Results 271 to 280 of about 257,647 (295)

Contextual and Hierarchical Classification of Satellite Images Based on Cellular Automata

IEEE Transactions on Geoscience and Remote Sensing, 2015
Satellite image classification is an important technique used in remote sensing for the computerized analysis and pattern recognition of satellite data, which facilitates the automated interpretation of a large amount of information. Today, there exist many types of classification algorithms, such as parallelepiped and minimum distance classifiers, but
Moisés Espínola, Jose A. Piedra-Fernández, Rosa Ayala, Luis Iribarne, James Z. Wang 0001   +4 more
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Two-Stream Contextualized CNN for Fine-Grained Image Classification

Proceedings of the AAAI Conference on Artificial Intelligence, 2016
Human's cognition system prompts that context information provides potentially powerful clue while recognizing objects. However, for fine-grained image classification, the contribution of context may vary over different images, and sometimes the context even confuses the classification result.
Jiang Liu 0011, Chenqiang Gao, Deyu Meng, Wangmeng Zuo   +3 more
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Image segmentation by contextual region growing based on fuzzy classification

2016 2nd International Conference on Advanced Technologies for Signal and Image Processing (ATSIP), 2016
This paper presents a semantic image segmentation approach that combines a fuzzy region classification and a contextual region-growing. First image is over-segmented and a domain knowledge based fuzzy classification is applied on obtained regions to provide a fuzzy semantic labeling. This allows the proposed approach to operate at high level instead of
Mahaman Sani Chaibou, Karim Kalti, Basel Solaiman, Mohamed Ali Mahjoub   +3 more
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Spatio-temporal contextual image classification based on spatial adaboost

Proceedings. 2005 IEEE International Geoscience and Remote Sensing Symposium, 2005. IGARSS '05., 2005
Spatial AdaBoost proposed by Nishii and Eguchi (TGRS 2005) is a contextual supervised classifier of land-cover categories of geostatistical data. It shows an excellent performance similar to that of the MRF-based classifier with much less computational cost. In this paper, we extend the method to the setup with multi spatio-temporal images.
Ryuei Nishii, Shinto Eguchi
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Sparse representation using contextual information for hyperspectral image classification

2013 IEEE International Conference on Cybernetics (CYBCO), 2013
This paper analyzes the classification of hyperspectral images with the sparse representation algorithm in the presence of a minimal reconstruction error. Incorporating the contextual information into the sparse recovery process can improve the classification performance. However, previous sparse algorithms using contextual information only assume that
Haoliang Yuan, Yang Lu 0009, Lina Yang, Huiwu Luo, Yuan Yan Tang   +4 more
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A one-class classification by spatial-contextual for remotely sensed image

2013 IEEE International Geoscience and Remote Sensing Symposium - IGARSS, 2013
Hyperspectral remote sensing is a technique based on the spectroscopy, which contains abundant spectral information besides the spatial information of the images, and overcomes the limitations of the wide-band remote sensing detection. When classifying hyperspectral and multispectral images with the existing algorithms, we use only the spectral ...
Xiaofei Wang, Shuang Wu, Ye Zhang 0008, Aihua Wang, Chuanlong Hou   +4 more
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Partially supervised contextual classification of multitemporal remotely sensed images

IGARSS 2003. 2003 IEEE International Geoscience and Remote Sensing Symposium. Proceedings (IEEE Cat. No.03CH37477), 2004
A key-problem in dealing with multitemporal images of a given geographical area is the identification of the changes occurring between distinct acquisition dates. A complete map of the change typologies can be generated when training data are available for all observation dates, but this completely supervised context involves expensive requirements. On
DE MARTINO, MICHAELA, MACCHIAVELLO, GIORGIA, MOSER, GABRIELE, SERPICO, SEBASTIANO   +3 more
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Object-based contextual image classification built on image segmentation

IEEE Workshop on Advances in Techniques for Analysis of Remotely Sensed Data, 2003, 2004
The continuously improving spatial resolution of remote sensing sensors sets new demand for applications utilizing this information. The need for the more efficient extraction of information from high resolution RS imagery and the seamless integration of this information into Geographic Information System (GIS) databases is driving geo-information ...
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Combining Contextual Information for Subspace Based Hyperspectral Image Classification

2018 9th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS), 2018
Hyperspectral image classification is a difficult task in remote sensing community due to the challenges caused by high dimensionality and limited training samples. The traditional classification approaches used to exploit the rich spectral information only, while in the past decades the contextual information has been considered extensively to promote
Shuyuan Xu
exaly   +2 more sources

Star: A Contextual Description of Superpixels for Remote Sensing Image Classification

2017
Remote Sensing Images are one of the main sources of information about the earth surface. They are widely used to automatically generate thematic maps that show the land cover of an area. This process is traditionally done by using supervised classifiers which learn patterns extracted from the image pixels annotated by the user and then assign a label ...
Tiago M. H. C. Santana, Alexei Manso Corrêa Machado, Arnaldo de Albuquerque Araújo, Jefersson A. dos Santos   +3 more
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