Crataegus songarica recognition using Gaofen-6 wide-field-view data assisted by multiple features
Received date: 2020-12-03
Revised date: 2021-01-27
Online published: 2021-04-25
Our ability to extract spatial distribution information for Crataegus songarica by remote sensing is relatively weak, and the performance of a single algorithm in information extraction is different. To address this problem, we explored the application potential of domestic Gaofen-6 (GF-6) wide-field-view (WFV) data in tree species recognition in arid and semi-arid areas. Using GF-6 WFV and Ziyuan-3 (ZY-3) data, we constructed a combined classifier recognition algorithm with multi-feature assistance. First, we combined a linear spectral clustering algorithm for ZY-3 data super-pixel segmentation with the sample set to determine the optimal scale, avoid the salt-and-pepper phenomenon and improve the recognition accuracy. Second, we extracted spectral features, texture features, and vegetation index features based on multi-source data, using the recursive feature elimination method to select classification features. Feature importance was evaluated based on the mean decrease impurity to construct the optimal classification feature set, improve the separability between feature classes, and reduce the redundancy between features. Finally, we constructed a weight-adaptive voting combination classifier based on the support vector machine algorithm and random forest algorithm. Combined with a variety of classification schemes, the spatial distribution of Crataegus songarica was extracted and verified to evaluate the influence of object-oriented algorithm, multi-classifier combination algorithm, and feature selection on the classification accuracy. The results showed that GF-6 WFV data can be used to identify Crataegus songarica and have great application potential in forestry production. Compared with GF-1 WFV, two new red-edge bands of GF-6 WFV play an important role in the identification of Crataegus songarica. Compared with traditional pixel-based methods, the object-based recognition of Crataegus songarica effectively mitigated the pepper salt phenomenon and significantly improved the recognition accuracy, algorithm efficiency, and robustness. Moreover, the feature selection effectively reduced the redundancy between classification features and improved the computational efficiency and algorithm stability; the selected features significantly improved the accuracy. Using a weight-adaptive multi-classifier voting combination algorithm can integrate the advantages of different algorithms, effectively avoid the partial ‘confusion’ caused by the representation force difference of the single classifier, and improve the recognition accuracy.
CHEN Chunxiu,CHEN Shujiang,XU Shiwei,CHEN Mengyu,JIA Xiang,HUANG Tiecheng,LI Chunlei . Crataegus songarica recognition using Gaofen-6 wide-field-view data assisted by multiple features[J]. Arid Zone Research, 2021 , 38(2) : 553 -561 . DOI: 10.13866/j.azr.2021.02.27
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