Study on Temporal and Spatial Adaptability of Crop Classification Models

Abstract

Crop classification is an important part of national agricultural management, and accurate crop classification is conducive to crop growth monitoring and yield assessment. However, due to the different growing years and regions, even the same crop has different growth processes and different phenological characteristics. Therefore, improving the spatial and temporal adaptability of the classification model is an important research content for large-scale crop classification. In this paper, several adjacent agricultural production areas are studied. Based on the stable time-series remote sensing image dataset, the adaptive changes of several machine learning classification methods with higher classification accuracy in spatial and temporal are studied. The paper selected Sentinel 1 satellite data with good anti-cloud interference and a short return visit cycle for experiments. Firstly, the training of each classification model in the same area is completed, and then the spatial adaptability of the model is studied in different adjacent ranges. Finally, the adaptability of different classification models to the change of the growth cycle of the same type of crop is also compared. The paper finds that the models such as CNN+LSTM and BinConvLSTM perform better in temporal and spatial.