提高。例如，基于融合数据，茶场的分类结果更加完整和紧凑。融合数据考虑了光谱和几何土地覆盖信息，并增加了提供相似光谱特征的植被类别的可分辨性。

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