Key message

We found high accuracy classification (F_{measure =} 95%, on cross-validation) of Araucaria angustifolia (Bertol.) Kuntze, an endangered native species, and Hovenia dulcis Thunb. an aggressive, invasive alien species in WorldView-2 multispectral images. In applying machine learning algorithms, the spectral attributes mainly related to the near-infrared band were the most important for the models.

Context

It is difficult to classify tree species in tropical rainforests due to the high spectral response’s diversity of existing species, as well as to adjust efficient machine learning techniques and orbital image resolution.

Aims

To explore the spectral and textural response of an endangered species (A. angustifolia) and an invasive species (H. dulcis) in WorldView-2 multispectral images, testing its recognition capability by machine learning techniques.

Methods

We used a WordView-2 (2016) image with 0.5-m spatial resolution. Then we manually clipped the canopy area of the two species in this image using two compositions: True color composition (R=660 nm, G=545 nm, B=480 nm) and near-infrared composition (NIR-2=950 nm, G=545 nm, B=480 nm). Thus, we applied spectral and textural descriptors (pyramid histogram of oriented gradients—PHOG and Edge Filter), which selects the most representative features of the dataset. Finally, we used artificial neural networks (ANN) and random forest (RF) for tree species classification.

Results

The species classification was performed with high accuracy (F_{measure =} 95%, on cross-validation), essentially for spectral attributes using the near-infrared composition. RF surpassed the ANN classification rates and also proved to be more stable and faster for training and testing.

Conclusion

The WorldView-2 multispectral sensor showed the potential to provide sufficient information for classifying two species, proving its usefulness in this phytophysiognomy where hyperspectral sensors are generally used for this type of classification.

中文翻译：

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机器学习和 WorldView-2 图像识别大西洋雨林中濒危和入侵物种的潜力

关键信息

我们发现Araucaria angustifolia (Bertol.) Kuntze（一种濒临灭绝的本地物种）和Hovenia dulcis Thunb 的高精度分类（F_{值 =} 95%，在交叉验证中） 。WorldView-2 多光谱图像中的侵略性外来入侵物种。在应用机器学习算法时，主要与近红外波段相关的光谱属性对模型来说是最重要的。

语境

由于现有物种的高光谱响应多样性，以及调整有效的机器学习技术和轨道图像分辨率，热带雨林中的树种难以分类。

宗旨

探索WorldView-2 多光谱图像中濒危物种 ( A. angustifolia ) 和入侵物种 ( H. dulcis )的光谱和纹理响应，通过机器学习技术测试其识别能力。

方法

我们使用了空间分辨率为 0.5 米的 WordView-2 (2016) 图像。然后我们使用两种成分手动剪裁该图像中两个物种的冠层区域：真彩色成分（R = 660 nm，G = 545 nm，B = 480 nm）和近红外成分（NIR-2 = 950 nm， G=545 纳米，B=480 纳米）。因此，我们应用了光谱和纹理描述符（定向梯度的金字塔直方图——PHOG 和边缘过滤器），它选择了数据集最具代表性的特征。最后，我们使用人工神经网络 (ANN) 和随机森林 (RF) 进行树种分类。

结果

物种分类以高精度执行（F_{度量 =} 95%，在交叉验证中），主要用于使用近红外成分的光谱属性。RF 超过了 ANN 分类率，并且也被证明在训练和测试中更加稳定和快速。

结论

WorldView-2 多光谱传感器显示出为两个物种的分类提供足够信息的潜力，证明了它在这种植物地貌学中的有用性，其中高光谱传感器通常用于此类分类。