Open AccessDissertation
Texture features for image classification and retrieval.
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The article was published on 2002-01-01 and is currently open access. It has received 570 citations till now. The article focuses on the topics: Contextual image classification & Texture (geology).read more
Citations
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Journal ArticleDOI
Precipitation Estimation from Remotely Sensed Imagery Using an Artificial Neural Network Cloud Classification System
TL;DR: In this article, a satellite-based rainfall estimation algorithm, Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN) Cloud Classification System (CCS), is described.
Journal ArticleDOI
Exploring feature-based approaches in PET images for predicting cancer treatment outcomes
I. El Naqa,Perry W. Grigsby,Aditya Apte,Elizabeth A. Kidd,Eric D. Donnelly,D. Khullar,S Chaudhari,Deshan Yang,M. Schmitt,Richard Laforest,Wade L. Thorstad,Joseph O. Deasy +11 more
TL;DR: Investigation of intensity-volume histogram metrics and shape and texture features extracted from PET images to predict patient's response to treatment suggests proposed approaches could potentially provide better tools and discriminant power for utilizing functional imaging in clinical prognosis.
Journal ArticleDOI
Retrieving forest structure variables based on image texture analysis and IKONOS-2 imagery
TL;DR: In this paper, the authors assessed the capability of 1-m resolution IKONOS-2 imagery to estimate the five main forest variables-age, top height, circumference, stand density and basal area-in even-aged common spruce stands.
Content-Based Image Retrieval Systems
TL;DR: In this paper, image data representation, similarity image retrieval, the architecture of a generic content-based image retrieval system, and different content- based image retrieval systems are presented.
Journal ArticleDOI
A comprehensive review of earthquake-induced building damage detection with remote sensing techniques
Laigen Dong,Jie Shan,Jie Shan +2 more
TL;DR: This paper provides a comprehensive review of remote sensing methods in two categories: multi-temporal techniques that evaluate the changes between the pre- and post-event data and mono-tem temporal techniques that interpret only the post- event data.
References
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Journal ArticleDOI
Precipitation Estimation from Remotely Sensed Imagery Using an Artificial Neural Network Cloud Classification System
TL;DR: In this article, a satellite-based rainfall estimation algorithm, Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN) Cloud Classification System (CCS), is described.
Journal ArticleDOI
Approaches for automated detection and classification of masses in mammograms
TL;DR: The methods for mass detection and classification for breast cancer diagnosis are discussed, and their advantages and drawbacks are compared.
Journal ArticleDOI
Exploring feature-based approaches in PET images for predicting cancer treatment outcomes
I. El Naqa,Perry W. Grigsby,Aditya Apte,Elizabeth A. Kidd,Eric D. Donnelly,D. Khullar,S Chaudhari,Deshan Yang,M. Schmitt,Richard Laforest,Wade L. Thorstad,Joseph O. Deasy +11 more
TL;DR: Investigation of intensity-volume histogram metrics and shape and texture features extracted from PET images to predict patient's response to treatment suggests proposed approaches could potentially provide better tools and discriminant power for utilizing functional imaging in clinical prognosis.
Journal ArticleDOI
An SVM Ensemble Approach Combining Spectral, Structural, and Semantic Features for the Classification of High-Resolution Remotely Sensed Imagery
Xin Huang,Liangpei Zhang +1 more
TL;DR: A new multifeature model, aiming to construct a support vector machine (SVM) ensemble combining multiple spectral and spatial features at both pixel and object levels is proposed, which provides more accurate classification results compared to the voting and probabilistic models.
Journal ArticleDOI
Retrieving forest structure variables based on image texture analysis and IKONOS-2 imagery
TL;DR: In this paper, the authors assessed the capability of 1-m resolution IKONOS-2 imagery to estimate the five main forest variables-age, top height, circumference, stand density and basal area-in even-aged common spruce stands.