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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. ...
Recently, fusion of aerial imagery and light detection and ranging (LiDAR) data has obtained a great attention in RS communities. ...
the data used in this study, and the IEEE GRSS Image Analysis and Data Fusion Technical Committee. ...
doi:10.5194/isprs-archives-xlii-4-w18-279-2019
fatcat:w3hzfgmrdnc63m7kvs2zcpm5fq
FOREST COVER CLASSIFICATION USING GEOSPATIAL MULTIMODAL DATA
2018
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Using simultaneously acquired airborne images and LiDAR data, we attempt to reproduce the 3D knowledge of tree shape, which interpreters potentially make use of. ...
Geospatial features which support interpretation are also used as inputs to the CNN. ...
Using simultaneously acquired airborne images and LiDAR data, we fed the 3D knowledge of tree shape (i.e. voxel) and geospatial features as well as RGB images to the proposed CNN. ...
doi:10.5194/isprs-archives-xlii-2-1091-2018
fatcat:zkyxno5jr5bs3d3r4jsujicm5m
Hyperspectral and LiDAR Data Fusion Using Extinction Profiles and Deep Convolutional Neural Network
2017
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
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This paper proposes a novel framework for the fusion of hyperspectral and LiDAR-derived rasterized data using extinction profiles (EPs) and deep learning. ...
The proposed approach is applied to two data sets acquired in Houston, USA and Trento, Italy. ...
Classification maps for Houston data: The outputs of RF on (a) hyperspectral data, (b) the stack of LiDAR and hyperspectral data, and (c) the proposed approach using feature stacking; the outputs of CNN-based ...
doi:10.1109/jstars.2016.2634863
fatcat:ft2d3fc6tnfcvkwvfcctthjl5u
Swin Transformer and Deep Convolutional Neural Networks for Coastal Wetland Classification Using Sentinel-1, Sentinel-2, and LiDAR Data
2022
Remote Sensing
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deep CNN network of VGG-16, a 3D CNN network, and Swin transformer for complex coastal wetland classification. ...
Results achieved in this study suggest the high potential for integrating and using CNN networks with the cutting-edge transformers for the classification of complex landscapes in remote sensing. ...
LiDAR data in the Swin transformer network and compared the achieved results with several solo CNN models as well as two shallow conventional classifiers of RF and SVM. ...
doi:10.3390/rs14020359
fatcat:t74rfxszwbew5d4tq2gecfrpfa
Disentangled Non-Local Network for Hyperspectral and LiDAR Data Classification
[article]
2021
arXiv
pre-print
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In this model, according to the spectral and spatial characteristics of HSI and LiDAR, a multiscale module and a convolutional neural network (CNN) are used to capture the spectral and spatial characteristics ...
In order to tackle this limitation, we propose a simple yet effective attention fusion model based on Disentangled Non-local (DNL) network for hyperspectral and LiDAR data joint classification task. ...
On the other hand, LiDAR data is also widely used in the classification of high-value crops [3] , urban land use analysis. ...
arXiv:2104.02302v1
fatcat:pheoqd2b7jbnrg6t63bcknr63a
Hyperspectral and LiDAR Fusion Using Deep Three-Stream Convolutional Neural Networks
2018
Remote Sensing
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First, extinction profiles are applied to both data sources in order to extract spatial and elevation features from hyperspectral and LiDAR-derived data, respectively. ...
In this paper, a novel framework is proposed for the fusion of hyperspectral images and LiDAR-derived elevation data based on CNN and composite kernels. ...
The same appreciation goes to the National Center for Airborne Laser Mapping (NCALM) at the University of Houston for providing the Houston data set and the IEEE GRSS Image Analysis and Data Fusion Technical ...
doi:10.3390/rs10101649
fatcat:7dnh6ggccnenrayg3f7tvhxhxu
Fusion of Hyperspectral CASI and Airborne LiDAR Data for Ground Object Classification through Residual Network
2020
Sensors
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Hyperspectral imaging has been widely used in the classification of ground objects because of its high resolution, multiple bands, and abundant spatial and spectral information. ...
By combining the characteristics of hyperspectral compact airborne spectrographic imager (CASI) data and airborne LiDAR data, we extracted a variety of features for data fusion and ground object classification ...
Acknowledgments: The authors sincerely thank the Shanghai Science and Technology Commission for their funding support.
Conflicts of Interest: The authors declare no conflict of interest. ...
doi:10.3390/s20143961
pmid:32708693
pmcid:PMC7412085
fatcat:6owydygp5vag3evdj7plczl3te
Hyperspectral and LiDAR data classification based on linear self-attention
[article]
2021
arXiv
pre-print
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An efficient linear self-attention fusion model is proposed in this paper for the task of hyperspectral image (HSI) and LiDAR data joint classification. ...
The attention module is a plug-and-play linear self-attention module that can be extensively used in any model. The proposed model has achieved the overall accuracy of 95.40\% on the Houston dataset. ...
Therefore, joint classification by using HSI and LiDAR data has become a research hotspot in remote sensing communities. ...
arXiv:2104.02301v1
fatcat:domk2l3iv5g6pimqltda2fgh7y
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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coverage and weather independent images, which in turn provides faster turnaround times for creation of large area geospatial data. ...
Whilst mapping and surveying work using airborne SAR have started to capture many interest among surveyors, professionals and practitioners abroad, Malaysia however is still lacking behind in term of the ...
The three algorithms were evaluated using a 40-cm spatial resolution digital orthophoto and the corresponding LIDAR data of Odense, Denmark. ...
doi:10.5194/isprs-archives-xlii-4-w16-489-2019
fatcat:xhlrmiru5reo5fbqkhafxucl6a
Deep Fusion of Remote Sensing Data for Accurate Classification
2017
IEEE Geoscience and Remote Sensing Letters
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The proposed framework employs deep convolutional neural networks (CNNs) to effectively extract features of multi-/hyper-spectral and light detection and ranging (LiDAR) data. ...
Index Terms-Convolutional neural network (CNN), data fusion, deep neural network (DNN), feature extraction (FE), multispectral image (MSI), hyperspectral image (HSI), light detection and ranging (LiDAR ...
The proposed deep model uses CNNs to extract the spectral-spatial features of MSI/HSI as well as the spatial-elevation features of LiDAR data. ...
doi:10.1109/lgrs.2017.2704625
fatcat:bmplmahdureynasabirdfavz24
Hyperspectral and Lidar Data Applied to the Urban Land Cover Machine Learning and Neural-Network-Based Classification: A Review
2021
Remote Sensing
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Rapid technological advances in airborne hyperspectral and lidar systems paved the way for using machine learning algorithms to map urban environments. ...
This paper presents principles and key features for airborne hyperspectral imaging, lidar, and the fusion of those, as well as applications of these for urban land cover classification. ...
[265] created a CNN framework used to extract the spectral-spatial features of HS data and the elevation features of lidar data. ...
doi:10.3390/rs13173393
fatcat:3w42hdwu6rbzfgqktkbjrapdc4
CNN-based 3D object classification using Hough space of LiDAR point clouds
2020
Human-Centric Computing and Information Sciences
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Thus, this paper proposes a Convolutional Neural Network (CNN)-based 3D object classification method using the Hough space of LiDAR point clouds to overcome these problems. ...
First, object point clouds are transformed into Hough space using a Hough transform algorithm, and then the Hough space is rasterized into a series of uniformly sized grids. ...
Authors' contributions LZ, YT and WS implemented the experiments and wrote the whole manuscript. SF and AG revised the manuscript. JL surveyed the related works. ...
doi:10.1186/s13673-020-00228-8
fatcat:kgfmyvtbpzgazbzg557i4jfuau
Two Headed Dragons: Multimodal Fusion and Cross Modal Transactions
[article]
2021
arXiv
pre-print
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to extract the spectral and spatial information from HSI and LiDAR. ...
The model is composed of stacked auto encoders that harness the cross key-value pairs for HSI and LiDAR, thus establishing a communication between the two modalities, while simultaneously using the CNNs ...
ical location. • We utilize CNNs to capture spatial data while using attention modes to capture the spectral essence and to achieve a true spatio-spectral fusion module. • We introduce a modular design ...
arXiv:2107.11585v1
fatcat:fx4ybbydjzfdhgifecgn4oekpa
A Novel LiDAR Data Classification Algorithm Combined CapsNet with ResNet
2020
Sensors
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LiDAR data contain feature information such as the height and shape of the ground target and play an important role for land classification. ...
Two different LiDAR data sets and several classic machine learning algorithms are used for comparative experiments. ...
Acknowledgments: The authors would like to thank the support of the laboratory and university.
Conflicts of Interest: The authors declare no conflict of interest. ...
doi:10.3390/s20041151
pmid:32093132
pmcid:PMC7071473
fatcat:qfasy4ix6bge3csvqw3axxxwjy
FWNet: Semantic Segmentation for Full-Waveform LiDAR Data Using Deep Learning
2020
Sensors
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In this paper, we investigate the possibility of using a deep learning method to solve the semantic segmentation task of airborne full-waveform light detection and ranging (lidar) data that consists of ...
Moreover, deep learning-based-techniques have demonstrated state-of-the-art performance for supervised learning tasks on 3D point cloud data, such as classification and segmentation tasks for open datasets ...
Acknowledgments: We thank the Helica s.r.l. and the authors of Zorti et al. (2019) for providing the full-waveform LiDAR data in the public domain. ...
doi:10.3390/s20123568
pmid:32599774
pmcid:PMC7349408
fatcat:gbb6sfdtirdmfjk3qf43l345ka
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