Remote sensing imagery classification contributes assistance to real-time applications for comfort and secures the society. The imagery of satellites entirely depends on the sensor type in satellites. Phenology reflection varies based on the land cover type, which absorbs external energy. Multispectral high-resolution imagery has the maximum details about the earth’s surface. This research work defines phenology based classification approach, which can produce precise high precision land cover classification. The need to develop a phenology based methodology reflects on the vegetation development classification and produces a much more suitable land cover map based on reflection values. The RGB channel values of the image do not influence this technique of reflection phenology classification. Phenology Based Classification Index (PBCI) supervised method is used to classify the high-resolution multispectral imagery with improved phenology classification methods. PBCI works on the passive sensor satellite images, without clouds and shadow in classification. The proposed method has compared with existing phenology classification methods using more than seven quality metrics.

遥感影像分类有助于实时应用以提供舒适感并确保社会安全。卫星的图像完全取决于卫星中的传感器类型。物候反射因吸收外部能量的土地覆盖类型而异。多光谱高分辨率图像具有有关地球表面的最大细节。这项研究工作定义了基于物候的分类方法，可以产生精确的高精度土地覆被分类。开发基于物候学的方法的需求反映了植被的发展分类，并基于反射值生成了更合适的土地覆盖图。图像的RGB通道值不影响这种反射物候分类技术。基于物候分类索引（PBCI）的监督方法用于通过改进的物候分类方法对高分辨率多光谱图像进行分类。PBCI处理无源传感器卫星图像，分类中没有云和阴影。所提出的方法与使用七个以上质量指标的现有物候分类方法进行了比较。