考虑光谱变异性的多光谱植被识别最优特征空间构建

Optimal Feature Space Construction for Multispectral Vegetation Recognition Considering Endmember Variability

在中低分辨率遥感卫星影像上,植被识别受数据获取条件和不同生长期等因素的影响,会存在端元光谱变异现象,导致植被解混误差较大。提出了一种顾及端元光谱变异性的最佳距离遗传算法(IIDGA),通过自动特征选择方法减小端元类内差异,增大类间差异,构建适用于中等分辨率影像的植被解混最优特征空间,提高Landsat影像的植被识别精度。通过比较传统波段组合、光谱和纹理特征全集与IIDGA优选特征的线性解混模型效果,验证了最优特征选择的重要性。结果显示,特征选择有助于提升解混精度(IIDGA的均方根误差最低,为0.180);同时,通过比较基于IID指数的Filter算法、基于标准GA的Wrapper算法和IIDGA在最优特征自动选取方面的性能,证实了IIDGA在平衡精度与效率方面的优势。

Due to differences in data acquisition and vegetation growth periods,vegetation recognition on low-and medium-resolution remote sensing imagery widely suffers from endmember variability.The endmember variability directly leads to large vegetation unmixing errors.To increase the vegetation recognition accuracy on the multispectral imagery,an intra-inter distance genetic algorithm(IIDGA)that accounts for the endmember variability was proposed.IIDGA can decrease the intra-distance and increase the inter-distance simultaneously,which enhanced the distinguishability of the endmembers through an automatic feature selection method.An optimal feature space for vegetation unmixing was constructed on the medium resolution imagery to improve the vegetation recognition accuracy based on the Landsat imagery.The importance of optimal feature selection was demonstrated by comparing the linear unmixing model accuracy based on the classical band combination features,the spectral and textural feature set and the proposed IIDGA.The results verified that feature selection was beneficial to improve the unmixing accuracy.The RMSE of IIDGA equalled 0.180 which was the lowest among the three methods.Meanwhile,the IID index-based Filter method,the standard GA-based Wrapper method and the proposed method were compared with their performances in automatic optimal feature selection.The results confirmed the superiority of the IIDGA in trading off accuracy and efficiency.