Results 21 to 30 of about 257,647 (295)
Contextual classification and segmentation of textured images [PDF]
International Conference on Acoustics, Speech, and Signal Processing, 2002 An algorithm which combines the merits of statistical classification- and estimation-theory-based approaches is proposed for textured image segmentation. The texture regions are modeled by noncausal Gaussian Markov random fields (GMRF). The algorithm is comprised of two stages.
P. W. Fung +2 more
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Cross Modal Few-Shot Contextual Transfer for Heterogenous Image Classification
Frontiers in Neurorobotics, 2021 Deep transfer learning aims at dealing with challenges in new tasks with insufficient samples. However, when it comes to few-shot learning scenarios, due to the low diversity of several known training samples, they are prone to be dominated by ...
Zhikui Chen +6 more
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Contextual multi-scale image classification on quadtree [PDF]
2016 IEEE International Conference on Image Processing (ICIP), 2016 In this paper, we propose a novel hierarchical method for remote sensing image classification. The proposed approach integrates an explicit hierarchical graph-based classifier, which uses a quad-tree structure to model multiscale interactions, and a third order Markov mesh random field to deal with pixel wise contextual information in the same scale ...
HEDHLI, IHSEN +3 more
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Combining Global and Local Information for Knowledge-Assisted Image Analysis and Classification
EURASIP Journal on Advances in Signal Processing, 2007 A learning approach to knowledge-assisted image analysis and classification is proposed that combines global and local information with explicitly defined knowledge in the form of an ontology. The ontology specifies the domain of interest, its subdomains,
M. G. Strintzis +3 more
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IET Radar, Sonar & Navigation, 2022 There are two types of important information in a polarimetric synthetic aperture radar (PolSAR) image: spatial features in two dimensions and polarimetric characteristics in the scattering dimension. Considering both polarimetric and spatial information
Maryam Imani
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Remote Sensing, 2022 In recent years, convolutional neural networks (CNNs) have been widely used in hyperspectral image (HSI) classification. However, feature extraction on hyperspectral data still faces numerous challenges.
Jun Sun +6 more
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Remote Sensing Image Change Detection Method Based on DBN and Object Fusion [PDF]
Jisuanji gongcheng, 2018 In high-resolution optical remote sensing image change detection,most of the object-oriented method can only use simple features combination to get the object features,which cannot implement design and characteristic extraction for high-level features ...
DOU Fangzheng,SUN Hanchang,SUN Xian,DIAO Wenhui,FU Kun
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CONTEXTUAL LAND USE CLASSIFICATION: HOW DETAILED CAN THE CLASS STRUCTURE BE? [PDF]
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2016 The goal of this paper is to investigate the maximum level of semantic resolution that can be achieved in an automated land use change detection process based on mono-temporal, multi-spectral, high-resolution aerial image data.
L. Albert, F. Rottensteiner, C. Heipke
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Supervised image classification based on AdaBoost with contextual weak classifiers [PDF]
IEEE International IEEE International IEEE International Geoscience and Remote Sensing Symposium, 2004. IGARSS '04. Proceedings. 2004, 2004 AdaBoost, one of machine learning techniques, is employed for supervised classification of land-cover categories of geostatistical data. We introduce contextual classifiers based on neighboring pixels. First, posterior probabilities are calculated at all pixels.
Ryuei Nishii, Shinto Eguchi
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Land Cover Mapping with Higher Order Graph-Based Co-Occurrence Model
Remote Sensing, 2018 Deep learning has become a standard processing procedure in land cover mapping for remote sensing images. Instead of relying on hand-crafted features, deep learning algorithms, such as Convolutional Neural Networks (CNN) can automatically generate ...
Wenzhi Zhao +3 more
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