RAPIDAI4EO: MONO- AND MULTI-TEMPORAL DEEP LEARNING MODELS FOR UPDATING THE CORINE LAND COVER PRODUCT

In the remote sensing community, Land Use Land Cover (LULC) classification with satellite imagery is a main focus of current research activities. Accurate and appropriate LULC classification, however, continues to be a challenging task. In this paper, we evaluate the performance of multi-temporal (monthly time series) compared to mono-temporal (single time step) satellite images for multi-label classification using supervised learning on the RapidAI4EO dataset. As a first step, we trained our CNN model on images at a single time step for multi-label classification, i.e. mono-temporal. We incorporated time-series images using a LSTM model to assess whether or not multi-temporal signals from satellites improves CLC classification. The results demonstrate an improvement of approximately 0.89% in classifying satellite imagery on 15 classes using a multi-temporal approach on monthly time series images compared to the mono-temporal approach. Using features from multi-temporal or mono-temporal images, this work is a step towards an efficient change detection and land monitoring approach.

Automated summary

This paper evaluates the performance of satellite imagery in Land Use Land Cover (LULC) classification and finds that using multi-temporal images improves the accuracy of multi-label classification compared to using single-time-step images. This research is important for achieving efficient change detection and land monitoring methods.