摘要
利用中低分辨率遥感影像提取作物分类种植面积的精度,往往难以满足农业遥感估产的需要。随着新型传感器的不断出现,应用高分辨率遥感影像高精度地提取作物分类面积日益成为发展趋势。由于高分辨率遥感影像提供的地物纹理、色调与形状等信息更加丰富,当前基于对象的地物识别分类方法仍不成熟,处理操作中人为干预过多,而且较为复杂,因此尝试以地面调查信息为辅助参量,采用常规基于像元的最大似然法监督分类方法,依据多尺度遥感影像信息提取的原理,分阶段地逐步提取作物种植面积,以此为农业遥感估产服务。
With complicated natural conditions, multiplicity of crop structure, small and dispersive distribution of parcel, the accuracy of images with moderate and lower resolution can＇t meet the acquisition of crop yield forecasting. With improvement of new sensors of high resolution, remote sensing imagery of high resolution can provide more abundant information such as texture, hue and so on. However, the current object-oriented classification approaches are not mature, which have too much thresholds to be set and more complicated and difficult to be used commonly. Therefore, combining QuickBird high spatial resolution satellite imagery with the field investigation data as mainly auxiliary information as well as using the pixel-oriented maximum likelihood method, crop planting area was obtained step by step, applying the principle of multi-scale information extraction, a test was set in Mianyang, Sichuan province. The result shows that the accuracy of crop classification is fairly exciting.
出处
《遥感技术与应用》
CSCD
2008年第1期17-23,I0005,共8页
Remote Sensing Technology and Application
基金
国家863项目:粮食预警遥感辅助决策系统(2003AA131050)
中国科学院知识创新工程重要方向项目:遥感估产运行系统中遥感监测过程检验与精度评估(KZCX3-SW-338-2)
