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Multi-Objective Task Scheduling for Energy-Efficient Cloud Implementation of Hyperspectral Image Classification

Jin Sun, Heng Li, Yi Zhang, Yang Xu, Yaoqin Zhu, Qitao Zang, Zebin Wu, Zhihui Wei
2020 IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing  
Aiming at hyperspectral image classification applications, this article proposes an energy-efficient cloud implementation by employing a multiobjective task scheduling algorithm.  ...  of the cloud system.  ...  [26] presented a cloud implementation of the popular K-means algorithm for hyperspectral image analysis.  ... 
doi:10.1109/jstars.2020.3036896 fatcat:lpopweco6zejthx6buix4fevce

A Distributed N-FINDR Cloud Computing-Based Solution for Endmembers Extraction on Large-Scale Hyperspectral Remote Sensing Data

Victor Andres Ayma Quirita, Gilson Alexandre Ostwald Pedro da Costa, César Beltrán
2022 Remote Sensing  
The implementation of the distributed algorithm was done by extending the InterCloud Data Mining Package, originally adopted for land cover classification, through the HyperCloud-RS framework, here adapted  ...  The experimental analysis addresses the performance issues, evaluating both accuracy and execution time, over the processing of different synthetic versions of the AVIRIS Cuprite hyperspectral dataset,  ...  Acknowledgments: The authors would like to thank the support of Artificial Intelligence Laboratory at Pontifical Catholic University of Peru.  ... 
doi:10.3390/rs14092153 fatcat:pqpf3p3ncvfypkq2adu2btiuhy

A Parallel Unmixing-Based Content Retrieval System for Distributed Hyperspectral Imagery Repository on Cloud Computing Platforms

Peng Zheng, Zebin Wu, Jin Sun, Yi Zhang, Yaoqin Zhu, Yuan Shen, Jiandong Yang, Zhihui Wei, Antonio Plaza
2021 Remote Sensing  
This paper proposes a novel parallel CBIR system for hyperspectral image (HSI) repository on cloud computing platforms under the guide of unmixed spectral information, i.e., endmembers and their associated  ...  As the volume of remotely sensed data grows significantly, content-based image retrieval (CBIR) becomes increasingly important, especially for cloud computing platforms that facilitate processing and storing  ...  We would also like to thank NASA, who provided Cuprite data for the experimental validation. Conflicts of Interest: The authors declare no conflict of interest.  ... 
doi:10.3390/rs13020176 fatcat:7b5xofj425dtxboklearf4ojym


C. Iseli, A. Lucieer
2019 The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences  
The introduction of the hyperspectral snapshot sensor provides interesting opportunities for acquisition of three-dimensional (3D) hyperspectral point clouds based on the structure-from-motion (SfM) workflow  ...  An SfM workflow was implemented to derive hyperspectral 3D point clouds and orthomosaics from overlapping frames.  ...  ACKNOWLEDGEMENTS We would like to acknowledge Nicolo Camaretta for access to comprehensive datasets relating to both the spatial distribution of species at the study site as well as leaf-level measured  ... 
doi:10.5194/isprs-archives-xlii-2-w13-379-2019 fatcat:7mxay7icfvf2dfkky3qptlf3sm

Sunglint Detection for Unmanned and Automated Platforms

Shungudzemwoyo Pascal Garaba, Jan Schulz, Marcel Robert Wernand, Oliver Zielinski
2012 Sensors  
To predict sunglint conspicuous in the simultaneously available sea surface images a sunglint image detection algorithm was developed and implemented.  ...  In parallel, a camera system was used to capture sea surface and sky images of the investigated points.  ...  Acknowledgments The authors extend their gratitude to the master and crew of R/V Heincke, and to R.  ... 
doi:10.3390/s120912545 fatcat:h7playzpc5apldithlrboj6kua

Compressive data fusion for multi-sensor image analysis

Saurabh Prasad, Hao Wu, James E. Fowler
2014 2014 IEEE International Conference on Image Processing (ICIP)  
Multiple views of a scene-obtained via different sensing modalities-have the potential to significantly enhance image analysis for remote sensing and other applications.  ...  A case study fusing experimental hyperspectral and LiDAR data demonstrates that statistical learning in the compressivemeasurement domain is not only feasible, but also provides a natural framework for  ...  This dataset contains a hyperspectral image and a LiDAR point cloud for this area.  ... 
doi:10.1109/icip.2014.7026019 dblp:conf/icip/PrasadWF14 fatcat:cu56vi3iqffq5fzwloz2iyfzge

Nonnegative Tensor CP Decomposition of Hyperspectral Data

Miguel A. Veganzones, Jeremy E. Cohen, Rodrigo Cabral Farias, Jocelyn Chanussot, Pierre Comon
2016 IEEE Transactions on Geoscience and Remote Sensing  
Isabelle Zin from the LTHE laboratory, CNRS, Prof. Marie Dumont from Meteo-France and Dr. Mauro Dalla Mura from GIPSA-lab, Grenoble INP, for providing the experimental dataset.  ...  Fig. 2 depicts the cloud ratio for each of the 44 images. Most of the images are partially cloudy, and the pixels covered by clouds are considered as missing data.  ...  The huge amount of hyperspectral data that will be delivered in the near future encouraged us to consider hyperspectral image analysis from a big data point of view.  ... 
doi:10.1109/tgrs.2015.2503737 fatcat:oaojnm72d5bhbdaxccb4uapwfu

ROI-Based On-Board Compression for Hyperspectral Remote Sensing Images on GPU

2017 Sensors  
In recent years, hyperspectral sensors for Earth remote sensing have become very popular. Such systems are able to provide the user with images having both spectral and spatial information.  ...  The algorithm aims at performing on-board compression using the target's related strategy.  ...  Author Contributions: R.G. performed part of the study, the software implementation and the preparation of the results; P.G. conceived the study, wrote the manuscript and designed the experiments.  ... 
doi:10.3390/s17051160 pmid:28534816 pmcid:PMC5470906 fatcat:pqgc4ajbzfbyxhommtq2wuqzam


P. L. Aguilar, A. Plaza, P. Martínez, R. M. Pérez
2002 Series in Machine Perception and Artificial Intelligence  
In this chapter we discuss the application of systolic arrays to speed up the performance of a new algorithm that performs unsupervised analysis of remote sensing images with high dimensionality (hyperspectral  ...  and complex shapes, but has rarely been applied to the classification/segmentation of hyperspectral images.  ...  Alejandro Curado, from our university Department of English, for his linguistic revision of this chapter.  ... 
doi:10.1142/9789812778086_0003 fatcat:5zjgcxorafb4zhpejcndu72elq

HyperMix: A new tool for quantitative evaluation of end member identification and spectral unmixing techniques

Luis-Ignacio Jimenez, Gabriel Martin, Antonio Plaza
2012 2012 IEEE International Geoscience and Remote Sensing Symposium  
The tool also includes a database of synthetic hyperspectral images (generated using fractals to simulate natural patterns) which can be used to evaluate the precision of the algorithms for endmember identification  ...  The proposed tool, called HyperMix, comprises several open source implementations of algorithms for endmember identification and spectral unmixing.  ...  These images are further divided into a number of clusters using the k-means algorithm [16] , where the number of clusters extracted from the five fractal images was always larger than the number of endmember  ... 
doi:10.1109/igarss.2012.6351276 dblp:conf/igarss/JimenezMP12 fatcat:22hojk7wznbrloqizuf7vrnfmu

2014 Index IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Vol. 7

2014 IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing  
., +, JSTARS Aug. 2014 3660-3667 Multi-GPU Implementation of the Minimum Volume Simplex Analysis Algorithm for Hyperspectral Unmixing.  ...  ., +, JSTARS Oct. 2014 4288-4300 Multi-GPU Implementation of the Minimum Volume Simplex Analysis Algorithm for Hyperspectral Unmixing.  ... 
doi:10.1109/jstars.2015.2397347 fatcat:ib3tjwsjsnd6ri6kkklq5ov37a


V. Ayma, C. Beltrán, P. N. Happ, G. A. O. P. Costa, R. Q. Feitosa
2019 The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences  
In the attempt to overcome the computational limitations related to Remote Sensing Big Data analysis, we implemented the K-Means and Expectation Maximization algorithms, as distributed clustering solutions  ...  , exploiting the capabilities of cloud computing infrastructure for processing very large datasets.  ...  To compute the thematic accuracy of both distributed implementations, K-Means and the EM algorithms, we used a scaled version at 40% of the dataset (original image and its binary mask).  ... 
doi:10.5194/isprs-archives-xlii-2-w16-29-2019 fatcat:hposfs7cprdvfnpmdlk7my3ewy

Panchromatic image processing using hyperspectral unmixing method

Zhenyu An, Zhenwei Shi, Jun Wu, Hongqiang Wang
2015 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS)  
After that, a hyperspectral unmixing algorithm, vertex component analysis, is then applied to extract endmembers that comprise the vertices of the data simplex.  ...  In the paper, we consider the probability of applying hyperspectral image (HSI) processing methods to panchromatic images (PIs), which is a novel yet crucial issue for further analyses.  ...  Then the same managements are implemented on the other pixels and the total number of vectors we obtained for the PI is L/2 × K/2.  ... 
doi:10.1109/igarss.2015.7326134 dblp:conf/igarss/AnSWW15 fatcat:lou72elzwfadxghod2akubvslm

Endmember extraction algorithms from hyperspectral images

M. C. Cantero, P. L. Aguilar, A. Plaza, R. M. Pérez, P. J. Martínez, J. Plaza
2006 Annals of Geophysics  
The identification of image endmembers is a crucial task in hyperspectral data exploitation.  ...  During the last years, several high-resolution sensors have been developed for hyperspectral remote sensing applications. Some of these sensors are already available on space-borne devices.  ...  Acknowledgements This work was supported by Regional Government Junta of Extremadura by means of the project 2PR03A061. The authors gratefully acknowledge Prof.  ... 
doi:10.4401/ag-3156 doaj:200221ff074e4872b65f629e32b116b9 fatcat:6xfzpf3phra7pje4mbcyhyu3k4

Small Drone Field Experiment: Data Collection & Processing [article]

Dalton Rosario, Christoph Borel, Damon Conover, Ryan McAlinden, Anthony Ortiz, Sarah Shiver, Blair Simon
2017 arXiv   pre-print
from images), and fusion of hyperspectral data with the recovered set of 3D point clouds representing the target area.  ...  The field experiment and associated data post processing approach to correct for reflectance, geo-rectify, recover the area's dense point clouds from images, register spectral with elevation properties  ...  The approach follows these major steps: (a) The fusion process starts by applying the K-means clustering algorithm to cluster all points in the 3D model's point clouds into k clusters.  ... 
arXiv:1711.10693v1 fatcat:isa7nxz6rverlbmkfoki5lv45u
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