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A Probabilistic Feature Fusion for Building Detection in Satellite Images

Dimitrios Konstantinidis, Tania Stathaki, Vasileios Argyriou, Nikos Grammalidis
2015 Proceedings of the 10th International Conference on Computer Vision Theory and Applications  
We will demonstrate that by taking advantage of the multi-spectral nature of a satellite image and by employing a probabilistic fusion of the aforementioned features, we manage to create a novel methodology  ...  Building segmentation from 2D images can be a very challenging task due to the variety of objects that appear in an urban environment.  ...  Beril Sirmacek for providing her code for our evaluation results.  ... 
doi:10.5220/0005260502050212 dblp:conf/visapp/KonstantinidisS15 fatcat:qclixfvnfvehpivgl4ywuyn2di

A Probabilistic Framework to Detect Buildings in Aerial and Satellite Images

Beril Sirmacek, Cem Unsalan
2011 IEEE Transactions on Geoscience and Remote Sensing  
To overcome these difficulties, we propose a novel building detection method using local feature vectors and a probabilistic framework.  ...  Extensive tests indicate that our method can be used to automatically detect buildings in a robust and fast manner in Ikonos satellite and our aerial images.  ...  ACKNOWLEDGMENT The authors would like thank the anonymous reviewers for their valuable comments and corrections.  ... 
doi:10.1109/tgrs.2010.2053713 fatcat:vi573kyi4betphj42kt52jgyui

Updation of Cartographical Database with the Aid of Different Traits in Blending of ANN and ANFIS

Priti Tyagi, Udhav Bhosle
2012 International Journal of Applied Information Systems  
In this work, the high resolution satellite images are utilized to identify the updated buildings.  ...  Nowadays, a wide number of buildings are updated often; this can be updated in the cartographical database which is comprised of remote sensing images of buildings.  ...  [2] have proposed a building detection method. This method was developed using local feature vectors and a probabilistic framework.  ... 
doi:10.5120/ijais12-450640 fatcat:clhys36ylrforofgukh3rwbiey

Automated Detection of Buildings from Heterogeneous VHR Satellite Images for Rapid Response to Natural Disasters

Shaodan Li, Hong Tang, Xin Huang, Ting Mao, Xiaonan Niu
2017 Remote Sensing  
In this paper, we present a novel approach for automatically detecting buildings from multiple heterogeneous and uncalibrated very high-resolution (VHR) satellite images for a rapid response to natural  ...  Finally, buildings are automatically detected in a hierarchical probabilistic model by fusing the MBI and masked PAN images.  ...  Feature Fusion in a Probabilistic Framework Similar to the gCRF in [25] , the proposed gCRF_MBI method is also based on a probabilistic framework for feature fusion.  ... 
doi:10.3390/rs9111177 fatcat:45el4m5oinghfej2dnthlh5uw4

A Gabor Filter-Based Protocol for Automated Image-Based Building Detection

Hafiz Suliman Munawar, Riya Aggarwal, Zakria Qadir, Sara Imran Khan, Abbas Z. Kouzani, M. A. Parvez Mahmud
2021 Buildings  
To resolve this situation, a novel probabilistic method has been suggested using local features and probabilistic approaches.  ...  The density of building locations in the image was extracted.  ...  The probabilistic building detection along with decision fusion was applied on the selected regions and results were depicted with grayscale images in Figure 8 .  ... 
doi:10.3390/buildings11070302 fatcat:i7jhnvinfvaojkfamsww3xmk4e

Deep Learning for Remote Sensing Image Understanding

Liangpei Zhang, Gui-Song Xia, Tianfu Wu, Liang Lin, Xue Cheng Tai
2016 Journal of Sensors  
The papers in this issue can be roughly organized into three main categories: (a) remote sensing imagery classification, (b) change detection of multitemporal remote sensing images, and (c) fusion of diverse  ...  satellite image classification.  ...  We appreciate all the authors for their submissions, as well as all the reviewers for their careful and professional review.  ... 
doi:10.1155/2016/7954154 fatcat:yoyzkgdi25er5hqggp4qub7plu

Model Fusion-Based Batch Learning with Application to Oil Spills Detection [chapter]

Chunsheng Yang, Yubin Yang, Jie Liu
2012 Lecture Notes in Computer Science  
We applied the developed batch learning techniques to detect oil spills using radar images collected from satellite stations.  ...  We believe that such a batch structure is also an opportunity that can be exploited in the learning process. In this short paper, we investigated the novel method for dealing with the batched data.  ...  Holte for providing data from the oil spills detection and allow us to explore it more deeply. We also like to thank Dr.  ... 
doi:10.1007/978-3-642-31087-4_5 fatcat:3bfqvssvmrg6nb6mntq6r6xrwy

Information fusion for rural land-use classification with high-resolution satellite imagery

Wanxiao Sun, V. Heidt, Peng Gong, Gang Xu
2003 IEEE Transactions on Geoscience and Remote Sensing  
We propose an information fusion method for the extraction of land-use information based on both the panchromatic and multispectral Indian Remote Sensing Satellite 1C (IRS-1C) satellite imagery.  ...  An edge map was generated using operations such as edge detection, edge thresholding and edge thinning. Finally, a modified region-growing approach was used to improve image classification.  ...  ACKNOWLEDGMENT The authors are very thankful to the Ministry of Environment, the State of Rheinland-Pfalze, Germany and the German Science Foundations for their financial support on satellite imagery and  ... 
doi:10.1109/tgrs.2003.810707 fatcat:ofhdzueglbgnzpf2uq2oxkjp5m

Advances in Multi-Sensor Data Fusion: Algorithms and Applications

Jiang Dong, Dafang Zhuang, Yaohuan Huang, Jingying Fu
2009 Sensors  
The paper presents an overview of recent advances in multi-sensor satellite image fusion.  ...  In image-based application fields, image fusion has emerged as a promising research area since the end of the last century.  ...  The authors would also like to thank the two anonymous reviewers for their helpful comments and suggestions.  ... 
doi:10.3390/s91007771 pmid:22408479 pmcid:PMC3292082 fatcat:ucoiobpr55bw7d3qnub2cnd6sa

Spatiotemporal Fusion in Remote Sensing [chapter]

Hessah Albanwan, Rongjun Qin
2020 Recent Advances in Image Restoration with Applications to Real World Problems  
With the increasing development of satellites, nowadays Terabytes of remote sensing images can be acquired every day.  ...  The fusion integrates data from various sources acquired asynchronously for information extraction, analysis, and quality improvement.  ...  the benchmark satellite images available.  ... 
doi:10.5772/intechopen.93111 fatcat:gbpctokirzhtje23fwhqg5otiq

Aggregated Primary Detectors For Generic Change Detection In Satellite Images

Vincent Vidal, Matthieu Limbert, Tugdual Ceillier, Lionel Moisan
2019 IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium  
Detecting changes between two satellite images of the same scene generally requires an accurate (and thus often uneasy to obtain) model discriminating relevant changes from irrelevant ones.  ...  We here present a generic method, based on the definition of four different a-contrario detection models (associated to arbitrary features), whose aggregation is then trained from specific examples with  ...  Morel, From gestalt theory to image analysis: a probabilistic approach, vol. 34, Springer, 2007. Fig. 3 . 3 First row: Weight of each of the 35 primary detectors in the fusion model.  ... 
doi:10.1109/igarss.2019.8898833 dblp:conf/igarss/VidalLCM19 fatcat:xwtpqo2ne5c3pbm7t5gh6razki

Autonomous Building Detection Using Edge Properties and Image Color Invariants

Ali Ghandour, Abedelkarim Jezzini
2018 Buildings  
In this paper we present an approach for building detection using multiple cues.  ...  Automated building extraction from high-resolution satellite imagery is a challenging research problem, and several issues remain with respect to the variety of variables to be accounted for.  ...  Acknowledgments: This project has been funded with support from the National Council for Scientific Research in Lebanon. Conflicts of Interest: The authors declare no conflict of interest.  ... 
doi:10.3390/buildings8050065 fatcat:pvv5p64uv5e4zfwdr3qwlnlk34

Multisensor robust localization in various environment with correlation checking test

Nozomu Ohashi, Yuki Funabora, Shinji Doki, Kae Doki
2021 ROBOMECH Journal  
In this research, we have proposed a sensor fusion system with a relative correlation checking test to realize robust localization.  ...  Pieces of erroneous position information, biased against others and having a negative correlation with others, are detected and excluded in our proposed system by checking their correlation between all  ...  Their system computes the depth of detected features in images using LiDAR points projected into images.  ... 
doi:10.1186/s40648-021-00190-9 fatcat:wubawte26fg2rcoue4e7sjayvy


J. Mu, S. Cui, P. Reinartz
2017 The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences  
In this paper a method for building detection in aerial images based on variational inference of logistic regression is proposed. It consists of three steps.  ...  In order to characterize the appearances of buildings in aerial images, an effective bag-of-Words (BoW) method is applied for feature extraction in the first step.  ...  A novel decision fusion approach to building detection in VHR optical satellite images is proposed in (Senaras et al., 2013) .  ... 
doi:10.5194/isprs-archives-xlii-1-w1-159-2017 fatcat:hiphgigvcrayziem3g6pwbwxpy

Cloud detection methodologies: variants and development—a review

Seema Mahajan, Bhavin Fataniya
2019 Complex & Intelligent Systems  
The concept of classification to build a decision tree for cloud detection on the snow cover area is used in [7] .  ...  Authors [5] proposed deep learning for cloud detection using multilevel image features of satellite imagery.  ...  Researchers [47] proposed a cloud detection method for satellite images with high resolution using ground objects multi-features, such as color, texture, and shape.  ... 
doi:10.1007/s40747-019-00128-0 fatcat:ftol5w36vzdwzpuqeijsz2dct4
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