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Advanced Multi-Sensor Optical Remote Sensing for Urban Land Use and Land Cover Classification: Outcome of the 2018 IEEE GRSS Data Fusion Contest

Yonghao Xu, Bo Du, Liangpei Zhang, Daniele Cerra, Miguel Pato, Emiliano Carmona, Saurabh Prasad, Naoto Yokoya, Ronny Hansch, Bertrand Le Saux
2019 IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing  
This paper presents the scientific outcomes of the 2018 Data Fusion Contest organized by the Image Analysis and Data Fusion Technical Committee of the IEEE Geoscience and Remote Sensing Society.  ...  Index Terms-Convolutional neural networks (CNN), deep learning, hyperspectral (HS) imaging (HSI), image analysis and data fusion, multimodal, multiresolution, multisource, multispectral light detection  ...  Le Saux would like to thank R. Daudt for the help with building the ground-truth. S. Prasad would like to thank Dr. J. F. Diaz for preprocessing and preparing the data, as well as F. F.  ... 
doi:10.1109/jstars.2019.2911113 fatcat:r5qtkkthfvf7dpde3adq6xsrh4

Identifying Illicit Drug Dealers on Instagram with Large-scale Multimodal Data Fusion [article]

Chuanbo Hu, Minglei Yin, Bin Liu, Xin Li, Yanfang Ye
2021 arXiv   pre-print
We then design a quadruple-based multimodal fusion method to combine the multiple data sources associated with each user account for drug dealer identification.  ...  How to identify illicit drug dealers from social media data has remained a technical challenge due to the following reasons.  ...  Social media mining: an introduction. Cambridge University Press. [78] Jixian Zhang. 2010. Multi-source remote sensing data fusion: status and trends.  ... 
arXiv:2108.08301v2 fatcat:r5omsmxaenfslcy6zdkt427ggq

A Hybrid Attention-Aware Fusion Network (HAFNet) for Building Extraction from High-Resolution Imagery and LiDAR Data

Peng Zhang, Peijun Du, Cong Lin, Xin Wang, Erzhu Li, Zhaohui Xue, Xuyu Bai
2020 Remote Sensing  
Furthermore, an attention-aware multimodal fusion block (Att-MFBlock) was introduced to overcome the fusion problem by adaptively selecting and combining complementary features from each modality.  ...  Recent studies have examined the role that deep learning (DL) could play in both multimodal data fusion and urban object extraction.  ...  Conflicts of Interest: The authors declare no conflict of interest.  ... 
doi:10.3390/rs12223764 fatcat:ppvlufjy6bfqpdn446t7atit2i

Sensor and Sensor Fusion Technology in Autonomous Vehicles: A Review

De Jong Yeong, Gustavo Velasco-Hernandez, John Barry, Joseph Walsh
2021 Sensors  
The current paper, therefore, provides an end-to-end review of the hardware and software methods required for sensor fusion object detection.  ...  We present an overview of the three primary categories of sensor calibration and review existing open-source calibration packages for multi-sensor calibration and their compatibility with numerous commercial  ...  - Pose a challenge to detect smaller obstacles but performs better than YOLO. - Poor extractions of features in shallow layers. - Loss of features in deep layers.  ... 
doi:10.3390/s21062140 pmid:33803889 pmcid:PMC8003231 fatcat:j52leqrvwnhu5brd7lxgozvwya

Multi-Sensor Image Fusion: A Survey of the State of the Art

Bing Li, Yong Xian, Daqiao Zhang, Juan Su, Xiaoxiang Hu, Weilin Guo
2021 Journal of Computer and Communications  
Taking remote sensing as an example, [25] summarized the early proposed remote sensing image fusion algorithms, [26] conducted a critical comparison of recently proposed remote sensing image fusion methods  ...  Section 3 presents an overview of the fusion strategy. Moreover, an overview of the fusion performance assessment metrics is introduced in Section 4.  ...  Conflicts of Interest The authors declare no conflicts of interest regarding the publication of this paper.  ... 
doi:10.4236/jcc.2021.96005 fatcat:ulm4hbfy5ndkdne6fng6ncyn3i

Gated Fully Fusion for Semantic Segmentation

Xiangtai Li, Houlong Zhao, Lei Han, Yunhai Tong, Shaohua Tan, Kuiyuan Yang
2020 PROCEEDINGS OF THE THIRTIETH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE AND THE TWENTY-EIGHTH INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE  
High-level features from Deep Convolutional Neural Networks already demonstrate their effectiveness in semantic segmentation tasks, however the coarse resolution of high-level features often leads to inferior  ...  In this paper, we propose a new architecture, named Gated Fully Fusion(GFF), to selectively fuse features from multiple levels using gates in a fully connected way.  ...  Therefore, an advanced fusion mechanism is required to collect information selectively from different feature maps.  ... 
doi:10.1609/aaai.v34i07.6805 fatcat:l6ndyyyjevcrbiyhhrbjxdlgje

Special Section Guest Editorial: Feature and Deep Learning in Remote Sensing Applications

John E. Ball, Derek T. Anderson, Chee Seng Chan
2018 Journal of Applied Remote Sensing  
This special section is centered on recent advancements in deep learning (and just feature learning in general) in the area of remote sensing.  ...  Specifically, humans have been the architects to date of features, algorithms (e.g., classifiers) and their fusion within and across sensors and platforms (e.g., satellites, UAVs, etc.).  ...  and open problems in deep learning for remote sensing, discusses modifications of DL architectures for remote sensing, provides an overview of deep learning tools, and gives an extensive summary of remote  ... 
doi:10.1117/1.jrs.11.042601 fatcat:pq3xg2sggfdtljjs3hrmp7tzdm

SuperDeConFuse: A Supervised Deep Convolutional Transform based Fusion Framework for Financial Trading Systems [article]

Pooja Gupta, Angshul Majumdar, Emilie Chouzenoux, Giovanni Chierchia
2020 arXiv   pre-print
Although many deep learning models have recently been proposed in this domain, most of them treat the stock trading time-series data as 2-D image data, whereas its true nature is 1-D time-series data.  ...  Since the stock trading systems are multi-channel data, many existing techniques treating them as 1-D time-series data are not suggestive of any technique to effectively fusion the information carried  ...  Deep fusion of remote sensing data for accurate classification. https://doi.org/10.1109/LGRS.2017.2704625. Chouzenoux, E., Pesquet, J. C., & Repetti, A. (2016).  ... 
arXiv:2011.04364v1 fatcat:oig4t5iarnaudkvvsevvicntfy

Multimodal Classification: Current Landscape, Taxonomy and Future Directions [article]

William C. Sleeman IV, Rishabh Kapoor, Preetam Ghosh
2021 arXiv   pre-print
Multimodal classification research has been gaining popularity in many domains that collect more data from multiple sources including satellite imagery, biometrics, and medicine.  ...  Many of the most difficult aspects of unimodal classification have not yet been fully addressed for multimodal datasets including big data, class imbalance, and instance level difficulty.  ...  Results of the 2017 IEEE Geoscience and Remote Sensing Society Data Fusion Contest showed that the top teams all utilized data from multiple sources and used ensemble methods [114] .  ... 
arXiv:2109.09020v1 fatcat:yagsbnxeefcpneqwgflrxxioqa

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 4934-4946 Convolutional Neural Network to Retrieve Water Depth in Marine Shallow Water Area From Remote Sensing Images.  ...  ., +, JSTARS 2020 1986-1995 Convolutional Neural Network to Retrieve Water Depth in Marine Shallow Water Area From 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

YOLOrs: Object Detection in Multimodal Remote Sensing Imagery

Manish Sharma, Mayur Dhanaraj, Srivallabha Karnam, Dimitris G. Chachlakis, Raymond Ptucha, Panos P. Markopoulos, Eli Saber
2021 IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing  
Deep-learning object detection methods that are designed for computer vision applications tend to underperform when applied to remote sensing data.  ...  Index Terms-Aerial imagery, fusion, multimodal, object detection, remote sensing (RS).  ...  YOLOrs has the capability to leverage data from multiple sensing modalities through a novel mid-level fusion scheme.  ... 
doi:10.1109/jstars.2020.3041316 fatcat:6xzsvwkeava7nedb7cnn2ejhye

A survey on deep multimodal learning for computer vision: advances, trends, applications, and datasets

Khaled Bayoudh, Raja Knani, Fayçal Hamdaoui, Abdellatif Mtibaa
2021 The Visual Computer  
The growing potential of multimodal data streams and deep learning algorithms has contributed to the increasing universality of deep multimodal learning.  ...  In particular, we summarize six perspectives from the current literature on deep multimodal learning, namely: multimodal data representation, multimodal fusion (i.e., both traditional and deep learning-based  ...  Feature-level fusion (i.e., early fusion) provides a richness of information from heterogeneous data.  ... 
doi:10.1007/s00371-021-02166-7 pmid:34131356 pmcid:PMC8192112 fatcat:jojwyc6slnevzk7eaiutlmlgfe

Front Matter: Volume 11519

Hiroshi Fujita, Xudong Jiang
2020 Twelfth International Conference on Digital Image Processing (ICDIP 2020)  
The papers will provide the readers an overview of many recent advances in the fields related to digital image processing.  ...  angle bounding box for remote sensing image [11519-58] 11519 10 A real time fusion system of infrared and low level light images based on FPGA [11519-64] 11519 11 Improvement of surface penetrating  ... 
doi:10.1117/12.2574725 fatcat:25m4fbyx7begned4fzymjv45tm

X-ModalNet: A Semi-Supervised Deep Cross-Modal Network for Classification of Remote Sensing Data [article]

Danfeng Hong, Naoto Yokoya, Gui-Song Xia, Jocelyn Chanussot, Xiao Xiang Zhu
2020 arXiv   pre-print
This paper addresses the problem of semi-supervised transfer learning with limited cross-modality data in remote sensing.  ...  , by learning to transfer more discriminative information from a small-scale hyperspectral image (HSI) into the classification task using a large-scale MSI or SAR data.  ...  Related to ours for scene parsing with multimodal deep networks, an early deep fusion architecture, simply stacking all multi-modalities as input, is used for semantic segmentation of urban RS images  ... 
arXiv:2006.13806v1 fatcat:3b47auxsb5fzvc74uim5kkhwhm

RGB-D-Based Object Recognition Using Multimodal Convolutional Neural Networks: A Survey

Mingliang Gao, Jun Jiang, Guofeng Zou, Vijay John, Zheng Liu
2019 IEEE Access  
RGB-D-based object recognition has evolved from early methods that using hand-crafted representations to the current state-of-the-art deep learning-based methods.  ...  We highlight two key issues, namely, training data deficiency and multimodal fusion.  ...  [145] exploited CNNs with deeply local description for remote sensing image classification and proved that deeply local descriptors outperformed the features extracted from fully connected layers.  ... 
doi:10.1109/access.2019.2907071 fatcat:shamfnufhfavjlcnrcldpgqtgq
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