【摘要】 文章以合肥市区TM影像为数据源,采用非负最小二乘算法,解决ENVI等商业软件在该算法上存在的不足;对数据进行线性波谱分离,通过对丰度总和数据和残余均方根误差等指标的分析,对TM影像进行了解析;对于TM图像土地覆盖中的混合像元限制分类精度的问题,采用对TM图像MNF变换结果的前5个主成分进行PPI指数计算,并对PPI阈值切割结果进行N维可视化;采用波谱均值作为端元波谱,选择端元数为5,对2种端元类型方案、4种不同的波谱特征进行数值处理,并以模型自身的条件约束等进行验证和样地数据的检核,发现该方法的有效性。更多还原

【Abstract】 The non-negative least-square algorithm was employed to accomplish linear spectral unmixing(LSU) with Hefei TM images as data source to overcome the inadequacy of the ENVI software.The TM images were resolved based on the analysis of the data abundance,sum and residual root mean square error.In view of the accuracy problem of mixed pixel limit classification in TM images for land cover,PPI index of the first five principal components of the results of TM image MNF transform was calculated and N dimension visualization was performed on PPI threshold cutting results.The numerical process was performed on two types of end member scheme and four kinds of spectral characteristics using spectral mean as end member spectrum and selecting five end members.It is found that the method is effective for the results were confirmed by the constraint condition and checked by sample data.更多还原