COMPARING DEEP RECURRENT LEARNING AND CONVOLUTIONAL LEARNING FOR MULTI-TEMPORAL VEGETATION CLASSIFICATION

Mapping vegetation quality is a vital and challenging task in remote sensing field. Due to changes of reflective features periods over time, many researchers are experiencing difficulties in retrieving automatically the type of the vegetation that meet their research needs. Recently, deep learning techniques has become the fastest-growing trend in remote sensing data classification including vegetation mapping data. To overcome the challenge of learning deep models and for easily multi-temporal vegetation mapping and monitoring, in this paper, we propose a Bidirectional Gated Recurrent Unit Network (BGRU) model, which is based on history information and able to deal with long-term sequential data using only few parameters to extract useful features using forward and backward gates for their automatically classification. Multi-temporal publicly available Sentinel-2A datasets with vegetation as the main theme are used to validate the proposed model and the obtained experimental results are evaluated with established criteria.