A New Classification Method for Semi-Arid Regions Based on SAR and LiDAR Data Fusion
- Resource Type
- Conference
- Authors
- Iervolino, Pasquale; Coppola, Alessandro; Guida, Raffaella; Riccio, Daniele
- Source
- IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium Geoscience and Remote Sensing Symposium, IGARSS 2019 - 2019 IEEE International. :708-711 Jul, 2019
- Subject
- Aerospace
Geoscience
Signal Processing and Analysis
Laser radar
Synthetic aperture radar
Vegetation mapping
Feature extraction
Buildings
Soil
Data integration
SAR
LiDAR
land classification
SAR texture
Gray Level Co-occurrence Matrix
- Language
- ISSN
- 2153-7003
This paper aims at developing a new enhanced algorithm for mapping semi-arid areas based on fusion techniques of Synthetic Aperture Radar (SAR) and Light Detection And Ranging (LIDAR) datasets. Firstly, both datasets are preprocessed to remove geometric and radiometric errors; then features of interest are extracted from SAR and LiDAR products to build masks and identify meaningful classes. Finally, classification results are refined with morphological filters. The new algorithm has been tested on data acquired by TerraSAR-X and an airborne LiDAR sensor over the Natural Reserve of Maspalomas in Canary Islands. Results show an overall classification accuracy of 85% with an absolute increment of more than 14% compared to a classification in which only LiDAR data are used.