A hierarchical category structure based convolutional recurrent neural network (HCS-ConvRNN) for Land-Cover classification using dense MODIS Time-Series data

Abstract

Hierarchical classification of land cover can be used to describe the Earth’s surface with different scales and properties. However, existing studies have rarely considered hierarchical information for land-cover classification, and have ignored dependencies in the hierarchical structure. In this study, we propose a hierarchical category structure-based convolutional recurrent neural network (HCS-ConvRNN). The HCS-ConvRNN method constrains the input through the leaf node of the hierarchical structure based input layer, and then constructs the dependencies among different layers in a top-down manner, in order to classify the pixels into the most relevant classes in a layer-by-layer manner. A total of 219 Moderate Resolution Imaging Spectroradiometer (MODIS) images of China from 2015 to 2017, at a 5-day interval, were used in the reported experiments. It is shown that: 1) the results of HCS-ConvRNN have rich spatial details; 2) the accuracy at each level of HCS-ConvRNN is better than that of MOD12Q1; and 3) generally HCS-ConvRNN can obtain a better classification performance than other networks such as the convolutional neural network (CNN) and gated recurrent unit (GRU). In summary, the proposed HCS-ConvRNN method can effectively achieve hierarchical land cover classification, and has the potential for accurate land cover classification at a large scale.