NDLI: A novel approach to generating DSM from high-resolution UAV images

Content Provider	IEEE Xplore Digital Library
Author	Jiayuan Li Mingyao Ai Qingwu Hu Dongwei Fu
Copyright Year	2014
Description	Author affiliation: Sch. of Remote Sensing & Inf. Eng., Wuhan Univ., Wuhan, China (Jiayuan Li; Mingyao Ai; Qingwu Hu; Dongwei Fu)
Abstract	In the past few years, unmanned aerial vehicles (UAVs) demonstrated their great potential for photogrammetric measurements in a lot of application fields because its less expensive, safer and higher resolution images. Nevertheless, their images are often affected by large rotation, big view-point change and small overlaps. In this paper, we present a novel approach for reliable Digital Surface Models (DSM) generation, which is designed to operate on high-resolution, wide-baseline UAV image sets and compute dense 3D point clouds efficiently. It is implemented as a procedure including the four steps of match, expand, filter and reconstruction, starting from a sparse set of matched difference-of-Gaussian (DoG) keypoints, forming a triangulation on it, then expanding per-pixel under local parallax continuity, using visibility constraints to filter false matches, finally generating the DSM. Experiments are conducted to demonstrate the effectiveness and accuracy of our approach and to show that state-of-the-art performance can be achieved with significant acceleration.
Starting Page	1
Ending Page	5
File Size	1236250
Page Count	5
File Format	PDF
ISSN	2161024X
e-ISBN	9781479957149
DOI	10.1109/GEOINFORMATICS.2014.6950836
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-06-25
Publisher Place	Taiwan
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Poisson surface reconstruction Image resolution Image recognition Local parallax continuity Digital surface models (DSM) Per-pixel expand Unmanned aerial vehicles (UAVs) Sparse match Image reconstruction Pattern matching
Content Type	Text
Resource Type	Article
Subject	Geography, Planning and Development Computer Networks and Communications Information Systems Electrical and Electronic Engineering Computer Vision and Pattern Recognition Software

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