A Hierarchical Deep Learning Crop Remote Sensing Classification Method

The invention discloses a hierarchical deep learning (HDL) crop remote sensing classification method. The LLC classification probability of high-level crops can be used as the prior probability of HLC classification of high-level crops. Conversely, the classification probability of HLC can also be used as the prior probability of LLC classification. , the repeated iterations of this process can be expressed by the Markov process model. The HDL model consists of two sub-models (HDL-LLC and HDL-HLC), which are used for the classification of low-level crop LLC and high-level crop HLC respectively, and calculate the classification probability of LLC and HLC classification probability calculations. The invention can realize the remote sensing classification of LLC and HLC at the same time, and incorporates the correlation between LLC and HLC into the classification process, thereby improving the accuracy of remote sensing classification of LLC and HLC, and solving the problem that the current LLC and HLC classifications are separated from each other and cannot be unified.