TM影像中基于光谱特征的棉花识别模型

为快速、准确地在遥感图像上从各种农作物中识别提取棉花作物的信息，满足大尺度、运行化棉花遥感监测系统的要求。作者在试验区的棉花主要生长期里，同期进行了棉花与其它主要农作物的地面光谱测量并采集了同期的Landsat TM图像。通过对各时期棉花及主要农作物的地面测量光谱与TM图像光谱特征的差异性及规律性分析，确定了试验区棉花遥感识别的最佳时相期为9月中下旬， 研究开发了基于光谱特征的棉花识别模型。经数学分析与实际应用验证，该模型简单、操作方便并且识别的准确度较高，适用于大尺度的“新疆棉花遥感监测运行系统”。

Spectral information based model for cotton identification on Landsat TM Image

For meeting the demand for large scale and operational cotton monitoring system using the remote sensing technology, the identification of cotton information from all kinds of crops on the remote sensing image must be rapidly, precisely and reliabley conducted. In cotton growth season, the spectral information of cotton and main crops was collected in field measurements using a spectroradiometer and also from Landsat TM images in the experimental area. Through analyzing the spectral features, the best time for cotton identification on TM images was confirmed and a spectral information based cotton identification model on TM images was developed. The model was validated by mathematical analysis and practical application. The wodel is simple, efficient, and more accurate, and is suitable to be used in the Cotton Operational Monitoring System using Remote Sensing in Xinjiang.