Results 271 to 280 of about 253,119 (310)

A markov random field approach to spatio-temporal contextual image classification

IEEE Transactions on Geoscience and Remote Sensing, 2003
Markov random fields (MRFs) provide a useful and theoretically well-established tool for integrating temporal contextual information into the classification process. In particular, when dealing with a sequence of temporal images, the usual MRF-based approach consists in adopting a "cascade" scheme, i.e., in propagating the temporal information from the
MELGANI, FARID, SERPICO, SEBASTIANO
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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, Chenqiang Gao, Deyu Meng, Wangmeng Zuo   +3 more
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Contextual Image Classification Based on Spatial Boosting

2006 IEEE International Symposium on Geoscience and Remote Sensing, 2006
Spatial AdaBoost proposed by Nishii and Eguchi (TGRS, 2005) is a supervised image classification method. It is a voting machine based on log posterior probabilities at a test pixel and its neighbors. The method can be obtained by less computation effort with respect to a classifier based on Markov random fields, but still shows a similar excellent ...
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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.
R. Nishii, S. Eguchi
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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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Polarimetric SAR image classification based on contextual sparse representation

2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), 2015
A CSR-Based (Contextual Sparse Representation) classification method for PolSAR image is proposed based on the idea of sparse representation and spatial correlation, which incorporates the intrinsic polarimetric information and the spatial contextual information in the sparse representation procedure.
Lamei Zhang, Liangjie Sun, Wooil M. Moon
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A discriminative model for contextual classification of hyperspectral images

2018 26th Signal Processing and Communications Applications Conference (SIU), 2018
In this study a probabilistic method for contextual classification of hyperspectral images is proposed with the purpose of land cover classification. The proposed method consists of a multinomial logistic regression model, and multi-nomial autologistic regression model for spatial smoothing.
Sezer Kutluk, Koray Kayabol, Aydin Akan
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Contextual Image Classification Through Fine-Tuned Graph Neural Networks

2021
Nowadays, computer vision techniques have become popular in several domains (e.g., agriculture, industry, medicine, and others). Their success derives from the advances in computational resources and the large volume of complex data (i.e., images). These factors led to an increase in the use of convolutional neural networks. However, such deep learning
Walacy S. Campos, Luis G. Souza, Priscila T. M. Saito, Pedro H. Bugatti   +3 more
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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, Jun Li
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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, Lina Yang, Huiwu Luo, Yuan Yan Tang   +4 more
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