Lin Chinsu, Popescu Sorin C, Thomson Gavin, Tsogt Khongor, Chang Chein-I
Department of Forestry and Natural Resources, National Chiayi University, 300 University Road, Chiayi 60004, Taiwan.
Department of Ecosystem Science and Management, Texas A&M University, College Station, TX, 77843, United States of America.
PLoS One. 2015 May 15;10(5):e0125554. doi: 10.1371/journal.pone.0125554. eCollection 2015.
This paper proposes a supervised classification scheme to identify 40 tree species (2 coniferous, 38 broadleaf) belonging to 22 families and 36 genera in high spatial resolution QuickBird multispectral images (HMS). Overall kappa coefficient (OKC) and species conditional kappa coefficients (SCKC) were used to evaluate classification performance in training samples and estimate accuracy and uncertainty in test samples. Baseline classification performance using HMS images and vegetation index (VI) images were evaluated with an OKC value of 0.58 and 0.48 respectively, but performance improved significantly (up to 0.99) when used in combination with an HMS spectral-spatial texture image (SpecTex). One of the 40 species had very high conditional kappa coefficient performance (SCKC ≥ 0.95) using 4-band HMS and 5-band VIs images, but, only five species had lower performance (0.68 ≤ SCKC ≤ 0.94) using the SpecTex images. When SpecTex images were combined with a Visible Atmospherically Resistant Index (VARI), there was a significant improvement in performance in the training samples. The same level of improvement could not be replicated in the test samples indicating that a high degree of uncertainty exists in species classification accuracy which may be due to individual tree crown density, leaf greenness (inter-canopy gaps), and noise in the background environment (intra-canopy gaps). These factors increase uncertainty in the spectral texture features and therefore represent potential problems when using pixel-based classification techniques for multi-species classification.
本文提出了一种监督分类方案，用于在高空间分辨率的快鸟多光谱图像（HMS）中识别属于22科36属的40种树木（2种针叶树，38种阔叶树）。总体kappa系数（OKC）和物种条件kappa系数（SCKC）用于评估训练样本中的分类性能，并估计测试样本中的准确性和不确定性。使用HMS图像和植被指数（VI）图像的基线分类性能分别用OKC值0.58和0.48进行评估，但与HMS光谱空间纹理图像（SpecTex）结合使用时性能显著提高（高达0.99）。使用4波段HMS和5波段VI图像时，40种物种中的一种具有非常高的条件kappa系数性能（SCKC≥0.95），但使用SpecTex图像时，只有5种物种具有较低的性能（0.68≤SCKC≤0.94）。当SpecTex图像与可见大气抗性指数（VARI）结合使用时，训练样本中的性能有显著提高。在测试样本中无法复制相同程度的提高，这表明物种分类准确性存在高度不确定性，这可能是由于单个树冠密度、叶片绿色度（树冠间隙）和背景环境中的噪声（树冠内间隙）。这些因素增加了光谱纹理特征的不确定性，因此在使用基于像素的分类技术进行多物种分类时代表了潜在问题。
