Deep Recurrent Neural Network for Crop Classification Task Based on Sentinel-1 and Sentinel-2 Imagery
Nataliia Kussul, Mykola Lavreniuk, Leonid Shumilo
Abstract
In the past few years, deep learning methods developed and progressed in the many applied fields of science due to the appearance of large amounts of freely available data and improvement of computing resources. The task of crop mapping based on satellite data is not an exclusion. However, the appearance of clouds and shadows on the optical images cause difficulties on applying typical deep learning methods that recommend themselves in other issues. The easiest solution was to consider only images with a small percentage of clouds, but such way decreased the available data informativeness. In this study, we propose the new deep learning method based on recurrent neural network for efficient and precise crop mapping based on Sentinel-1 and Sentinel-2 imagery. The main idea of the study is to utilize all available information from the satellites and to provide an opportunity for neural network to extract the necessary features without any expert knowledge. Taking into account that regular feedforward neural networks could not deal effectively with such issue, authors suggest recurrent neural network with long short-term memory cells. Experimental results for the part of Kyiv region showed that this approach is rather efficient and outperformed traditional machine learning approaches and deep U-net architecture in terms of overall accuracy.