变端元混合像元分解冬小麦种植面积测量方法

针对线性混合像元分解(Linear Spectral Unmixing,LSU)在端元(Endmember)个数不变情况下常会出现端元分解过剩现象导致分解结果精度不高的问题,以地物分布的聚集性特征为基础,提出了基于格网的变端元线性混合像元分解(Dynamic Endmember LSU,DELSU)方法.以冬小麦为研究...

The Application of the Dynamic Endmember Linear Spectral Unmixing Model to Winter Wheat Area Estimation

Linear spectral unmixing（LSU）is the most common method for solving mixed pixel problem; nevertheless, if the number of the pixels’ endmember is regarded as unchangeable, the traditional pixel unmixing algorithm cannot attain a good result. In the light of the characteristic that pixels usually have the same composition as their neighboring pixels, the authors proposed a grid-based dynamic endmember linear spectral unmixing（DELSU） model.  The land cover classification experiment was conducted with the adoption of the Landsat TM image as the experimental data. The abundance map of winter wheat derived from the visual interpretation of the QuickBird image was used for accuracy evaluation. The experimental results show that the use of the DELSU model could extract the area of winter wheat at a precision higher than that of the traditional maximum likelihood method and the LSU model. This model absorbs the traditional classification advantages and improves the measurement accuracy of the target features. As an improved method of LSU，DELSU is also applicable to the measurement of other land use/cover types.