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An Object-Based Markov Random Field with Partition-Global Alternately Updated for Semantic Segmentation of High Spatial Resolution Remote Sensing Image

Hongtai Yao, Xianpei Wang, Le Zhao, Meng Tian, Zini Jian, Li Gong, Bowen Li
2021 Remote Sensing  
To solve this problem, this paper proposes an object-based Markov random field method with partition-global alternately updated (OMRF-PGAU).  ...  The Markov random field (MRF) method is widely used in remote sensing image semantic segmentation because of its excellent spatial (relationship description) ability.  ...  Acknowledgments: Tested aerial images are provided by Tiancan Mei of Wuhan University, China. Thank you very much. Conflicts of Interest: The authors declare no conflict of interest.  ... 
doi:10.3390/rs14010127 fatcat:kmgmch6korbgdhh4oh2slbwznq

SEMANTIC SEGMENTATION OF FOREST STANDS OF PURE SPECIES AS A GLOBAL OPTIMIZATION PROBLEM

C. Dechesne, C. Mallet, A. Le Bris, V. Gouet-Brunet
2017 ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences  
Both airborne lidar point clouds and multispectral very high spatial resolution images are integrated for that purpose.  ...  The global methods, based on an energy model, tend to be more efficient with an accuracy gain up to 15%. The segmentation results using such models have an accuracy ranging from 96% to 99%.  ...  It defines the task of partitioning an image into regions that delineate meaningful objects and labelling those regions with an object label.  ... 
doi:10.5194/isprs-annals-iv-1-w1-141-2017 fatcat:mdug5rv7lbbzppygvetaycstv4

Building Extraction from High Spatial Resolution Remote Sensing Images via Multiscale-Aware and Segmentation-Prior Conditional Random Fields

Qiqi Zhu, Zhen Li, Yanan Zhang, Qingfeng Guan
2020 Remote Sensing  
However, edge oversmoothing still exists when CRF is directly used to extract buildings from high spatial resolution (HSR) remote sensing images.  ...  Based on a computer vision multi-scale semantic segmentation network (D-LinkNet), a novel building extraction framework is proposed, named multiscale-aware and segmentation-prior conditional random fields  ...  Acknowledgments: The authors would like to thank the editor and the anonymous reviewers for their comments and suggestions. Conflicts of Interest: The authors declare no conflict of interest.  ... 
doi:10.3390/rs12233983 fatcat:jvnwhadcpbhd7okhwvyoidgudy

Remote sensing image segmentation by active queries

Devis Tuia, Jordi Muñoz-Marí, Gustavo Camps-Valls
2012 Pattern Recognition  
The proposed method is successfully evaluated in two challenging remote sensing scenarios: hyperspectral and very high spatial resolution (VHR) multispectral images segmentation.  ...  For remote sensing image segmentation, the selected samples are the most informative pixels in the image so that classifiers trained with reduced active datasets become faster and more robust.  ...  PBLAP2-127713 and PZ00P2-136827), and by the Spanish Ministry of Science and Innovation under projects TEC2009-13696, AYA2008-05965-C04-03, and CONSOLIDER/CSD 2007-00018.  ... 
doi:10.1016/j.patcog.2011.12.012 fatcat:odwc3jdtbjgdbmtiqa2wzevbia

Algorithms for semantic segmentation of multispectral remote sensing imagery using deep learning

Ronald Kemker, Carl Salvaggio, Christopher Kanan
2018 ISPRS journal of photogrammetry and remote sensing (Print)  
This dataset contains very-high resolution MSI collected by an unmanned aircraft system.  ...  In this paper, we adapt state-of-the-art DCNN frameworks in computer vision for semantic segmentation for MSI imagery.  ...  Michael Gartley with DIRSIG support.  ... 
doi:10.1016/j.isprsjprs.2018.04.014 fatcat:njpadtv6b5hqlpl72nyhlycrem

A review of EO image information mining [article]

Marco Quartulli, Igor G. Olaizola
2012 arXiv   pre-print
We analyze the state of the art of content-based retrieval in Earth observation image archives focusing on complete systems showing promise for operational implementation.  ...  The solutions envisaged for the issues related to feature simplification and synthesis, indexing, semantic labeling are reviewed. The methodologies for query specification and execution are analyzed.  ...  The system is specifically aimed at high resolution remote sensing image retrieval.  ... 
arXiv:1203.0747v2 fatcat:nwiylcsdrnhthi753xcxwxgo7e

A Review of Point Cloud Semantic Segmentation [article]

Yuxing Xie, Jiaojiao Tian, Xiao Xiang Zhu
2019 arXiv   pre-print
Firstly, we outline the acquisition and evolution of the 3D point cloud from the perspective of remote sensing and computer vision, as well as the published benchmarks for PCSS studies.  ...  3D Point Cloud Semantic Segmentation (PCSS) is attracting increasing interest, due to its applicability in remote sensing, computer vision and robotics, and due to the new possibilities offered by deep  ...  Markov Random Field (MRF) and Conditional Random Field (CRF) are machine learning approaches to solve graph-based segmentation problems.  ... 
arXiv:1908.08854v2 fatcat:kwpsogl4gbd3nji4xpcx32c4ta

Semantic segmentation of multisensor remote sensing imagery with deep ConvNets and higher-order conditional random fields

Yansong Liu, Sankaranarayanan Piramanayagam, Sildomar T. Monteiro, Eli Saber
2019 Journal of Applied Remote Sensing  
Semantic segmentation of multisensor remote sensing imagery with deep ConvNets and higher-order conditional random fields," Abstract.  ...  Specifically, we propose a decision-level multisensor fusion technique for semantic labeling of the very-high-resolution optical imagery and LiDAR data.  ...  Acknowledgments The authors would like to acknowledge the Department of Defense for its support of this research as well as the usage of the dataset provided by ISPRS and BSF Swissphoto, released in conjunction  ... 
doi:10.1117/1.jrs.13.016501 fatcat:743zuxiifze4xcbdslztrgixbm

Issues in managing image and video data

Shawn D. Newsam, Jelena Tesic, Lei Wang, B. S. Manjunath, Minerva M. Yeung, Rainer W. Lienhart, Chung-Sheng Li
2003 Storage and Retrieval Methods and Applications for Multimedia 2004  
This paper presents an overview of our recent work on managing image and video data. The first half of the paper describes a representation for the semantic spatial layout of video frames.  ...  In particular, Markov random fields are used to characterize the spatial arrangement of frame tiles that are labeled using support vector machine classifiers.  ...  Alternately, Markov random fields (MRFs) are one of the more successful techniques for modeling the spatial distribution of image data 2,3,4,5 .  ... 
doi:10.1117/12.538096 dblp:conf/spieSR/NewsamTWM04 fatcat:mzttazcgnzct3efxycqu4j4fju

Multimodal Fusion of Mobility Demand Data and Remote Sensing Imagery for Urban Land-Use and Land-Cover Mapping

Martina Pastorino, Federico Gallo, Angela Di Febbraro, Gabriele Moser, Nicola Sacco, Sebastiano B. Serpico
2022 Remote Sensing  
Two different methods are proposed within this framework, the first based on pixelwise probabilistic decision fusion and the second on the combination with a region-based multiscale Markov random field  ...  After a discussion on the function of mobility demand data, a probabilistic fusion framework is developed to take advantage of remote sensing and transport data, and their joint use for urban land-use  ...  Acknowledgments: The authors would like to thank Chiara Tacconi and Maria Pia Tuscano for their help with the implementation.  ... 
doi:10.3390/rs14143370 fatcat:oe4hnryvg5gdflp6zm3gc3kpd4

Graph-based semi-supervised classification on very high resolution remote sensing images

Sanjay Ranka, Ranga Raju Vatsavai, Anand Rangarajan, Manu Sethi, Yupeng Yan
2017 International Journal of Big Data Intelligence  
Classification of very high resolution (VHR) remote sensing imagery is a rapidly emerging discipline but faces several challenges owing to the huge scale of the pixel data involved, indiscernibility in  ...  Our results showcase several advantages in accuracy and efficiency, which coupled with the ease of model building and inherently parallelizable optimization make our framework a great choice for deployment  ...  The utility of both spectral and spatial information was also proven to be effective in [12, 26] with a kernel-based setting wherein SVM was used for classifying high resolution images.  ... 
doi:10.1504/ijbdi.2017.10002925 fatcat:lk75kucio5ejnggaysmckrdpym

Geospatial Correspondences for Multimodal Registration

Diego Marcos, Raffay Hamid, Devis Tuia
2016 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)  
We incorporate this representation in a Markov Random Field to simultaneously account for nonlinear mis-registrations and enforce locality priors to find matches between multi-sensor images.  ...  The growing availability of very high resolution (<1 m/pixel) satellite and aerial images has opened up unprecedented opportunities to monitor and analyze the evolution of land-cover and land-use across  ...  An object-based variant resides in the semantic tie points strategy proposed in [30] and used in [29] for remote sensing domain adaptation.  ... 
doi:10.1109/cvpr.2016.550 dblp:conf/cvpr/GonzalezHT16 fatcat:3qpbc6iezbgy3kbggthlhjf5xy

GeoAI for Large-Scale Image Analysis and Machine Vision: Recent Progress of Artificial Intelligence in Geography

Wenwen Li, Chia-Yu Hsu
2022 ISPRS International Journal of Geo-Information  
GeoAI, or geospatial artificial intelligence, has become a trending topic and the frontier for spatial analytics in Geography.  ...  in a variety of image analysis and machine vision tasks.  ...  Acknowledgments: The authors sincerely appreciate Yingjie Hu and Song Gao for comments on an earlier version of the manuscript. Conflicts of Interest: The authors declare no conflict of interest.  ... 
doi:10.3390/ijgi11070385 fatcat:yyzi46anyfcjrjuzcjfhbczo5y

Approximating shapes in images with low-complexity polygons

Muxingzi Li, Florent Lafarge, Renaud Marlet
2020 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)  
We present an algorithm for extracting and vectorizing objects in images with polygons.  ...  Departing from a polygonal partition that oversegments an image into convex cells, the algorithm refines the geometry of the partition while labeling its cells by a semantic class.  ...  We thank Jean-Philippe Bauchet for technical discussions. This work was partially supported by ANR-17-CE23-0003 project BIOM.  ... 
doi:10.1109/cvpr42600.2020.00866 dblp:conf/cvpr/LiLM20 fatcat:45e46nqefvhqhkye2gjde3bpjq

Conditional Random Field and Deep Feature Learning for Hyperspectral Image Classification

Fahim Irfan Alam, Jun Zhou, Alan Wee-Chung Liew, Xiuping Jia, Jocelyn Chanussot, Yongsheng Gao
2018 IEEE Transactions on Geoscience and Remote Sensing  
Image segmentation is considered to be one of the critical tasks in hyperspectral remote sensing image processing.  ...  The use of a graphical model such as a conditional random field (CRF) contributes further in capturing contextual information and thus improving the segmentation performance.  ...  The labeling was done with the help of high-resolution color images in Google Earth. E.  ... 
doi:10.1109/tgrs.2018.2867679 fatcat:6cyzgw7g7rfs7ertcsx72iwspa
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