摘要
混合像元的存在不仅影响了基于高光谱影像的地物识别和分类精度,而且已成为遥感科学向定量化发展的主要障碍。本文以扎龙湿地为试验区,以环境一号卫星采集的高光谱影像为数据源,分别采用传统的全约束最小二乘光谱解混算法(fully constrained least squares spectral unmixing algorithm, FCLS)与基于稀疏约束最小二乘光谱解混算法(sparse constrained least squares spectral unmixing algorithm, SUFCLS)实现了试验区湿地的精细分类,并对两种分类结果的表现及其分类精度进行了对比分析。研究结果表明:SUFCLS算法能够自适应的从光谱库中选择场景中所占比例最高的一组端元,并将此端元的组合应用于传统的全约束最小二乘光谱解混中实现不同湿地类型丰度的提取,该算法充分考虑了端元的空间异质性,弥补了FCLS算法在端元选取过程中的不足。精度验证结果表明与FCLS算法相比,SUFCLS算法分类结果的均方根误差更小,丰度的相关系数更高,因此该方法对于提高湿地解混精度以及实现湿地精细化分类具有重要意义。
The existence of mixed pixels not only affects land cover type recognition and classification accuracy based on hyperspectral images, but also has become a major obstacle to the quantitative development of remote sensing science. Taking the Zhalong Nature Reserve as a study area, the current study compared the performance of the sparse constrained least squares spectral unmixing algorithm (SUFCLS) and the fully constrained least squares spectral unmixing algorithm (FCLS) for wetland remote sensing classification. The classification accuracy and errors of the two algorithms were evaluated and analyzed. The results show that the SUFCLS algorithm adaptively selected the highest percentage endmember combination from the spectral library, and integrated the selected endmembers into the FCLS algorithm to conduct the abundance inversion. Having considered the spatial heterogeneity of endmembers, the SUFCLS algorithm overcomes the shortcoming of the FCLS algorithm during the process of endmembers selection. Compared with the FCLS, higher correlation was observed between the classification results of SUFCLS and the abundance of the wetland communities (reed swamp, cattail marsh, leymus chinensis meadow, and weed meadow) visually interpreted from the highresolution imagery. In addition, the root mean square error (RMSE) decreased, which indicates that the SUFCLS algorithm has an important significance in improving wetland unmixing accuracies and implementing wetland high precision classification.
作者
张雅春
那晓东
臧淑英
ZHANG Yachun;NA Xiaodong;ZANG Shuying(Heilongjiang Province Key Laboratory of Geographical Environment Monitoring and Spatial Information Service in Cold Regions, Harbin Normal University, Harbin 150025, China)
出处
《地理科学进展》
CSSCI
CSCD
北大核心
2018年第12期1705-1712,共8页
Progress in Geography
基金
国家自然科学基金项目(41001243
41571199)
黑龙江省普通本科高等学校青年创新人才培养计划项目(UNPYSCT-2016073)
哈尔滨师范大学优青项目(XRQG15)~~
关键词
高光谱影像
稀疏解混
线性解混
湿地分类
扎龙自然保护区
hyperspectral image
sparse unmixing
linear unmixing
wetland classification
Zhalong Natural Reserve
