综合光谱纹理和时序信息的油茶遥感提取研究

doi:10.3964/j.issn.1000-0593(2023)05-1589-09

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Abstract Camellia oleifera, which has high nutritional value and is known as oriental “olive oil”, is an important economic forest in southern China, and China has the widest distribution of Camellia oleifera in the world. Extracting the distribution and planting area of Camellia oleifera is significant for forestry departments to carry out macro-management and production guidance of Camellia oleifera. Changning City, Hunan Province, located in a subtropical zone with complex object features and many mountains and hills, is the study area. Many farmland and forests are distributed in this subtropic area, and some vegetation varies greatly in different seasons, which brings great challenges to remote sensing extraction of Camellia oleifera. This paper uses GF-2 high-resolution satellite images in spring, summer and autumn. Combining vegetation index, texture features, PCA principal components, and four different time series combinations in spring and summer, spring and autumn, summer and autumn, and random forest algorithm, 17 classification scenes (S1—S17) were constructed. Three classification algorithms, random forest, support vector machine and maximum likelihood, were used to carry out remote sensing extraction experiments of Camellia oleiferato select the optimal feature combination, classification season, time series combination and optimal classification method. The results show that the classification accuracy based only on spectral information is low, and the addition of texture features can greatly improve the accuracy, while PCA has a weak effect on improving the accuracy; By comparing the classification results of single-period remote sense data in different seasons, it is found that the season with the highest extraction accuracy of Camellia oleifera is summer. With the summer image of the optimal feature combination (S8), the producer accuracy of Camellia oleifera is 94.06%, and the user accuracy of Camellia oleifera is 92.57%. In the classification scenes S10—S17, it is found that the accuracy of using time series information is improved compared with that of single-period images, and the classification accuracy of time series combination from high to low is: spring, summer and autumn, spring and summer, spring and autumn, summer and autumn. Random Forest, Support Vector Machine and Maximum Likelihood are used to extract Camellia oleifera by integrating spectral, texture and time series information, and the classification accuracy of random forest algorithm is the best in general. The therandom forest method (S17) using multi-temporal remote sensing vegetation index, texture and PCA in spring, summer and autumn is the scheme with the highest classification accuracy. The overall accuracy and Kappa coefficient are 96.85% and 0.961 0 respectively, and the producer accuracy of Camellia oleifera is 98.31%, and the user accuracy of Camellia oleifera is 94.33%. The random forest method (S10) using remote sensing vegetation index and texture in spring and summeris the best scheme with calculation efficiency and classification accuracy. The overall accuracy and Kappa coefficient are 95.62% and 0.945 8, respectively. The producer and user accuracy of Camellia oleifera are 96.93% and 95.09%, respectively. The best remote sensing extraction scheme of Camellia oleifera proposed in this paper can provide a reference for remote sensing monitoring of Camellia oleifera and other economic forest extraction in subtropical areas.

Key words：Camellia oleifera; Remote sensing; Time sequence; Vegetation index; Texture features

Received: 2022-03-07 Accepted: 2022-07-05

ZTFLH:

TP753

Corresponding Authors: LI Cun-jun E-mail: licj@nercita.org.cn

Cite this article:

MENG Hao-ran,LI Cun-jun,ZHENG Xiang-yu, et al. Research on Extraction of Camellia Oleifera by Integrating Spectral, Texture and Time Sequence Remote Sensing Information[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(05): 1589-1597.

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https://www.gpxygpfx.com/EN/10.3964/j.issn.1000-0593(2023)05-1589-09 OR https://www.gpxygpfx.com/EN/Y2023/V43/I05/1589

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