Remote Sensing based Building Extraction
Building extraction from remote sensing data plays an important role in urban planning, disaster management, navigation, updating geographic databases, and several other geospatial applications. Even though significant research has been carried out for more than two decades, the success of automatic...
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MDPI - Multidisciplinary Digital Publishing Institute
2020
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| Online Access: | Get Fullteks DOAB: description of the publication |
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| LEADER | 04898naaaa2201225uu 4500 | ||
|---|---|---|---|
| 001 | doab_20_500_12854_58168 | ||
| 005 | 20210212 | ||
| 020 | |a books978-3-03928-383-5 | ||
| 020 | |a 9783039283835 | ||
| 020 | |a 9783039283828 | ||
| 024 | 7 | |a 10.3390/books978-3-03928-383-5 |c doi | |
| 041 | 0 | |a English | |
| 042 | |a dc | ||
| 100 | 1 | |a Yang, Bisheng |4 auth | |
| 700 | 1 | |a Awrangjeb, Mohammad |4 auth | |
| 700 | 1 | |a Hu, Xiangyun |4 auth | |
| 700 | 1 | |a Tian, Jiaojiao |4 auth | |
| 245 | 1 | 0 | |a Remote Sensing based Building Extraction |
| 260 | |b MDPI - Multidisciplinary Digital Publishing Institute |c 2020 | ||
| 300 | |a 1 electronic resource (442 p.) | ||
| 506 | 0 | |a Open Access |2 star |f Unrestricted online access | |
| 520 | |a Building extraction from remote sensing data plays an important role in urban planning, disaster management, navigation, updating geographic databases, and several other geospatial applications. Even though significant research has been carried out for more than two decades, the success of automatic building extraction and modeling is still largely impeded by scene complexity, incomplete cue extraction, and sensor dependency of data. Most recently, deep neural networks (DNN) have been widely applied for high classification accuracy in various areas including land-cover and land-use classification. Therefore, intelligent and innovative algorithms are needed for the success of automatic building extraction and modeling. This Special Issue focuses on newly developed methods for classification and feature extraction from remote sensing data for automatic building extraction and 3D | ||
| 540 | |a Creative Commons |f https://creativecommons.org/licenses/by-nc-nd/4.0/ |2 cc |4 https://creativecommons.org/licenses/by-nc-nd/4.0/ | ||
| 546 | |a English | ||
| 653 | |a object recognition | ||
| 653 | |a n/a | ||
| 653 | |a very high resolution | ||
| 653 | |a image fusion | ||
| 653 | |a regularization | ||
| 653 | |a simple linear iterative clustering (SLIC) | ||
| 653 | |a digital building height | ||
| 653 | |a building | ||
| 653 | |a DTM extraction | ||
| 653 | |a 3D reconstruction | ||
| 653 | |a imagery | ||
| 653 | |a GIS data | ||
| 653 | |a high-resolution satellite images | ||
| 653 | |a building edges detection | ||
| 653 | |a high resolution optical images | ||
| 653 | |a point clouds | ||
| 653 | |a building extraction | ||
| 653 | |a land-use | ||
| 653 | |a morphological attribute filter | ||
| 653 | |a deep convolutional neural network | ||
| 653 | |a boundary extraction | ||
| 653 | |a high spatial resolution remotely sensed imagery | ||
| 653 | |a remote sensing | ||
| 653 | |a fully convolutional network | ||
| 653 | |a 3-D | ||
| 653 | |a semantic segmentation | ||
| 653 | |a morphological profile | ||
| 653 | |a modelling | ||
| 653 | |a roof segmentation | ||
| 653 | |a boundary regulated network | ||
| 653 | |a 3D urban expansion | ||
| 653 | |a feature fusion | ||
| 653 | |a developing city | ||
| 653 | |a very high resolution imagery | ||
| 653 | |a building detection | ||
| 653 | |a occlusion | ||
| 653 | |a change detection | ||
| 653 | |a building index | ||
| 653 | |a Massachusetts buildings dataset | ||
| 653 | |a elevation map | ||
| 653 | |a high spatial resolution remote sensing imagery | ||
| 653 | |a data fusion | ||
| 653 | |a generative adversarial network | ||
| 653 | |a unmanned aerial vehicle (UAV) | ||
| 653 | |a high-resolution aerial images | ||
| 653 | |a ultra-hierarchical sampling | ||
| 653 | |a U-Net | ||
| 653 | |a binary decision network | ||
| 653 | |a straight-line segment matching | ||
| 653 | |a outline extraction | ||
| 653 | |a building boundary extraction | ||
| 653 | |a deep learning | ||
| 653 | |a aerial images | ||
| 653 | |a mobile laser scanning | ||
| 653 | |a feature extraction | ||
| 653 | |a multiscale Siamese convolutional networks (MSCNs) | ||
| 653 | |a urban building extraction | ||
| 653 | |a high-resolution aerial imagery | ||
| 653 | |a mathematical morphology | ||
| 653 | |a indoor modelling | ||
| 653 | |a Gabor filter | ||
| 653 | |a active contour model | ||
| 653 | |a attention mechanism | ||
| 653 | |a convolutional neural network | ||
| 653 | |a LiDAR | ||
| 653 | |a accuracy analysis | ||
| 653 | |a point cloud | ||
| 653 | |a feature-level-fusion | ||
| 653 | |a building reconstruction | ||
| 653 | |a richer convolution features | ||
| 653 | |a open data | ||
| 653 | |a VHR remote sensing imagery | ||
| 653 | |a Inria aerial image labeling dataset | ||
| 653 | |a LiDAR point cloud | ||
| 653 | |a method comparison | ||
| 653 | |a 5G signal simulation | ||
| 653 | |a reconstruction | ||
| 653 | |a building regularization technique | ||
| 653 | |a web-net | ||
| 856 | 4 | 0 | |a www.oapen.org |u https://mdpi.com/books/pdfview/book/2139 |7 0 |z Get Fullteks |
| 856 | 4 | 0 | |a www.oapen.org |u https://directory.doabooks.org/handle/20.500.12854/58168 |7 0 |z DOAB: description of the publication |