Crown-level tree species classification is a challenging task due to the spectral similarity among different tree species. Shadow, underlying objects, and other materials within a crown may decrease the purity of extracted crown spectra and further reduce classification accuracy. To address this problem, an innovative pixel-weighting approach was developed for tree species classification at the crown level. The method utilized high density discrete LiDAR data for individual tree delineation and Airborne Imaging Spectrometer for Applications (AISA) hyperspectral imagery for pure crown-scale spectra extraction. Specifically, three steps were included: 1) individual tree identification using LiDAR data, 2) pixel-weighted representative crown spectra calculation using hyperspectral imagery, with which pixel-based illuminated-leaf fractions estimated using a linear spectral mixture analysis (LSMA) were employed as weighted factors, and 3) representative spectra based tree species classification was performed through applying a support vector machine (SVM) approach. Analysis of results suggests that the developed pixel-weighting approach (OA = 82.12%, Kc = 0.74) performed better than treetop-based (OA = 70.86%, Kc = 0.58) and pixel-majority methods (OA = 72.26, Kc = 0.62) in terms of classification accuracy. McNemar tests indicated the differences in accuracy between pixel-weighting and treetop-based approaches as well as that between pixel-weighting and pixel-majority approaches were statistically significant.

由于不同树种之间的光谱相似性，树冠级别树种分类是一项具有挑战性的任务。牙冠内的阴影，底层物体和其他材料可能会降低提取的牙冠光谱的纯度，并进一步降低分类精度。为了解决这个问题，开发了一种创新的像素加权方法来对树冠级别的树种进行分类。该方法利用高密度离散LiDAR数据进行单个树描绘，并使用机载成像光谱仪（AISA）高光谱图像进行纯冠尺度光谱提取。具体来说，包括以下三个步骤：1）使用LiDAR数据识别单个树，2）使用高光谱图像计算像素加权的代表性树冠光谱，使用线性光谱混合分析（LSMA）估计的基于像素的照叶分数作为加权因子，并且3）通过应用支持向量机（SVM）方法进行基于代表性光谱的树种分类。结果分析表明，开发的像素加权方法（OA = 82.12％，Kc = 0.74）优于基于树顶的方法（OA = 70.86％，Kc = 0.58）和像素多数方法（OA = 72.26，Kc = 0.62） ）的分类准确性。McNemar测试表明，像素加权和基于树梢的方法之间的准确性差异以及像素加权和多数像素方法之间的准确性差异在统计学上是显着的。3）通过应用支持向量机（SVM）方法进行基于代表性光谱的树种分类。结果分析表明，开发的像素加权方法（OA = 82.12％，Kc = 0.74）优于基于树顶的方法（OA = 70.86％，Kc = 0.58）和像素多数方法（OA = 72.26，Kc = 0.62） ）的分类准确性。McNemar测试表明，像素加权和基于树梢的方法之间的准确性差异以及像素加权和多数像素方法之间的准确性差异在统计学上是显着的。3）通过应用支持向量机（SVM）方法进行基于代表性光谱的树种分类。结果分析表明，开发的像素加权方法（OA = 82.12％，Kc = 0.74）优于基于树顶的方法（OA = 70.86％，Kc = 0.58）和像素多数方法（OA = 72.26，Kc = 0.62） ）的分类准确性。McNemar测试表明，像素加权和基于树梢的方法之间的准确性差异以及像素加权和多数像素方法之间的准确性差异在统计学上是显着的。58）和像素多数方法（OA = 72.26，Kc = 0.62）。McNemar测试表明，像素加权和基于树梢的方法之间的准确性差异以及像素加权和多数像素方法之间的准确性差异在统计学上是显着的。58）和像素多数方法（OA = 72.26，Kc = 0.62）。McNemar测试表明，像素加权和基于树梢的方法之间的准确性差异以及像素加权和多数像素方法之间的准确性差异在统计学上是显着的。