RapidAI4EO: Mono- and Multi-temporal Deep Learning models for Updating the CORINE Land Cover Product

TL;DR

This paper compares mono-temporal and multi-temporal satellite imagery for land cover classification, demonstrating that multi-temporal data slightly improves classification accuracy and aids in change detection and land monitoring.

Contribution

It introduces a CNN-LSTM framework for multi-temporal satellite image classification and shows its effectiveness over mono-temporal methods.

Findings

Multi-temporal approach improves classification accuracy by 0.89%.

Multi-temporal features enhance change detection capabilities.

The method supports more effective land monitoring.

Abstract

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.