Identification of dominant tree species based on Resource-1 02D hyperspectral image data

Jingchun Zhou, Zhanyong Feng, Yiping Li, Jinliang Wang, Xiangrui Meng, Yuan Liu, Shaobo Qiu
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Abstract

Fine-grained classification of tree species by using high-spectral image data has garnered considerable attention from scholars. In this study, through field measurements from Maguan County, Wenshan Prefecture, Yunnan Province, China, high-spectral image data from the Chinese Resource-1 02D satellite were used as the data source. Various analyses were conducted on the original image’s spectral curve, the spectral curve after envelope removal, the spectral curve after first-order differential transformation, and the spectral curve after second-order differential transformation. A spectral angle mapping classification method was employed to classify and identify four dominant tree species in Maguan County, and the accuracy of the classification results was validated using a confusion matrix. Results indicate that the highest accuracy in tree species classification was achieved when first-order differential transformation and envelope removal were used for the spectral curve; the overall accuracy exceeded 95%, and the kappa value was approximately 0.95. The classification results for the spectral curve after second-order differential transformation were the lowest, with an overall accuracy of 81.69% and a kappa value of 0.76. This research demonstrates that applying first-order differential transformation or envelope removal in combination with spectral angle mapping classification considerably reduces data processing time and improves tree species classification accuracy.
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根据 Resource-1 02D 高光谱图像数据识别主要树种
利用高光谱影像数据对树种进行精细分类已受到学者们的广泛关注。本研究通过对中国云南省文山州马关县的实地测量,以中国资源一号02D卫星的高光谱影像数据为数据源。对原始图像的光谱曲线、去除包络线后的光谱曲线、一阶微分变换后的光谱曲线和二阶微分变换后的光谱曲线进行了各种分析。采用光谱角度映射分类法对马关县的四种优势树种进行了分类和识别,并利用混淆矩阵验证了分类结果的准确性。结果表明,对光谱曲线采用一阶微分变换和去除包络线时,树种分类的准确率最高;总体准确率超过 95%,kappa 值约为 0.95。二阶微分变换后的光谱曲线分类结果最低,总体准确率为 81.69%,卡帕值为 0.76。这项研究表明,将一阶微分变换或包络去除与光谱角度映射分类相结合可大大减少数据处理时间,并提高树种分类的准确性。
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