D3 Artikkeli ammatillisessa konferenssijulkaisussa
Heuristicallly targeted Minimum Description Length test for stone detection from public point cloud data
Tekijät: Paavo Nevalainen , Juuso Suomi , Jukka Heikkonen
Toimittaja: Rissanen Jorma, Leppä-Aho Janne, Roos Teemu, Myllymäki Petri
Konferenssin vakiintunut nimi: Workshop on Information Theoretic Methods in Science and Engineering
Kustannuspaikka: Helsinki
Julkaisuvuosi: 2016
Kokoomateoksen nimi: Proceedings of the Ninth Workshop on Information Theoretic Methods in Science and Engineering
Sarjan nimi: Publication series B, Report B
Numero sarjassa: 9
Vuosikerta: 1
Aloitussivu: 26
Lopetussivu: 29
Verkko-osoite: https://helda.helsinki.fi/bitstream/handle/10138/169918/witmse2016proc.pdf?sequence=3
Coarse cross-terrain point clouds are gathered by aerial
laser scan (ALS) and dense point clouds by unmanned
vehicle (UAV) operation. These two data sources have
complementary nature and should be combined for various
applications. This paper uses minimum description
(MDL) length approach to detect individual stones and
their physical dimensions from UAV data. The MDL procedure
is spatially targeted by a two-step heuristics: local
stoniness likelihood derived from ALS data and the curvature
detection on UAV data. Comparison of the performance
of MDL principle and a geometric approach,
namely mean square error (MSE) minimization is presented.
The MDL approach can be applied to cloud point
densities ρ ≥ 3 m−2.
Ladattava julkaisu This is an electronic reprint of the original article. |  |
