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GROUND OBJECT RECOGNITION USING COMBINED HIGH RESOLUTION AIRBORNE IMAGES AND DSM
2012
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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A novel method called Sparse Representation is introduced to extract ground objects from airborne images. ...
A refinement procedure based on elevation histogram is carried on to improve the coarse classification due to misclassification of trees/vegetation and buildings/road. ...
The Vaihingen data set was provided by the German Society for Photogrammetry, Remote Sensing and Geoinformation (DGPF) [Cramer, 2010] : http://www.ifp.uni-stuttgart.de/dgpf/DKEP-Allg.html ...
doi:10.5194/isprsarchives-xxxix-b3-573-2012
fatcat:zp2sulvstvdaldoguq5bs3vpzy
THE POTENTIAL OF BUILDING DETECTION FROM SAR AND LIDAR USING DEEP LEARNING
2019
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Thus, this paper reviews current techniques and future trends of multi-sources Remote Sensing for building extraction. ...
mapping approach offers practicality and significant cost savings for the nation minimizing the need for ground control points on the ground in addition to providing high-resolution, day-and-night, cloud ...
., & Rastiveis, (2017) conducted the research on an automatic urban objects extraction using airborne Remote Sensing data to process and efficiently interpret the vast amount of airborne imagery and LIDAR ...
doi:10.5194/isprs-archives-xlii-4-w16-489-2019
fatcat:xhlrmiru5reo5fbqkhafxucl6a
Pattern Recognition in Remote Sensing
2010
Pattern Recognition Letters
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The paper by Valero et al. presents a new method for extracting roads in very high resolution remotely sensed images based on advanced directional morphological operators called path openings and path ...
The paper by Bovolo et al. presents a novel method for change detection based on change vector analysis and support vector data description technique. ...
doi:10.1016/j.patrec.2010.04.014
fatcat:jwx6imthxjfg7aakkeu4fpjtlu
CNN-BASED FEATURE-LEVEL FUSION OF VERY HIGH RESOLUTION AERIAL IMAGERY AND LIDAR DATA
2019
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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In this paper, a CNN-based feature-level framework is proposed to integrate LiDAR data and aerial imagery for object classification in urban area. ...
In our method, after generating low-level descriptors and fusing them in a feature-level fusion by layer-stacking, the proposed framework employs a novel CNN to extract the spectral-spatial features for ...
ACKNOWLEDGEMENTS The authors would like to thank the National Center for Airborne Laser Mapping and the Hyperspectral Image Analysis Laboratory at the University of Houston for acquiring and providing ...
doi:10.5194/isprs-archives-xlii-4-w18-279-2019
fatcat:w3hzfgmrdnc63m7kvs2zcpm5fq
2020 Index IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Vol. 13
2020
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
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., +, JSTARS 2020 941-949
A Self-Supervised Learning Framework for Road Centerline Extraction
From High-Resolution Remote Sensing Images. ...
., +, JSTARS 2020
3958-3974
A Self-Supervised Learning Framework for Road Centerline Extraction
From High-Resolution Remote Sensing Images. ...
A New Deep-Learning-Based Approach for Earthquake-Triggered Landslide Detection From Single-Temporal RapidEye Satellite Imagery. Yi, Y., +, JSTARS 2020 ...
doi:10.1109/jstars.2021.3050695
fatcat:ycd5qt66xrgqfewcr6ygsqcl2y
The Combined Use of Airborne Remote Sensing Techniques within a GIS Environment for the Seismic Vulnerability Assessment of Urban Areas: An Operational Application
2016
Remote Sensing
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of urban areas, especially considering the actual lack of widely and freely available high-resolution space-borne remote sensing imagery. ...
On these bases, the joint use of airborne remotely-sensed measurements and GIS tools could be very useful for multi-risk assessment purposes. ...
The authors wish to thank all reviewers for their comments on the paper, as these have allowed the improvement of the work.
Conflicts of Interest: The authors declare no conflict of interest. ...
doi:10.3390/rs8020146
fatcat:qt33iyr4wfaabdmeqypctxdnly
Fusion of Optical and Thermal Imagery and LiDAR Data for Application to 3-D Urban Environment and Structure Monitoring
[chapter]
2012
Remote Sensing - Advanced Techniques and Platforms
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The 3-D urban application is based on an integrated data set: spectral models, ground camera and airborne images, and LiDAR data. ...
Then the road model is established, based on the continuous network of points which are used to extract information such as centerline, edge and width of the road (Akel et al. 2003; Hinz & Baumgartner ...
Fusion of Optical and Thermal Imagery and LiDAR Data for Application to 3-D Urban Environment and Structure Monitoring, Remote Sensing -Advanced Techniques and Platforms, Dr. ...
doi:10.5772/36066
fatcat:3outhr4aqfehfnna3afdlni6mi
2019 Index IEEE Transactions on Geoscience and Remote Sensing Vol. 57
2019
IEEE Transactions on Geoscience and Remote Sensing
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., and Drake, V.A., Insect Biological Parameter Estimation Based on the Invariant Target Parameters of the Scattering Matrix; TGRS Aug. 2019 6212-6225 Hu, C., see Zhang, M., TGRS Sept. 2019 6666-6674 ...
and Hanssen, R.F., Incorporating Temporary Coherent Li, X., Yeo, T.S., Yang, Y., Chi, C., Zuo, F., Hu, X., and Pi, Y., Refo-cusing and Zoom-In Polar Format Algorithm for Curvilinear Spotlight SAR Imaging ...
., +, TGRS Jan. 2019 406-415 A Novel Two-Step Registration Method for Remote Sensing Images Based on Deep and Local Features. ...
doi:10.1109/tgrs.2020.2967201
fatcat:kpfxoidv5bgcfo36zfsnxe4aj4
AutoGeoLabel: Automated Label Generation for Geospatial Machine Learning
[article]
2022
arXiv
pre-print
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2021
2021 IEEE International Conference on Big Data (Big Data)
unpublished
doi:10.1109/bigdata52589.2021.9672060 fatcat:nydfl3g3uveudjf6qwscbwri6q
doi:10.1109/bigdata52589.2021.9672060 fatcat:nydfl3g3uveudjf6qwscbwri6q
We evaluate a big data processing pipeline to auto-generate labels for remote sensing data. It is based on rasterized statistical features extracted from surveys such as e.g. LiDAR measurements. ...
The general method proposed here is platform independent, and it can be adapted to generate labels for other satellite modalities in order to enable machine learning on overhead imagery for land use classification ...
Based on an airborne LiDAR survey for New York City, we demonstrated and explored a novel approach to use simple statistical features of remote sensing data in order to create data classes. ...
arXiv:2202.00067v1
fatcat:ytf3422ggbfctbeqaxgz3gby3e
2014 Index IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Vol. 7
2014
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
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., and Foerster, S ...
., +, JSTARS July 2014 2942-2956 Image resolution A New Region Growing-Based Method for Road Network Extraction and Its Application on Different Resolution SAR Images. ...
., +, JSTARS Oct. 2014 4199-4217 A New Region Growing-Based Method for Road Network Extraction and Its Application on Different Resolution SAR Images. ...
doi:10.1109/jstars.2015.2397347
fatcat:ib3tjwsjsnd6ri6kkklq5ov37a
Three-Dimensional Urban Land Cover Classification by Prior-Level Fusion of LiDAR Point Cloud and Optical Imagery
2021
Remote Sensing
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However, few studies have focused on the fusion of LiDAR point clouds and optical imagery for three-dimensional land cover classification, especially using a deep learning framework. ...
Our proposed prior-fusion strategy has higher overall accuracy (82.47%) on data from the International Society for Photogrammetry and Remote Sensing, compared with the baseline (74.62%), point-level (79.86% ...
Acknowledgments: The Vaihingen data set was accessed on 9 May 2018 from the German Society for Photogrammetry, Remote Sensing and Geoinformation (DGPF) [44] : http://www.ifp.uni-stuttgart. de/dgpf/DKEPAllg.html ...
doi:10.3390/rs13234928
fatcat:aiz6kel3jvbdjknj2atz56xjjq
Multisource and Multitemporal Data Fusion in Remote Sensing
[article]
2018
arXiv
pre-print
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for researchers at different levels (i.e., students, researchers, and senior researchers) willing to conduct novel investigations on this challenging topic by supplying sufficient detail and references ...
processing of remotely sensed data. ...
They developed a method to fuse airborne LiDAR and multispectral imagery with two main consecutive steps: 1) Point cloud segmentation (region growing) and classification (mean shift) using 3D LiDAR and ...
arXiv:1812.08287v1
fatcat:hmojxdoaybc6vjeto5s3x7b6z4
FOREST ROADIDENTIFICATION AND EXTRACTIONOF THROUGH ADVANCED LOG MATCHING TECHNIQUES
2017
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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The algorithm utilized Laplacian of Gaussian (LoG) filter and slope calculation on high resolution multispectral imagery and LiDAR data respectively to extract both primary road and secondary road segments ...
A novel algorithm for forest road identification and extraction was developed. ...
for providing the very high resolution aerial imagery. ...
doi:10.5194/isprs-archives-xlii-3-w3-201-2017
fatcat:azlw4aoq6fco7isyari3nhmdam
Road Feature Extraction from High Resolution Aerial Images Upon Rural Regions Based on Multi-Resolution Image Analysis and Gabor Filters
[chapter]
2012
Remote Sensing - Advanced Techniques and Platforms
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., 2001) , a fully automated road extraction strategy based on Kohonen's self-organizing map (SOM) is proposed to detect road information in high-resolution multi-spectral aerial imagery. ...
Traditional methods for acquiring road information include i) ground surveying and ii) delineating roads from remotely sensed imagery (Zhang & Couloigner, 2004) . ...
Road feature extraction for rural and urban areas from high spatial resolution remotely sensed imagery is discussed separately in this section. ...
doi:10.5772/45893
fatcat:rgsrzd7mmvbmpbugdg2rbbuhoe
Remote Sensing Satellite Image Processing Techniques for Image Classification: A Comprehensive Survey
2017
International Journal of Computer Applications
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In remote sensing, the image processing techniques can be categories in to four main processing stages: Image preprocessing, Enhancement, Transformation and Classification. ...
This paper is a brief survey of advance technological aspects of Digital Image Processing which are applied to remote sensing images obtained from various satellite sensors. ...
Dutta et al., [9] Categorize vegetation canopy based on leaves area per density( LAD) in dense forest using airborne LIDAR remote sensing imagery and hyper spectral imagery based on Pit-Free Canopy Height ...
doi:10.5120/ijca2017913306
fatcat:2bzxqgiy2nfkldftt5wgj4v4e4
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