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Deep learning for semantic segmentation of remote sensing images with rich spectral content [article]

A Hamida, A. Benoît, L Klein, C Amar, N. Audebert
2017 arXiv   pre-print
Therefore, this paper presents recent Deep Learning approaches for fine or coarse land cover semantic segmentation estimation.  ...  With the rapid development of Remote Sensing acquisition techniques, there is a need to scale and improve processing tools to cope with the observed increase of both data volume and richness.  ...  Some recent works already use deep neural nets to process remotely sensed images.  ... 
arXiv:1712.01600v1 fatcat:46njo5nqpvhkrh5rytkv7odede

Deep learning for semantic segmentation of remote sensing images with rich spectral content

A. Ben Hamida, A. Benoit, P. Lambert, L. Klein, C. Ben Amar, N. Audebert, S. Lefevre
2017 2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS)  
Therefore, this paper presents recent Deep Learning approaches for fine or coarse land cover semantic segmentation estimation.  ...  With the rapid development of Remote Sensing acquisition techniques, there is a need to scale and improve processing tools to cope with the observed increase of both data volume and richness.  ...  Some recent works already use deep neural nets to process remotely sensed images.  ... 
doi:10.1109/igarss.2017.8127520 dblp:conf/igarss/HamidaBLKAAL17 fatcat:52dxfdt4vba7pjfvi7572sr46y

A Survey of Semantic Construction and Application of Satellite Remote Sensing Images and Data

Hui Lu, Qi Liu, Xiaodong Liu, Yonghong Zhang
2021 Journal of Organizational and End User Computing  
With the rapid development of satellite technology, remote sensing data has entered the era of big data, and the intelligent processing of remote sensing image has been paid more and more attention.  ...  , for remote sensing big data, the technical progress in the study field of semantic construction is analyzed from four aspects: semantic description and understanding, semantic segmentation, semantic  ...  Building a deep learning network model to learn more high-level and rich scene semantic information of remote sensing images is the main research direction in the future.  ... 
doi:10.4018/joeuc.20211101.oa6 fatcat:mxuhgviitff3nldf2gzrfiog7e

Editorial for the Special Issue "Frontiers in Spectral Imaging and 3D Technologies for Geospatial Solutions"

Eija Honkavaara, Konstantinos Karantzalos, Xinlian Liang, Erica Nocerino, Ilkka Pölönen, Petri Rönnholm
2019 Remote Sensing  
This Special Issue hosts papers on the integrated use of spectral imaging and 3D technologies in remote sensing, including novel sensors, evolving machine learning technologies for data analysis, and the  ...  utilization of these technologies in a variety of geospatial applications.  ...  The Remote Sensing editorial team is gratefully acknowledged for its support during all phases of the endeavor to successfully complete this volume.  ... 
doi:10.3390/rs11141714 fatcat:jmyg2y523jfyhm5ykkc3jsqbpa

Remote sensing image description based on word embedding and end-to-end deep learning

Yuan Wang, Hongbing Ma, Kuerban Alifu, Yalong Lv
2021 Scientific Reports  
Second, a new multi-level end-to-end model is employed to further describe the content of remote sensing images and to better advance the description tasks for P. euphratica and Tamarix in remote sensing  ...  Experimental results reveal that the natural language sentences generated using this method can better describe P. euphratica and Tamarix in remote sensing images compared with conventional deep learning  ...  for representing the content of remote sensing images. 3.  ... 
doi:10.1038/s41598-021-82704-4 pmid:33542421 fatcat:u6r4bfrcxjgppptuuvnwvill44

Land Cover Mapping with Higher Order Graph-Based Co-Occurrence Model

Wenzhi Zhao, William Emery, Yanchen Bo, Jiage Chen
2018 Remote Sensing  
Deep learning has become a standard processing procedure in land cover mapping for remote sensing images.  ...  segments of the image.  ...  Acknowledgments: The authors would like to thank Philipp Krähenbühl for providing the valuable code of the conditional random forest (CRF), also the ISPRS community for providing Vaihingen dataset and  ... 
doi:10.3390/rs10111713 fatcat:6mfsa2co3nd5rhoxgstobdougu

Superpixel-Based Building Damage Detection from Post-earthquake Very High Resolution Imagery Using Deep Neural Networks [article]

Jun Wang, Zhoujing Li, Yixuan Qiao, Qiming Qin, Peng Gao, Guotong Xie
2021 arXiv   pre-print
However, little attention has been paid to exploiting rich features represented in VHR images using Deep Neural Networks (DNN).  ...  Remotely sensed very high spatial resolution (VHR) imagery can provide vital information due to their ability to map the affected buildings with high geometric precision.  ...  ACKNOWLEDGMENT This work was supported by the "National Science and Technology Major Project of China (11-Y20A05-9001-15/16)".  ... 
arXiv:2112.04744v3 fatcat:g6rqizzvmjbnlahj7syb3x6lvu

Editorial of Special Issue "Machine and Deep Learning for Earth Observation Data Analysis"

Vasileios Syrris, Sveinung Loekken
2021 Remote Sensing  
Earth observation and remote sensing technologies provide ample and comprehensive information regarding the dynamics and complexity of the Earth system [...]  ...  for providing high-quality evaluation reports and (iii) the editorial staff of Remote Sensing for their support in administering this project.  ...  Conflicts of Interest: The authors declare no conflict of interest.  ... 
doi:10.3390/rs13142758 fatcat:a62lqmosrbbzxbnxye7r2ciyra

Deep learning in remote sensing applications: A meta-analysis and review

Lei Ma, Yu Liu, Xueliang Zhang, Yuanxin Ye, Gaofei Yin, Brian Alan Johnson
2019 ISPRS journal of photogrammetry and remote sensing (Print)  
A B S T R A C T Deep learning (DL) algorithms have seen a massive rise in popularity for remote-sensing image analysis over the past few years.  ...  Subsequently, a detailed review is conducted to describe/discuss how DL has been applied for remote sensing image analysis tasks including image fusion, image registration, scene classification, object  ...  Sincere thanks to anonymous reviewers and members of the editorial team, for the comments and contributions.  ... 
doi:10.1016/j.isprsjprs.2019.04.015 fatcat:wheurssuyrdetfbzt3qex7lsba

Semi-Automated Semantic Segmentation of Arctic Shorelines Using Very High-Resolution Airborne Imagery, Spectral Indices and Weakly Supervised Machine Learning Approaches

Bibek Aryal, Stephen M. Escarzaga, Sergio A. Vargas Vargas Zesati, Miguel Velez-Reyes, Olac Fuentes, Craig Tweedie
2021 Remote Sensing  
data and compare it to the performance of traditional Machine Learning (ML) based approaches—namely, random forest, xgboost—and spectral water indices—Normalized Difference Water Index (NDWI), and Normalized  ...  In this work, we propose a modified variant of the Deep Neural Network based U-Net Architecture for the automated mapping of 4 Band Orthorectified NOAA Airborne Imagery using sparsely labeled training  ...  Methods The task of identifying and mapping geomorphological features in remote sensing images fits well within the framework of semantic segmentation.  ... 
doi:10.3390/rs13224572 fatcat:tye7pzdbebeftpaahjwi6j2onu

Performance Evaluation of Single-Label and Multi-Label Remote Sensing Image Retrieval Using a Dense Labeling Dataset

Zhenfeng Shao, Ke Yang, Weixun Zhou
2018 Remote Sensing  
However, most of the existing datasets are single-labeled, with each image in these datasets being annotated by a single label representing the most significant semantic content of the image.  ...  These results demonstrated the advantages of multiple labels over single labels for interpreting complex remote sensing images.  ...  Conflicts of Interest: The authors declare no conflict of interest.  ... 
doi:10.3390/rs10060964 fatcat:qdmujg5kl5g2hgdpunhnaxjpbm

$\mathrm{BAS}^4$Net: Boundary-Aware Semi-Supervised Semantic Segmentation Network for Very High Resolution Remote Sensing Images

Xian Sun, Aijun Shi, Hai Huang, Helmut Mayer
2020 IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing  
Semantic segmentation is a fundamental task in remote sensing image understanding.  ...  However, it is still challenging for Very High Resolution (VHR) remote sensing images.  ...  CONCLUSION In this work, we have proposed BAS 4 Net for the semantic segmentation of VHR remote sensing images.  ... 
doi:10.1109/jstars.2020.3021098 fatcat:unvmqs3he5fxzfxpjoxn44gjxi

Intelligent Object Recognition of Urban Water Bodies Based on Deep Learning for Multi-Source and Multi-Temporal High Spatial Resolution Remote Sensing Imagery

Shiran Song, Jianhua Liu, Yuan Liu, Guoqiang Feng, Hui Han, Yuan Yao, Mingyi Du
2020 Sensors  
In recent years, deep learning has been applied in the field of remote sensing measurements because of its powerful feature extraction ability.  ...  High spatial resolution remote sensing image (HSRRSI) data provide rich texture, geometric structure, and spatial distribution information for surface water bodies.  ...  Conflicts of Interest: The authors declare no conflict of interest.  ... 
doi:10.3390/s20020397 pmid:31936791 pmcid:PMC7014233 fatcat:37n5fijasjfzzbzd5djspbdr34

Corn Residue Covered Area Mapping with a Deep Learning Method Using Chinese GF-1 B/D High Resolution Remote Sensing Images

Wancheng Tao, Zixuan Xie, Ying Zhang, Jiayu Li, Fu Xuan, Jianxi Huang, Xuecao Li, Wei Su, Dongqin Yin
2021 Remote Sensing  
Mapping crop residue covered areas accurately using remote sensing images can monitor the protection of black soil in regional areas.  ...  Our developed and other deep semantic segmentation networks (MU-net, GU-net, MSCU-net, SegNet, and Dlv3+) improve the classification accuracy of IOUAVG/KappaAVG with 0.0091/0.0058, 0.0133/0.0091, 0.044  ...  Fortunately, deep learning semantic segmentation methods are developing rapidly in the field of computer vision and classification of remote sensing images [30, 31] .  ... 
doi:10.3390/rs13152903 fatcat:tge7behkkjavjbjuggn2paypn4

A Survey on Deep Learning-Driven Remote Sensing Image Scene Understanding: Scene Classification, Scene Retrieval and Scene-Guided Object Detection

Yating Gu, Yantian Wang, Yansheng Li
2019 Applied Sciences  
Similar to other domains (e.g., speech recognition and natural image recognition), deep learning has also become the state-of-the-art technique in RSISU.  ...  RSISU includes the following sub-tasks: remote sensing image scene classification, remote sensing image scene retrieval, and scene-driven remote sensing image object detection.  ...  Conflicts of Interest: The authors declare no conflict of interest.  ... 
doi:10.3390/app9102110 fatcat:oj3acgbmwnhzppxvvjbsn5cfzq
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