Temporal optimisation of image acquisition for land cover classification with Random Forest and MODIS time-series

: The analysis and classification of land cover is one of the principal applications in terrestrial remote sensing. Due to the seasonal variability of different vegetation types, the ability to discriminate different land cover types changes over time. Multi-temporal classification can help to improve the classification accuracies, but different constraints, such as financial restrictions or atmospheric conditions, may limit their application. The optimisation of image acquisition timing and frequencies can help to increase the effectiveness of the classification process. For this purpose, the Feature Importance measure of the state-of-the art machine learning method Random Forest was used to determine the optimal image acquisition periods for a general (Grassland, Forest, Water, Settlement, Peatland) and grassland specific (Improved Grassland, Semi-Improved Grassland) land cover classification in central Ireland on a 9-year time-series of MODIS Terra 16 day composite data (MOD13Q1). F...

• Type: Journal
• Year: 2015
• Where: AEOG
• Authors: Ingmar Nitze, Brian Barrett, Fiona Cawkwell
• Keywords: AEOG 2015, Artificial Intelligence