基于多时相SAR数据和SPOT数据的盘古林场林分类型识别

Forest Type Classification Based on Multi-temporal SAR and SPOT Remote Sensing Data in Pangu Forest Farm

林分类型信息的提取是遥感影像分类中的热点和难点。而大兴安岭地区又是我国重点林区和天然林主要分布区之一,植被类型丰富,种类繁多,为林分类型精确识别带来了很大的难度。为了比较和提高林分类型的分类精度,研究以大兴安岭地区盘古林场为实验区,综合利用SPOT-5影像和不同时相的RADARSAT-2全极化SAR影像,采用3种分类方案及最大似然分类方法对研究区遥感影像进行分类,并比较不同分类方案对林分类型识别的精度。3种方案分别是:(1)单独采用SPOT影像对林分类型进行识别;(2)对全极化SAR数据进行极化分解提取参数并结合SPOT数据参与分类;(3)结合SPOT数据与多时相全极化SAR分解参数进行分类。结果表明:对比SPOT、加入单时相和加入多时相3种方案的分类结果,方案三加入多时相SAR影像与SPOT数据对白桦林、落叶松林、樟子松林和云杉林的分类中总分类精度最高,为84.64%,Kappa系数为0.79,对林分类型的识别最为有效,而单用SPOT数据对林分类型识别的精度最低,精度为76.66%,Kappa系数为0.70。

Information extraction of forest type is difficult in remote sensing image classification. Daxing＇ an Mountains is an important forestry area in China mainly covered with natural forests, rich with a wide range of plants resources, which makes it difficult to accurately identify the forest types in this region. In order to compare and improve the accuracy of classification result, taking Pangu Forest Farm in Daxing＇ an Mountains as the study area, we proposed three methods to classify forest types by the maximum likelihood method combining with SPOT-5 and two different temporal RADARSAT-2 fully polarimetric SAR remote sensing data. We designed three schemes to classify the forest types and compared the accuracy. In the three schemes, SPOT image was only used to distinguish forest types, some descriptive parameters extracted from SAR polarimetry （POLSAR） images and the SPOT data were used for classification, and the integration of parameters extracted from multi- temporal of full polarimetric SAR （PolSAR） images with SPOT data was used for classification. The most effective method to identify white birch, larch, Pinus sylvestris and spruce among three proposed schemes was the third using multi-temporal SAR and SPOT remote sensing image. The classification accuracy and the Kappa coefficient were 84.64% and 0.79, respectively. However, the accuracy of forest type classification by using SPOT data individually was the lowest of 76.66% with the Kappa coefficient of 0.70.