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2020 IEEE Geoscience and Remote Sensing Letters  
Cheng 499 Fisher Vector Encoding of Supervoxel-Based Features for Airborne LiDAR Data Classification ......................... ..........................................................................  ...  Li 524 Deep Residual Encoder-Decoder Networks for Desert Seismic Noise Suppression ........................................ .............................................................................  ... 
doi:10.1109/lgrs.2020.2972782 fatcat:pyplqlwzsvaphf5q6r4e7jp54u

Deep Learning for Fusion of APEX Hyperspectral and Full-Waveform LiDAR Remote Sensing Data for Tree Species Mapping

Wenzhi Liao, Frieke Van Coillie, Lianru Gao, Liwei Li, Bing Zhang, Jocelyn Chanussot
2018 IEEE Access  
However, current deep learning architecture for multi-sensor data fusion might not always perform better than single data source, especially for the fusion of hyperspectral and light detection and ranging  ...  Experimental results on fusing real APEX hyperspectral and LiDAR data demonstrate the effectiveness of the proposed deep fusion framework.  ...  auto-encoder instead of deep sparse filters.  ... 
doi:10.1109/access.2018.2880083 fatcat:etaikage6rdpfgcp62skznsl2m

Classification of Hyperspectral and LiDAR Data Using Coupled CNNs

Renlong Hang, Zhu Li, Pedram Ghamisi, Danfeng Hong, Guiyu Xia, Qingshan Liu
2020 IEEE Transactions on Geoscience and Remote Sensing  
In this paper, we propose an efficient and effective framework to fuse hyperspectral and Light Detection And Ranging (LiDAR) data using two coupled convolutional neural networks (CNNs).  ...  One CNN is designed to learn spectral-spatial features from hyperspectral data, and the other one is used to capture the elevation information from LiDAR data.  ...  Specifically, it first mapped the hyperspectral data into a hidden space via an encoding path, and then reconstructed the LiDAR data with a decoding path.  ... 
doi:10.1109/tgrs.2020.2969024 fatcat:sn6fcji6drhrtbgsvtseh4hbke

THE POTENTIAL OF BUILDING DETECTION FROM SAR AND LIDAR USING DEEP LEARNING

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.  ...  knowledge and the usage of this technology together with Deep Learning, Machine Learning approach especially in building extraction for topographic mapping and urban planning and development.  ...  An AE is composed of an encoder and a decoder.  ... 
doi:10.5194/isprs-archives-xlii-4-w16-489-2019 fatcat:xhlrmiru5reo5fbqkhafxucl6a

Three-Dimensional Urban Land Cover Classification by Prior-Level Fusion of LiDAR Point Cloud and Optical Imagery

Yanming Chen, Xiaoqiang Liu, Yijia Xiao, Qiqi Zhao, Sida Wan
2021 Remote Sensing  
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.  ...  Then, a LiDAR point cloud is linked to the prior information using the nearest neighbor method and classified by a deep neural network.  ...  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

Table of contents

2020 IEEE Geoscience and Remote Sensing Letters  
Multispectral Data Residual Encoder-Decoder Conditional Generative Adversarial Network for Pansharpening ............................. ..................................................................  ...  Zhang 1573 Hyperspectral Data Deep Feature-Based Multitask Joint Sparse Representation for Hyperspectral Image Classification ..................... .....................................................  ... 
doi:10.1109/lgrs.2020.3016456 fatcat:jdpckutnjfd45dhp4j5pudigwm

Table of contents

2020 IEEE Transactions on Geoscience and Remote Sensing  
Li, and Q. Du 4590 Band-Independent Encoder-Decoder Network for Pan-Sharpening of Remote Sensing Images .......................... ............................................................. C.  ...  For more information please see "Inflight Performance of the TanSat Atmospheric Carbon Dioxide Grating Spectrometer," by Zhongdong Yang et al., which begins on page 4691. 4764 Lidar Data Classification  ... 
doi:10.1109/tgrs.2020.2998593 fatcat:6li5sdy3nrfnxbveudd77nj5ay

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  
., +, JSTARS 2020 1450-1466 Using An Attention-Based LSTM Encoder-Decoder Network for Near Real-Time Disturbance Detection.  ...  ., +, JSTARS 2020 2369-2384 Using An Attention-Based LSTM Encoder-Decoder Network for Near Real- Time Disturbance Detection.  ...  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

Multiscale Semantic Feature Optimization and Fusion Network for Building Extraction Using High-Resolution Aerial Images and LiDAR Data

Qinglie Yuan, Helmi Zulhaidi Mohd Shafri, Aidi Hizami Alias, Shaiful Jahari Hashim
2021 Remote Sensing  
images and LiDAR data for building extraction.  ...  To address the above problems, this paper proposes an FCN framework based on the residual network and provides the training pattern for multi-modal data combining the advantage of high-resolution aerial  ...  As shown in the decoder parts of Figure 1 , the network uses pattern A (backbone + branch + decoder) to extract features for multi-modal data, while pattern B (backbone + decoder) extracts features for  ... 
doi:10.3390/rs13132473 fatcat:thupwpioxvbthn65i2q2bkxkqu

Joint Learning from Earth Observation and OpenStreetMap Data to Get Faster Better Semantic Maps

Nicolas Audebert, Bertrand Le Saux, Sebastien Lefevre
2017 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)  
Deep neural networks have been used in the past for remote sensing data classification from various sensors, including multispectral, hyperspectral, SAR and LiDAR data.  ...  In this paper, we study different use cases and deep network architectures to leverage OpenStreetMap data for semantic labeling of aerial and satellite images.  ...  The authors would like to thank the WUDAPT (http://www.wudapt.org/) and GeoWIKI (http://geo-wiki.org/) initiatives and the IEEE GRSS Image Analysis and Data Fusion Technical Committee.  ... 
doi:10.1109/cvprw.2017.199 dblp:conf/cvpr/AudebertSL17 fatcat:yub7f6rsxnadrcj4l2qbzxy6o4

FUSION OF HYPERSPECTRAL, MULTISPECTRAL, COLOR AND 3D POINT CLOUD INFORMATION FOR THE SEMANTIC INTERPRETATION OF URBAN ENVIRONMENTS

M. Weinmann, M. Weinmann
2019 The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences  
To assess the potential of the different feature sets and their combination, we present results achieved for the MUUFL Gulfport Hyperspectral and LiDAR Airborne Data Set.</p>  ...  </strong> In this paper, we address the semantic interpretation of urban environments on the basis of multi-modal data in the form of RGB color imagery, hyperspectral data and LiDAR data acquired from  ...  Among a diversity of proposed network architectures, exemplary approaches rely on the use of a fully convolutional network (Sherrah, 2016) , an encoder-decoder architecture (Volpi and Tuia, 2017) or  ... 
doi:10.5194/isprs-archives-xlii-2-w13-1899-2019 fatcat:3xq3myk2jng4la36z24q6ssgu4

Joint Learning from Earth Observation and OpenStreetMap Data to Get Faster Better Semantic Maps [article]

Nicolas Audebert , Sébastien Lefèvre
2017 arXiv   pre-print
Deep neural networks have been used in the past for remote sensing data classification from various sensors, including multispectral, hyperspectral, SAR and LiDAR data.  ...  In this paper, we study different use cases and deep network architectures to leverage OpenStreetMap data for semantic labeling of aerial and satellite images.  ...  Acknowledgements The Vaihingen dataset was provided by the German Society for Photogrammetry, Remote Sensing and Geoinformation (DGPF) [7] : http://www.ifp. uni-stuttgart.de/dgpf/DKEP-Allg.html.  ... 
arXiv:1705.06057v1 fatcat:ng3ptiqldvgztpvc6fpjqqyw4a

Advances in Deep Learning for Hyperspectral Image Analysis—Addressing Challenges Arising in Practical Imaging Scenarios [chapter]

Xiong Zhou, Saurabh Prasad
2020 Advances in Computer Vision and Pattern Recognition  
Deep neural networks have proven to be very effective for computer vision tasks, such as image classification, object detection, and semantic segmentation -- these are primarily applied to color imagery  ...  In recent years, there has been an emergence of deep learning algorithms being applied to hyperspectral and multispectral imagery for remote sensing and biomedicine tasks.  ...  In addition to the categorical cross entropy loss, a symmetric decoder branch was added to the MLP and multiple reconstruction losses, measured by the mean squared error of the encoder and decoder, were  ... 
doi:10.1007/978-3-030-38617-7_5 fatcat:23ibk4ojbvepbpikxgjxan4i6e

A Survey: Deep Learning for Hyperspectral Image Classification with Few Labeled Samples

Sen Jia, Shuguo Jiang, Zhijie Lin, Nanying Li, Meng Xu, Shiqi Yu
2021 Neurocomputing  
With the rapid development of deep learning technology and improvement in computing capability, deep learning has been widely used in the field of hyperspectral image (HSI) classification.  ...  Therefore, many research works focus on building a deep learning model for HSI classification with few labeled samples.  ...  Autoencoder for HSI classification An autoencoder [30] is a classic neural network, which consists of two parts: an encoder and a decoder.  ... 
doi:10.1016/j.neucom.2021.03.035 fatcat:jkufaor2jnbcvei5ndhqukrhoy

ICACC 2019 TOC

2019 2019 9th International Conference on Advances in Computing and Communication (ICACC)  
Design Of An Autonomous Mobile Robot Based On The Sensor Data Fusion Of Lidar 360, Ultrasonic Sensor And Wheel Speed Encoder Mukund S Chettiar, Premnath S, Sivasankaran K, Sidaarth R And Adarsh SCost Effective  ...  S Alzheimer's Disease Classification Using Deep Convolutional Neural Network Blessy C Simon And Dr.  ... 
doi:10.1109/icacc48162.2019.8986197 fatcat:fzj3onsn6fgs3bdtuxms2frilm
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