基于多时相资源一号04星多光谱影像的土地利用分类——以海南西北部地区为例

【目的】基于3期2015年获取的资源一号04星(CBERS-04)多光谱遥感数据,探讨CBERS-04多光谱数据在热带地区土地利用分类中的应用潜力。【方法】结合光谱和物候信息,分别采用最大似然法和决策树分类方法对海南西北部地区土地利用现状进行分类研究。【结果】基于单景的最大似然法可获得相对理想的分类精度,总体分类精度为85.8%~88.8%,卡帕系数为0.80~0.84;同时使用3期影像作为输入,运用最大似然法和决策树分类方法,其分类精度均有明显提升,总体分类精度达91.61%~92.61%,卡帕系数为0.88~0.89,其中最大似然法略优于决策树分类算法。【结论】联合多期CBERS-04多光谱数据能够准确提取热带地区土地利用现状信息,具有广阔的应用前景。

Land utilization classification based on CBERS-04 multi-spectral images---A case study in northwest Hainan,China

Objective]China-Brazil Earth Resources Satellite 4(CBERS-04) multispectral remote sensing data ac-quired in 2015 at three stages were used to explore their potential for land utilization classification in tropical areas.Method]By combining spectral and phenological information, a case study in northwest region of Hainan was conducted using maximum likelihood method and decision tree classification approach based on the CBERS-04 images obtained at three stages. Result]The maximum likelihood method based on single image can achieve relatively ideal classification ac-curacy, reaching an overall classification accuracy of 85.8%-88.8%and kappa coefficient of 0.80-0.84. However, the clas-sification accuracy has improved significantly by inputting multi-temporal images obtained at three stages and using maxi-mum likelihood method and decision tree classification. The overall accuracy reached 91.61%-92.61% and kappa coeffi-cient 0.88-0.89. Conclusion]The study demonstrates the broad application prospects of multiple stages of CBERS-04 multispectral data for land utilization classification in tropical regions.