基于改进光谱角法的红树林高分遥感分类方法研究

传统影像分类方法多利用影像端元光谱进行地物分类，影像的空间结构信息被忽视，本研究结合面向对象分类方法思想以提高红树林遥感分类精度。本研究提出了一种结合端元类型选择、像元提纯等混合像元分解手段及分水岭图像分割算法的改进光谱角影像分类方法，并以山口红树林国家级自然保护区为研究区，利用GF-1号遥感影像数据，在光谱特征分析和地面调查的基础上，对红树林生态系统进行分类，并对分类精度进行分析。研究结果表明:改进的光谱角分类方法对GF-1影像分类效果较好，既兼顾地类光谱组成较复杂时的特殊性，又有效避免结果的破碎化现象，且总体精度达到95%（KAPPA系数0.944），证明了其在红树林遥感影像分类及信息提取方面的应用潜力，为红树林生态系统业务化遥感监测奠定了基础。

Research on high resolution remote sensing mangrove classification method based on improved spectral angle mapper

The traditional image classification methods mostly use endmember spectrum to classify ground objects, however, the spatial structure information of images is ignored. In this study, an improved spectral angle mapper image classification method combining hybrid pixel decomposition means such as endmember type selection and pixel purification and watershed image segmentation algorithm is proposed, and the mangrove ecosystem is classified based on spectral feature analysis and ground survey using GF-1 remote sensing image data with Shankou Mangrove National Nature Reserve as the study area, and the classification accuracy is also analyzed. The research results show that the improved spectral angle mapper classification method is effective in classifying GF-1 images, taking into account the specificity of the complex spectral composition of the ground class and effectively avoiding the fragmentation of the results, and the overall accuracy reaches 95% with the KAPPA coefficient of 0.944, which proves its application potential in mangrove remote sensing image classification and information extraction, and lays a foundation for the operational remote sensing monitoring of mangrove ecosystem.