Deep Learning Crop Classification Approach Based on Coding Input Satellite Data Into the Unified Hyperspace

To provide reliable crop maps for the same territory each year, it is necessary to collect in-situ data for each year independently. Collecting ground truth data is a very time consuming and challenging task. At present, unfortunately, there is no an adopted approach, how to utilize in-situ and satellite data from previous years for crop mapping in the subsequent years. In this paper, we propose a new deep learning approach using sparse autoencoder based on only satellite data, and a further procedure of neural network fine-tuning based on in-situ data. The possibility of utilizing this deep learning architecture based on translating all available satellite data into the unified hyperspace. The study is carried out for the central part of Ukraine. Obtained results show that this technique is feasible and provides reliable crop classification maps with overall accuracy (OA) of 91.0% and 85.9% for two different experiments. The use of the proposed approach makes it possible to avoid, or decrease, the necessity for collecting in-situ data for each year and for each part of large territory.