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Remote Sensing based Building Extraction

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ISBN: 9783039283828 9783039283835 Year: Pages: 442 DOI: 10.3390/books978-3-03928-383-5 Language: English
Publisher: MDPI - Multidisciplinary Digital Publishing Institute
Subject: Technology (General) --- General and Civil Engineering --- Construction
Added to DOAB on : 2020-04-07 23:07:09
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Abstract

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

Keywords

roof segmentation --- outline extraction --- convolutional neural network --- boundary regulated network --- very high resolution imagery --- building boundary extraction --- convolutional neural network --- active contour model --- high resolution optical images --- LiDAR --- richer convolution features --- building edges detection --- high spatial resolution remote sensing imagery --- building --- modelling --- reconstruction --- change detection --- LiDAR --- point cloud --- 3-D --- building extraction --- deep learning --- attention mechanism --- very high resolution --- imagery --- building detection --- aerial images --- feature-level-fusion --- straight-line segment matching --- occlusion --- building regularization technique --- point clouds --- boundary extraction --- regularization --- building reconstruction --- digital building height --- 3D urban expansion --- land-use --- DTM extraction --- open data --- developing city --- accuracy analysis --- building detection --- building index --- feature extraction --- mathematical morphology --- morphological attribute filter --- morphological profile --- building extraction --- deep learning --- semantic segmentation --- data fusion --- high-resolution satellite images --- GIS data --- high-resolution aerial images --- deep learning --- generative adversarial network --- semantic segmentation --- Inria aerial image labeling dataset --- Massachusetts buildings dataset --- building extraction --- simple linear iterative clustering (SLIC) --- multiscale Siamese convolutional networks (MSCNs) --- binary decision network --- unmanned aerial vehicle (UAV) --- image fusion --- high spatial resolution remotely sensed imagery --- object recognition --- deep learning --- method comparison --- LiDAR point cloud --- building extraction --- elevation map --- Gabor filter --- feature fusion --- semantic segmentation --- urban building extraction --- deep convolutional neural network --- VHR remote sensing imagery --- U-Net --- remote sensing --- deep learning --- building extraction --- web-net --- ultra-hierarchical sampling --- 3D reconstruction --- indoor modelling --- mobile laser scanning --- point clouds --- 5G signal simulation --- building extraction --- high-resolution aerial imagery --- fully convolutional network --- semantic segmentation --- n/a

Very High Resolution (VHR) Satellite Imagery: Processing and Applications

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ISBN: 9783039217564 9783039217571 Year: Pages: 262 DOI: 10.3390/books978-3-03921-757-1 Language: English
Publisher: MDPI - Multidisciplinary Digital Publishing Institute
Subject: Technology (General) --- General and Civil Engineering --- Environmental Engineering
Added to DOAB on : 2019-12-09 11:49:16
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Recently, growing interest in the use of remote sensing imagery has appeared to provide synoptic maps of water quality parameters in coastal and inner water ecosystems;, monitoring of complex land ecosystems for biodiversity conservation; precision agriculture for the management of soils, crops, and pests; urban planning; disaster monitoring, etc. However, for these maps to achieve their full potential, it is important to engage in periodic monitoring and analysis of multi-temporal changes. In this context, very high resolution (VHR) satellite-based optical, infrared, and radar imaging instruments provide reliable information to implement spatially-based conservation actions. Moreover, they enable observations of parameters of our environment at greater broader spatial and finer temporal scales than those allowed through field observation alone. In this sense, recent very high resolution satellite technologies and image processing algorithms present the opportunity to develop quantitative techniques that have the potential to improve upon traditional techniques in terms of cost, mapping fidelity, and objectivity. Typical applications include multi-temporal classification, recognition and tracking of specific patterns, multisensor data fusion, analysis of land/marine ecosystem processes and environment monitoring, etc. This book aims to collect new developments, methodologies, and applications of very high resolution satellite data for remote sensing. The works selected provide to the research community the most recent advances on all aspects of VHR satellite remote sensing.

Keywords

road extraction --- very high-resolution image --- fast marching method --- semiautomatic --- edge constraint --- beaver mimicry --- beaver dam analogue --- QuickBird --- riparian --- stream restoration --- Worldview --- benthic mapping --- seagrass --- airborne hypespectral imagery --- Worldview-2 --- atmospheric correction --- sunglint correction --- water column correction --- dimensionality reduction techniques --- SVM classification --- linear unmixing --- building detection --- built-up areas extraction --- local feature points --- saliency index --- morphological building index --- Deformable CNN --- Faster R-CNN --- data augmentation --- occluded object detection --- very high-resolution Pléiades imagery --- canopy height model --- acquisition geometry --- forested mountain --- accuracy assessment --- remote sensing imagery --- super-resolution --- ultra-dense connection --- feature distillation --- video satellite --- compensation unit --- urban water mapping --- water index --- shadow detection --- threshold stability --- agriculture parcel segmentation --- superpixels --- consensus --- texture analysis --- multi-resolution segmentation (MRS) --- greenhouse extraction --- over-segmentation index (OSI) --- under-segmentation index (USI) --- error index of total area (ETA) --- composite error index (CEI) --- GaoFen-2 (GF-2) --- synthetic aperture radar --- landslide monitoring --- sub-pixel offset tracking --- Slumgullion landslide --- natural hazards --- large displacements --- remote sensing --- scene classification --- CNN --- capsule --- PrimaryCaps --- CapsNet --- High-resolution satellite imagery --- submesoscale --- spiral eddy --- cyanobacteria --- surface convergence --- western Baltic Sea

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