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Autore: | Jiang Wanshou |
Titolo: | Techniques and Applications of UAV-Based Photogrammetric 3D Mapping |
Pubblicazione: | Basel, : MDPI Books, 2022 |
Descrizione fisica: | 1 electronic resource (294 p.) |
Soggetto topico: | Technology: general issues |
History of engineering & technology | |
Soggetto non controllato: | compound building reconstruction |
LiDAR | |
point clouds | |
semantic decomposition | |
structure from motion | |
match pair | |
cycle consistency inference | |
repetitive structure | |
very short baseline | |
high-resolution remote sensing images | |
building extraction | |
multiscale features | |
aggregate semantic information | |
feature pyramid | |
spatial eight-quadrant kernel convolution | |
3D point cloud | |
semantic segmentation | |
indoor scene | |
wide-baseline stereo image | |
deep learning | |
convolutional neural network | |
affine invariant feature | |
image matching | |
photogrammetric mesh model | |
building façade | |
3D reconstruction | |
least square fitting | |
single image super-resolution | |
lightweight image super-resolution | |
U-shaped residual network | |
dense shortcut | |
effective feature distillation | |
high-frequency loss | |
power lines | |
UAV inspection | |
red-black propagation | |
depth map fusion | |
PatchMatch | |
digital photogrammetry | |
camera self-calibration | |
Brown model | |
polynomial model | |
aerial triangulation | |
GF-7 image | |
building footprint | |
building height | |
multi-view | |
point cloud | |
multi-view reconstruction | |
detail preserving | |
depth estimation | |
surface meshing | |
texture mapping | |
coplanar extraction | |
deep convolutional neural network | |
geometric topology | |
homography matrix | |
airborne LiDAR | |
coal mine | |
surface subsidence | |
deformation detection | |
digital subsidence model | |
Persona (resp. second.): | JiangSan |
XiaoXiongwu | |
JiangWanshou | |
Sommario/riassunto: | The book focuses on the techniques for UAV-based 3D mapping and its applications in varying fields since the explosive development of UAV-based photogrammetric 3D mapping and their wide applications from traditional surveying and mapping to other related fields have been witnessed in photogrammetry and remote sensing. In the last decade, unmanned aerial vehicle (UAV) images have become one of the most important remote sensing data sources for photogrammetric 3D mapping. Besides, the rapid development of recent techniques, e.g., SfM (Structure from Motion) for off-line image orientation, SLAM (Simultaneous Localization and Mapping) for on-line UAV navigation, and the deep learning (DL) embedded 3D reconstruction pipeline, has promoted UAV-based 3D mapping towards the direction of automation and intelligence. It is really worthy to collecting the cutting-edge techniques and reporting their promising applications. |
Titolo autorizzato: | Techniques and Applications of UAV-Based Photogrammetric 3D Mapping |
Formato: | Materiale a stampa |
Livello bibliografico | Monografia |
Lingua di pubblicazione: | Inglese |
Record Nr.: | 9910595072603321 |
Lo trovi qui: | Univ. Federico II |
Opac: | Controlla la disponibilità qui |