Abstract:
It has been now widely assessed in the literature that both multi/hyperspectral optical images and 3D lidar point clouds are necessary inputs for tree species based fores...Show MoreMetadata
Abstract:
It has been now widely assessed in the literature that both multi/hyperspectral optical images and 3D lidar point clouds are necessary inputs for tree species based forest stand detection. Nevertheless, no comprehensive analysis of the genuine relevance of each data source has been performed so far: existing strategies are limited to a single spatial and spectral resolution. This paper investigates which is the optimal combination of geospatial optical images and lidar point clouds. A supervised semantic segmentation framework is fed with various sources (multispectral satellite and airborne images, hyperspectral airborne images, low, medium and high density lidar point clouds), ablation cases are defined, and the discrimination performance of several fusion schemes is assessed under a challenging mountainous area in France.
Date of Conference: 22-27 July 2018
Date Added to IEEE Xplore: 04 November 2018
ISBN Information: