Relevant bibliographies by topics / Remote-sensing images. Computer vision

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers

Academic literature on the topic 'Remote-sensing images. Computer vision'

Author: Grafiati
Published: 4 June 2021
Last updated: 16 February 2022

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Journal articles on the topic "Remote-sensing images. Computer vision"

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Zhu, Zhiqin, Yaqin Luo, Hongyan Wei, et al. "Atmospheric Light Estimation Based Remote Sensing Image Dehazing." Remote Sensing 13, no. 13 (2021): 2432. http://dx.doi.org/10.3390/rs13132432.

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Remote sensing images are widely used in object detection and tracking, military security, and other computer vision tasks. However, remote sensing images are often degraded by suspended aerosol in the air, especially under poor weather conditions, such as fog, haze, and mist. The quality of remote sensing images directly affect the normal operations of computer vision systems. As such, haze removal is a crucial and indispensable pre-processing step in remote sensing image processing. Additionally, most of the existing image dehazing methods are not applicable to all scenes, so the correspondi
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Bass, L. P., Yu A. Plastinin, and I. Yu Skryabysheva. "The machine training in problems of satellite images’s processing." Metrologiya, no. 4 (2020): 15–37. http://dx.doi.org/10.32446/0132-4713.2020-4-15-37.

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Use of the technical (computer) vision systems for Earth remote sensing is considered. An overview of software and hardware used in computer vision systems for processing satellite images is submitted. Algorithmic methods of the data processing with use of the trained neural network are described. Examples of the algorithmic processing of satellite images by means of artificial convolution neural networks are given. Ways of accuracy increase of satellite images recognition are defined. Practical applications of convolution neural networks onboard microsatellites for Earth remote sensing are pr
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Chen, Guobin, and Wei Dai. "A Method of Restoring Fuzzy Remote Sensing Image Based on Dark Pixel Prior." International Journal of Pattern Recognition and Artificial Intelligence 34, no. 08 (2019): 2054020. http://dx.doi.org/10.1142/s0218001420540208.

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Remote sensing image deblurring is a long-term and challenging inverse problem. Among them, the ability to find the correct image prior is the key to recovering high-quality and clear images. Therefore, in order to recover high-quality clear images, this paper has found a new and effective image prior: The dark pixel a priori in remote sensing images and a fuzzy remote sensing image restoration method based on dark pixel prior is proposed. Since the dark pixels in the clear remote sensing image will increase the pixel value of the dark pixels in the blurred remote sensing image due to the weig
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da Silva, C. C. V., K. Nogueira, H. N. Oliveira, and J. A. dos Santos. "TOWARDS OPEN-SET SEMANTIC SEGMENTATION OF AERIAL IMAGES." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences IV-3/W2-2020 (October 29, 2020): 19–24. http://dx.doi.org/10.5194/isprs-annals-iv-3-w2-2020-19-2020.

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Abstract. Classical and more recently deep computer vision methods are optimized for visible spectrum images, commonly encoded in grayscale or RGB colorspaces acquired from smartphones or cameras. A more uncommon source of images exploited in the remote sensing field are satellite and aerial images. However the development of pattern recognition approaches for these data is relatively recent, mainly due to the limited availability of this type of images, as until recently they were used exclusively for military purposes. Access to aerial imagery, including spectral information, has been increa
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Zhang, Yong Mei, and Li Ma. "Target Fusion Algorithm for Remote Sensing Image Recognition." Applied Mechanics and Materials 128-129 (October 2011): 1075–78. http://dx.doi.org/10.4028/www.scientific.net/amm.128-129.1075.

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Multisource image fusion technology is widely applied in a variety of fields such as remote sensing, computer vision, medical diagnosis and military surveillance. In most instances, multi-spectral and panchromatic images can provide more complementary information for feature extraction. Two kinds of images are used for target recognition. Algorithms based on gradient for image fusion only consider high-frequency information changes of the images, and neglect the richness of high-frequency information. To solve this problem, a new kind of self-adaptive rule and algorithm based on gradient and e
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Zhou, Liming, Chang Zheng, Haoxin Yan, et al. "Vehicle Detection in Remote Sensing Image Based on Machine Vision." Computational Intelligence and Neuroscience 2021 (August 9, 2021): 1–12. http://dx.doi.org/10.1155/2021/8683226.

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Target detection in remote sensing images is very challenging research. Followed by the recent development of deep learning, the target detection algorithm has obtained large and fast growth. However, in the application of remote sensing images, due to the small target, wide range, small texture, and complex background, the existing target detection methods cannot achieve people’s hope. In this paper, a target detection algorithm named IR-PANet for remote sensing images of an automobile is proposed. In the backbone network CSPDarknet53, SPP is used to strengthen the learning content. Then, IR-
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Wang, Chisheng, Junzhuo Ke, Wenqun Xiu, Kai Ye, and Qingquan Li. "Emergency Response Using Volunteered Passenger Aircraft Remote Sensing Data: A Case Study on Flood Damage Mapping." Sensors 19, no. 19 (2019): 4163. http://dx.doi.org/10.3390/s19194163.

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Current satellite remote sensing data still have some inevitable defects, such as a low observing frequency, high cost and dense cloud cover, which limit the rapid response to ground changes and many potential applications. However, passenger aircraft may be an alternative remote sensing platform in emergency response due to the high revisit rate, dense coverage and low cost. This paper introduces a volunteered passenger aircraft remote sensing method (VPARS) for emergency response. It uses the images captured by the passenger volunteers during flight. Based on computer vision algorithms and g
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Schmitt, M., and Y. L. Wu. "REMOTE SENSING IMAGE CLASSIFICATION WITH THE SEN12MS DATASET." ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences V-2-2021 (June 17, 2021): 101–6. http://dx.doi.org/10.5194/isprs-annals-v-2-2021-101-2021.

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Abstract. Image classification is one of the main drivers of the rapid developments in deep learning with convolutional neural networks for computer vision. So is the analogous task of scene classification in remote sensing. However, in contrast to the computer vision community that has long been using well-established, large-scale standard datasets to train and benchmark high-capacity models, the remote sensing community still largely relies on relatively small and often application-dependend datasets, thus lacking comparability. With this paper, we present a classification-oriented conversio
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Dong, Yunyun, Weili Jiao, Tengfei Long, et al. "Local Deep Descriptor for Remote Sensing Image Feature Matching." Remote Sensing 11, no. 4 (2019): 430. http://dx.doi.org/10.3390/rs11040430.

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Feature matching via local descriptors is one of the most fundamental problems in many computer vision tasks, as well as in the remote sensing image processing community. For example, in terms of remote sensing image registration based on the feature, feature matching is a vital process to determine the quality of transform model. While in the process of feature matching, the quality of feature descriptor determines the matching result directly. At present, the most commonly used descriptor is hand-crafted by the designer’s expertise or intuition. However, it is hard to cover all the different
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Feng, R., X. Li, and H. Shen. "MOUNTAINOUS REMOTE SENSING IMAGES REGISTRATION BASED ON IMPROVED OPTICAL FLOW ESTIMATION." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences IV-2/W5 (May 29, 2019): 479–84. http://dx.doi.org/10.5194/isprs-annals-iv-2-w5-479-2019.

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<p><strong>Abstract.</strong> Mountainous remote sensing images registration is more complicated than in other areas as geometric distortion caused by topographic relief, which could not be precisely achieved via constructing local mapping functions in the feature-based framework. Optical flow algorithm estimating motion of consecutive frames in computer vision pixel by pixel is introduced for mountainous remote sensing images registration. However, it is sensitive to land cover changes that are inevitable for remote sensing image, resulting in incorrect displacement. To addr
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Dissertations / Theses on the topic "Remote-sensing images. Computer vision"

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Ünsalan, Cem. "Multispectral satellite image understanding." Columbus, Ohio : Ohio State University, 2003. http://rave.ohiolink.edu/etdc/view?acc%5num=osu1061903845.

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Thesis (Ph. D.)--Ohio State University, 2003.<br>Title from first page of PDF file. Document formatted into pages; contains xix, 235 p. : ill. (some col.). Advisor: Kim L. Boyer, Department of Electrical Engineering. Includes bibliographical references (p. 216-235).
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Lao, Yin, and 劉然. "Image matching of running vehicles." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2004. http://hub.hku.hk/bib/B30278806.

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Hultberg, Johanna. "Dehazing of Satellite Images." Thesis, Linköpings universitet, Datorseende, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-148044.

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The aim of this work is to find a method for removing haze from satellite imagery. This is done by taking two algorithms developed for images taken from the sur- face of the earth and adapting them for satellite images. The two algorithms are Single Image Haze Removal Using Dark Channel Prior by He et al. and Color Im- age Dehazing Using the Near-Infrared by Schaul et al. Both algorithms, altered to fit satellite images, plus the combination are applied on four sets of satellite images. The results are compared with each other and the unaltered images. The evaluation is both qualitative, i.e.
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Workman, Scott. "Leveraging Overhead Imagery for Localization, Mapping, and Understanding." UKnowledge, 2018. https://uknowledge.uky.edu/cs_etds/64.

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Ground-level and overhead images provide complementary viewpoints of the world. This thesis proposes methods which leverage dense overhead imagery, in addition to sparsely distributed ground-level imagery, to advance traditional computer vision problems, such as ground-level image localization and fine-grained urban mapping. Our work focuses on three primary research areas: learning a joint feature representation between ground-level and overhead imagery to enable direct comparison for the task of image geolocalization, incorporating unlabeled overhead images by inferring labels from nearby gr
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Lloyd, Timothy Brian. "Surface extraction from coordinate measurement data to facilitate dimensional inspection." Thesis, Georgia Institute of Technology, 1999. http://hdl.handle.net/1853/15815.

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Forssén, Per-Erik. "Detection of Man-made Objects in Satellite Images." Thesis, Linköping University, Linköping University, Computer Vision, 1997. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-54356.

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<p>In this report, the principles of man-made object detection in satellite images is investigated. An overview of terminology and of how the detection problem is usually solved today is given. A three level system to solve the detection problem is proposed. The main branches of this system handle road, and city detection respectively. To achieve data source flexibility, the Logical Sensor notion is used to model the low level system components. Three Logical Sensors have been implemented and tested on Landsat TM and SPOT XS scenes. These are: BDT (Background Discriminant Transformation) to co
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De, Franchis Carlo. "Earth Observation and Stereo Vision." Thesis, Université Paris-Saclay (ComUE), 2015. http://www.theses.fr/2015SACLN002/document.

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Cette thèse étudie les problèmes posés par l’estimation automatique de modèles numériques d’élévation de la surface terrestre à partir de photographies prises par des satellites. Ce travail a bénéficié d’une collaboration avec le CNES (Centre National d’Etudes Spatiales) sur le développement d’outils de vision stéréoscopique pour Pléiades, le premier satellite d’observation de la Terre capable de produire des paires ou triplets d’images quasi-simultanées. Le premier chapitre de la thèse décrit un modèle simplifié de caméra pushbroom destiné aux satellites d’observation de la Terre, et aborde l
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Bradley, Justin Mathew. "Particle Filter Based Mosaicking for Forest Fire Tracking." Diss., CLICK HERE for online access, 2007. http://contentdm.lib.byu.edu/ETD/image/etd2001.pdf.

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Romero, Adriana. "Assisting the training of deep neural networks with applications to computer vision." Doctoral thesis, Universitat de Barcelona, 2015. http://hdl.handle.net/10803/316577.

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Deep learning has recently been enjoying an increasing popularity due to its success in solving challenging tasks. In particular, deep learning has proven to be effective in a large variety of computer vision tasks, such as image classification, object recognition and image parsing. Contrary to previous research, which required engineered feature representations, designed by experts, in order to succeed, deep learning attempts to learn representation hierarchies automatically from data. More recently, the trend has been to go deeper with representation hierarchies. Learning (very) deep represe
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Kurtz, Camille. "Une approche collaborative segmentation - classification pour l'analyse descendante d'images multirésolutions." Phd thesis, Université de Strasbourg, 2012. http://tel.archives-ouvertes.fr/tel-00735217.

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Depuis la fin des années 1990, les images optiques à très hautes résolutions spatiales issues de capteurs satellitaires sont de plus en plus accessibles par une vaste communauté d'utilisateurs. En particulier, différents systèmes satellitaires sont maintenant disponibles et produisent une quantité de données importante, utilisable pour l'observation de la Terre. En raison de cet important volume de données,les méthodes analytiques manuelles deviennent inadaptées pour un traitement efficace de ces données. Il devient donc crucial d'automatiser ces méthodes par des procédés informatiques, capabl
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Books on the topic "Remote-sensing images. Computer vision"

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Cem, Ünsalan, and SpringerLink (Online service), eds. Two-Dimensional Change Detection Methods: Remote Sensing Applications. Springer London, 2012.

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Richards, John A. Remote Sensing Digital Image Analysis: An Introduction. 5th ed. Springer Berlin Heidelberg, 2013.

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Lasaponara, Rosa. Satellite Remote Sensing: A New Tool for Archaeology. Springer Netherlands, 2012.

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Theory of remote image formation. Cambridge University Press, 2004.

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Thomas, Blaschke, Gärtner Georg, Hay, Geoffrey J. (Geoffrey Joseph), 1966-, et al., eds. Object-Based Image Analysis: Spatial Concepts for Knowledge-Driven Remote Sensing Applications. Springer-Verlag Berlin Heidelberg, 2008.

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Kanellopoulos, Ioannis. Machine Vision and Advanced Image Processing in Remote Sensing: Proceedings of Concerted Action MAVIRIC (Machine Vision in Remotely Sensed Image Comprehension). Springer Berlin Heidelberg, 1999.

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Pouliot, Jacynthe. Progress and New Trends in 3D Geoinformation Sciences. Springer Berlin Heidelberg, 2013.

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(1997), IEEE Aerospace Conference. 1997 IEEE Aerospace Conference proceedings. IEEE Service Center, 1997.

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IEEE Aerospace Conference (1999 Snowmass at Aspen, Colo.). 1999 IEEE Aerospace Conference proceedings. Institute of Electrical and Electronics Engineers, 1999.

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IEEE Aerospace Conference (2001 Big Sky, Mont.). 2001 IEEE Aerospace Conference: Proceedings : March 10-17, 2001, Big Sky, Montana. IEEE, 2001.

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Book chapters on the topic "Remote-sensing images. Computer vision"

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Sawant, Neela, Sharat Chandran, and B. Krishna Mohan. "Retrieving Images for Remote Sensing Applications." In Computer Vision, Graphics and Image Processing. Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11949619_76.

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Lu, Zeshan, Kun Liu, Yongwei Zhang, et al. "Building Detection via Complementary Convolutional Features of Remote Sensing Images." In Pattern Recognition and Computer Vision. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-60633-6_53.

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Lei, Jiahui, Chongjun Gao, Jing Hu, Changxin Gao, and Nong Sang. "Orientation Adaptive YOLOv3 for Object Detection in Remote Sensing Images." In Pattern Recognition and Computer Vision. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-31654-9_50.

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Yang, Shuojin, Liang Tian, Bingyin Zhou, et al. "Inception Parallel Attention Network for Small Object Detection in Remote Sensing Images." In Pattern Recognition and Computer Vision. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-60633-6_39.

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Ma, Xiaofeng, Wenyuan Li, and Zhenwei Shi. "Attention-Based Convolutional Networks for Ship Detection in High-Resolution Remote Sensing Images." In Pattern Recognition and Computer Vision. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-03341-5_31.

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Azimi, Seyed Majid, Eleonora Vig, Reza Bahmanyar, Marco Körner, and Peter Reinartz. "Towards Multi-class Object Detection in Unconstrained Remote Sensing Imagery." In Computer Vision – ACCV 2018. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-20893-6_10.

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Uma Shankar, B., Saroj K. Meher, Ashish Ghosh, and Lorenzo Bruzzone. "Remote Sensing Image Classification: A Neuro-fuzzy MCS Approach." In Computer Vision, Graphics and Image Processing. Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11949619_12.

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Kallel, Manel, Mohamed Naouai, and Yosr Slama. "New Metrics to Evaluate Pattern Recognition in Remote Sensing Images." In Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-33275-3_82.

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Bouteldja, Samia, and Assia Kourgli. "An Efficient CBIR System for High Resolution Remote Sensing Images." In Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-13469-3_46.

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Li, Yinhao, Yutaro Iwamoto, Lanfen Lin, and Yen-Wei Chen. "Parallel-Connected Residual Channel Attention Network for Remote Sensing Image Super-Resolution." In Computer Vision – ACCV 2020 Workshops. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-69756-3_2.

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Conference papers on the topic "Remote-sensing images. Computer vision"

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Ouyang, Chao, Feng Zhang, Zhong Chen, and Yifei Zhang. "Airplane detection in remote sensing images using convolutional neural networks." In Pattern Recognition and Computer Vision, edited by Zhiguo Cao, Yuehuang Wang, and Chao Cai. SPIE, 2018. http://dx.doi.org/10.1117/12.2285776.

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Ge, Wenying, and Guoying Liu. "Unsupervised classification of high-resolution remote-sensing images under edge constraints." In Pattern Recognition and Computer Vision, edited by Zhiguo Cao, Yuehuang Wang, and Chao Cai. SPIE, 2018. http://dx.doi.org/10.1117/12.2285777.

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Tian, Jinwen, Daimeng Zhang, Qingyun Tang, Jun Zhang, and Xiaomao Liu. "Method of segmenting river from remote sensing image." In Pattern Recognition and Computer Vision, edited by Zhiguo Cao, Yuehuang Wang, and Chao Cai. SPIE, 2018. http://dx.doi.org/10.1117/12.2283228.

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Jiang, Zhongze, Zhong Chen, Kaixiang Ji, and Jian Yang. "Semantic segmentation network combined with edge detection for building extraction in remote sensing images." In MIPPR 2019: Pattern Recognition and Computer Vision, edited by Zhenbing Liu, Jayaram K. Udupa, Nong Sang, and Yuehuan Wang. SPIE, 2020. http://dx.doi.org/10.1117/12.2538019.

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Zhang, Chunsen, and Yingwei Ge. "Semantic segmentation of buildings in high resolution remote sensing images using conditional random fields." In MIPPR 2019: Pattern Recognition and Computer Vision, edited by Zhenbing Liu, Jayaram K. Udupa, Nong Sang, and Yuehuan Wang. SPIE, 2020. http://dx.doi.org/10.1117/12.2539313.

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Li, Shiming, Qingwang Liu, Zengyuan Li, Erxue Chen, and Jianbing Zhang. "Building height extraction from overlapping airborne images in urban environment using computer vision approach." In 2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS). IEEE, 2017. http://dx.doi.org/10.1109/igarss.2017.8128318.

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Chen, Min, Qing Zhu, Jun Zhu, Zhu Xu, and Duoxiang Cheng. "Improved phase congruency based interest point detection for multispectral remote sensing images." In 2015 ISPRS International Conference on Computer Vision in Remote Sensing, edited by Cheng Wang, Rongrong Ji, and Chenglu Wen. SPIE, 2016. http://dx.doi.org/10.1117/12.2234947.

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Aixia Li, Zequn Guan, and Haiyan Guan. "Multi-overlapped based global registration of UAV images." In 2012 International Conference on Computer Vision in Remote Sensing (CVRS). IEEE, 2012. http://dx.doi.org/10.1109/cvrs.2012.6421242.

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Xiao, Jie, and Kaixia Lu. "Automated railroad reconstruction from remote sensing image based on texture filter." In Pattern Recognition and Computer Vision, edited by Zhiguo Cao, Yuehuang Wang, and Chao Cai. SPIE, 2018. http://dx.doi.org/10.1117/12.2285197.

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Zou, Lamei, Changfeng Li, Weidong Yang, Shiyang Zhou, and Shiwei Nie. "Remote sensing image ship detection based on feature pyramid." In MIPPR 2019: Pattern Recognition and Computer Vision, edited by Zhenbing Liu, Jayaram K. Udupa, Nong Sang, and Yuehuan Wang. SPIE, 2020. http://dx.doi.org/10.1117/12.2539136.

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