Terrasar-X and Rapideye Data for the Parameterisation of Relational Characteristics of Urban Atkis DLM Objects

Hese, S.; Lindner, Martin; Voltersen, M.; Berger, Christian

doi:10.5194/isprsarchives-xxxviii-4-w19-111-2011

Cited by 1 publication

(1 citation statement)

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“…First, fostering information extraction from optical images and proposing advanced fusing methods are main topics of interest to the detriment of feature extraction from lidar and Radar data [2]. Consequently, only few papers try to generate a large number of attributes and to assess their relevance for land-cover discrimination [3]. Even when such a step is performed, it is often limited to standard topographic objects (vegetation, buildings, roads).…”

Section: Introductionmentioning

confidence: 99%

Fusion of LiDAR and radar data for land-cover mapping in natural environments

Barbanson

Mallet

Gressin

et al. 2015

2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS)

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Land-cover geodatabases are key products for the understanding of environmental systems and for setting up national and international prevention and protection policies. However, their automatic generation and update remain complicated with high accuracy over large scales. In natural environments, most of the existing solutions are semi-automatic in order to achieve a suitable discrimation of the large number of forest and crop classes. A large amount of remote sensing possibilities is at the moment available and data fusion appears to be the most suitable solution for that purpose. The paper tackles the issue of land-cover mapping in such areas assuming the existence of a partly non-updated 5-class geodatabase: buildings, roads, water, crops, forests. Lidar point clouds and Radar images at two spatial resolutions and bands are merged at the feature level and fed into an efficient supervised classification framework. Results show that some classes benefit from the joint exploitation of multiple observations in terms of accuracy or recall.

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Section: Introductionmentioning

confidence: 99%