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S. Daneshtalab, H. Rastiveis, B. Hosseiny
2019 The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences  
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


K. Suzuki, U. Rin, Y. Maeda, H. Takeda
2018 The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences  
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

Pedram Ghamisi, Bernhard Hofle, Xiao Xiang Zhu
2017 IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing  
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

Ali Jamali, Masoud Mahdianpari
2022 Remote Sensing  
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]

Wenxia Liu, Feng Gao, Junyu Dong
2021 arXiv   pre-print
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

Hao Li, Pedram Ghamisi, Uwe Soergel, Xiao Zhu
2018 Remote Sensing  
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

Zhanyuan Chang, Huiling Yu, Yizhuo Zhang, Keqi Wang
2020 Sensors  
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]

Min Feng, Feng Gao, Jian Fang, Junyu Dong
2021 arXiv   pre-print
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


Z. Nordin, H. Z. M. Shafri, A. F. Abdullah, S. J. Hashim
2019 The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences  
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

Yushi Chen, Chunyang Li, Pedram Ghamisi, Xiuping Jia, Yanfeng Gu
2017 IEEE Geoscience and Remote Sensing Letters  
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

Agnieszka Kuras, Maximilian Brell, Jonathan Rizzi, Ingunn Burud
2021 Remote Sensing  
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

Wei Song, Lingfeng Zhang, Yifei Tian, Simon Fong, Jinming Liu, Amanda Gozho
2020 Human-Centric Computing and Information Sciences  
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]

Rupak Bose, Shivam Pande, Biplab Banerjee
2021 arXiv   pre-print
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

Aili Wang, Minhui Wang, Haibin Wu, Kaiyuan Jiang, Yuji Iwahori
2020 Sensors  
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

Takayuki Shinohara, Haoyi Xiu, Masashi Matsuoka
2020 Sensors  
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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