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Multimodal Remote Sensing Benchmark Datasets for Land Cover Classification with A Shared and Specific Feature Learning Model [article]

Danfeng Hong and Jingliang Hu and Jing Yao and Jocelyn Chanussot and Xiao Xiang Zhu
2021 arXiv   pre-print
To this end, we propose a shared and specific feature learning (S2FL) model.  ...  and synthetic aperture radar (SAR) data, Augsburg -- hyperspectral, SAR, and digital surface model (DSM) data, are released and used for land cover classification.  ...  Acknowledgement The authors would like to the Hyperspectral Image Analysis group at the University of Houston and the IEEE GRSS DFC2013 for providing the University of Houston HS dataset.  ... 
arXiv:2105.10196v1 fatcat:hjm5sqvugfc5te4saylthvz4fm

Multimodal remote sensing benchmark datasets for land cover classification with a shared and specific feature learning model

Danfeng Hong, Jingliang Hu, Jing Yao, Jocelyn Chanussot, Xiao Xiang Zhu
2021 ISPRS journal of photogrammetry and remote sensing (Print)  
To this end, we propose a shared and specific feature learning (S2FL) model.  ...  and synthetic aperture radar (SAR) data, Augsburghyperspectral, SAR, and digital surface model (DSM) data, are released and used for land cover classification.  ...  Acknowledgement The authors would like to the Hyperspectral Image Analysis group at the University of Houston and the IEEE GRSS DFC2013 for providing the University of Houston HS dataset.  ... 
doi:10.1016/j.isprsjprs.2021.05.011 fatcat:zx4qrfd5c5gh3odgah5mmatqua

Global Land-Cover Mapping With Weak Supervision: Outcome of the 2020 IEEE GRSS Data Fusion Contest

Caleb Robinson, Kolya Malkin, Nebojsa Jojic, Huijun Chen, Rongjun Qin, Changlin Xiao, Michael Schmitt, Pedram Ghamisi, Ronny Hansch, Naoto Yokoya
2021 IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing  
The 2020 Contest addressed the problem of automatic global land-cover mapping with weak supervision, i.e., estimating high-resolution semantic maps while only low-resolution reference data are available  ...  This article presents the scientific outcomes of the 2020 Data Fusion Contest (DFC2020) organized by the Image Analysis and Data Fusion Technical Committee of the IEEE Geoscience and Remote Sensing Society  ...  The DFC2020 provides one of the first benchmark datasets for large-scale weakly supervised learning in the context of global land-use/cover classification from multimodal data.  ... 
doi:10.1109/jstars.2021.3063849 fatcat:mkv2d7cv4vbudog6vtexun452m

FOREST COVER CLASSIFICATION USING GEOSPATIAL MULTIMODAL DATA

K. Suzuki, U. Rin, Y. Maeda, H. Takeda
2018 The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences  
To address climate change, accurate and automated forest cover monitoring is crucial.  ...  Inspired by the interpreters' techniques, we propose a unified approach that integrates these datasets in a shallow layer in the CNN network.  ...  ACKNOWLEDGEMENTS We would like to thank Nakayama Forester for their help in a field survey. This work was supported by JST ACT-I Grant Number JPMJPR16UE, Japan.  ... 
doi:10.5194/isprs-archives-xlii-2-1091-2018 fatcat:zkyxno5jr5bs3d3r4jsujicm5m

Self-supervised Audiovisual Representation Learning for Remote Sensing Data [article]

Konrad Heidler, Lichao Mou, Di Hu, Pu Jin, Guangyao Li, Chuang Gan, Ji-Rong Wen, Xiao Xiang Zhu
2021 arXiv   pre-print
By fine-tuning the models on a number of commonly used remote sensing datasets, we show that our approach outperforms existing pre-training strategies for remote sensing imagery.  ...  In remote sensing, the lack of comparable large annotated datasets and the wide diversity of sensing platforms impedes similar developments.  ...  The DeepGlobe Land Cover Classification Challenge [57] aims to provide a benchmark for this task.  ... 
arXiv:2108.00688v1 fatcat:bhvcwavkibhxfmezayic5yryfe

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

William C. Sleeman IV, Rishabh Kapoor, Preetam Ghosh
2021 arXiv   pre-print
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.  ...  We address these challenges by proposing a new taxonomy for describing such systems based on trends found in recent publications on multimodal classification.  ...  Domain Specific Solutions One of the common applications for mutlimodal learning is remote sensing with hyperspectral satellite imagery.  ... 
arXiv:2109.09020v1 fatcat:yagsbnxeefcpneqwgflrxxioqa

Noise-Tolerant Deep Neighborhood Embedding for Remotely Sensed Images with Label Noise

Jian Kang, Ruben Fernandez-Beltran, Xudong Kang, Jingen Ni, Antonio J Plaza
2021 IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing  
Recently, many deep learning-based methods have been developed for solving remote sensing (RS) scene classification or retrieval tasks.  ...  Our experiments, conducted on two benchmark RS datasets, validate the effectiveness of the proposed approach on three different RS scene interpretation tasks, including classification, clustering, and  ...  Both the AID and NWPU-RESISC45 datasets are designed for land-cover or land-use classification.  ... 
doi:10.1109/jstars.2021.3056661 fatcat:h6vv4f7rfrhtpjchpqn4u63poq

Semantic Segmentation of Earth Observation Data Using Multimodal and Multi-scale Deep Networks [chapter]

Nicolas Audebert, Bertrand Le Saux, Sébastien Lefèvre
2017 Lecture Notes in Computer Science  
Especially, we train a variant of the SegNet architecture on remote sensing data over an urban area and study different strategies for performing accurate semantic segmentation.  ...  Our contributions are the following: 1) we transfer efficiently a DFCNN from generic everyday images to remote sensing images; 2) we introduce a multi-kernel convolutional layer for fast aggregation of  ...  The Vaihingen data set was provided by the German Society for Photogrammetry, Remote Sensing and Geoinformation (DGPF) [39] : http://www.ifp.uni-stuttgart.de/dgpf/DKEP-Allg.html.  ... 
doi:10.1007/978-3-319-54181-5_12 fatcat:k4kpmewrrfetdp36rq3xrluma4

Semantic Segmentation of Earth Observation Data Using Multimodal and Multi-scale Deep Networks [article]

Nicolas Audebert , Sébastien Lefèvre
2016 arXiv   pre-print
Especially, we train a variant of the SegNet architecture on remote sensing data over an urban area and study different strategies for performing accurate semantic segmentation.  ...  Our contributions are the following: 1) we transfer efficiently a DFCNN from generic everyday images to remote sensing images; 2) we introduce a multi-kernel convolutional layer for fast aggregation of  ...  The Vaihingen data set was provided by the German Society for Photogrammetry, Remote Sensing and Geoinformation (DGPF) [39] : http://www.ifp.uni-stuttgart.de/dgpf/DKEP-Allg.html.  ... 
arXiv:1609.06846v1 fatcat:7tu6as23pbd7xgsnjzokor3bp4

BigEarthNet-MM: A Large Scale Multi-Modal Multi-Label Benchmark Archive for Remote Sensing Image Classification and Retrieval [article]

Gencer Sumbul, Arne de Wall, Tristan Kreuziger, Filipe Marcelino, Hugo Costa, Pedro Benevides, Mário Caetano, Begüm Demir, Volker Markl
2021 arXiv   pre-print
multi-label remote sensing (RS) image retrieval and classification.  ...  Each pair of patches in BigEarthNet-MM is annotated with multi-labels provided by the CORINE Land Cover (CLC) map of 2018 based on its thematically most detailed Level-3 class nomenclature.  ...  INTRODUCTION As a result of advancements in satellite technology, recent years have witnessed a significant increase in the volume of remote sensing (RS) image archives.  ... 
arXiv:2105.07921v1 fatcat:pptg5dlcrbdcvldbyfxkykxfju

RSBNet: One-Shot Neural Architecture Search for A Backbone Network in Remote Sensing Image Recognition [article]

Cheng Peng, Yangyang Li, Ronghua Shang, Licheng Jiao
2021 arXiv   pre-print
In this paper, we research a new design paradigm for the backbone architecture in RSI recognition tasks, including scene classification, land-cover classification and object detection.  ...  Recently, a massive number of deep learning based approaches have been successfully applied to various remote sensing image (RSI) recognition tasks.  ...  , most existing methods for land-cover classification focus on improving the feature representation abilities of the backbone to adapt specific spatial patterns of remote sensing images.  ... 
arXiv:2112.03456v1 fatcat:jnamplj3urh6fnsia7gbnv7hd4

Challenges and Opportunities of Multimodality and Data Fusion in Remote Sensing

M. Dalla Mura, S. Prasad, F. Pacifici, P. Gamba, J. Chanussot, J. A. Benediktsson
2015 Proceedings of the IEEE  
Remote sensing is one of the most common ways to extract relevant information about the Earth and our environment.  ...  In this paper, we sketch the current opportunities and challenges related to the exploitation of multimodal data for Earth observation.  ...  For instance, hyperspectral and LiDAR data, and depth images at different locations are used in [78] to quantify physical features, such as land-cover properties and openness, to learn a human perception  ... 
doi:10.1109/jproc.2015.2462751 fatcat:cyaxiwfjqbdqzefhjgta5fivee

Front Matter: Volume 8390

Proceedings of SPIE, Sylvia S. Shen, Paul E. Lewis
2012 Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XVIII  
SPIEDigitalLibrary.org Paper Numbering: Proceedings of SPIE follow an e-First publication model, with papers published first online and then in print and on CD-ROM.  ...  Publication of record for individual papers is online in the SPIE Digital Library.  ...  Through a graphicallyrich web site for browsing and downloading all of the selected datasets, benchmarks, and tutorials, IMAGESEER provides a widely accessible database of NASA-centric, easy to read, image  ... 
doi:10.1117/12.979120 fatcat:xwhwxgmm35fpnl7yft37jlxkgy

Remote Sensing Image Scene Classification Meets Deep Learning: Challenges, Methods, Benchmarks, and Opportunities [article]

Gong Cheng, Xingxing Xie, Junwei Han, Lei Guo, Gui-Song Xia
2020 arXiv   pre-print
Considering the rapid evolution of this field, this paper provides a systematic survey of deep learning methods for remote sensing image scene classification by covering more than 140 papers.  ...  Remote sensing image scene classification, which aims at labeling remote sensing images with a set of semantic categories based on their contents, has broad applications in a range of fields.  ...  [2] surveyed multimodal remote sensing image classification and summarized the leading algorithms for this field.  ... 
arXiv:2005.01094v1 fatcat:qz3at3gyvrbtzkluumalvpqb64

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  
The competition was based on urban land use and land cover classification, aiming to distinguish between very diverse and detailed classes of urban objects, materials, and vegetation.  ...  Participants proposed elaborate approaches rooted in remote-sensing, and also in machine learning and computer vision, to make the most of the available data.  ...  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. Shahraki and S.  ... 
doi:10.1109/jstars.2019.2911113 fatcat:r5qtkkthfvf7dpde3adq6xsrh4
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