棉花遥感识别的混合像元分解

为了进一步提高棉花遥感识别精度，以新疆玛纳斯县为研究区，运用线性光谱混合模型（LSMM），对TM遥感数据的混合像元分解技术与方法进行了研究。将棉花、玉米、番茄和土壤4类典型的端元组分光谱值代入线性模型，在非约束条件下，用最小二乘法估计混合系数，得到每种地物类型的丰度及RMS误差图，以实地测量的棉花种植面积对模型分解效果进行评估，结果表明：线性光谱混合模型构模简单、计算量小，棉花线性光谱混合像元分解精度达到90%以上，可用于新疆棉花的遥感识别。

Decomposition of mixed pixel for cotton identification using remote sensing data

In order to improve cotton identifying accuracy, taking Manas county in Xinjiang province as study area, the linear spectral mixture model (LSMM) was applied to the study of pixel unmixing technique based on TM remote sensing data. Four typical endmember spectrum values were put into the linear model, including spectrums of cotton, corn, tomato and soil. Under unconstrained condition, the mixed coefficient was derived by the least square method, together with the abundance of each surface feature and RMS error chart. The results of pixel unmixing were tested with ground measurement of the cotton field in the study area, which showed that the LSMM modeling was simple with less calculation, and the precision of the decomposition of mixed pixels exceeded 90% that's enough for cotton identification with remote sensing data in Xinjiang province.