M
Mahdi Khodadadzadeh
Researcher at Helmholtz-Zentrum Dresden-Rossendorf
Publications - 57
Citations - 996
Mahdi Khodadadzadeh is an academic researcher from Helmholtz-Zentrum Dresden-Rossendorf. The author has contributed to research in topics: Hyperspectral imaging & Support vector machine. The author has an hindex of 11, co-authored 56 publications receiving 725 citations. Previous affiliations of Mahdi Khodadadzadeh include Tarbiat Modares University & University of Extremadura.
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Fusion of Hyperspectral and LiDAR Remote Sensing Data Using Multiple Feature Learning
TL;DR: A new efficient strategy for fusion and classification of hyperspectral and LiDAR data designed to integrate multiple types of features extracted from these data, which does not require any regularization parameters.
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Spectral–Spatial Classification of Hyperspectral Data Using Local and Global Probabilities for Mixed Pixel Characterization
TL;DR: This paper presents a new spectral-spatial classifier for hyperspectral data that specifically addresses the issue of mixed pixel characterization and indicates that the proposed classifier leads to state-of-the-art performance when compared with other approaches, particularly in scenarios in which very limited training samples are available.
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Subspace-Based Support Vector Machines for Hyperspectral Image Classification
TL;DR: This letter extends the subspace-projection-based concept to support vector machines (SVMs), a very popular technique for remote sensing image classification, and constructs the SVM nonlinear functions using the subspaces associated to each class.
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A Discontinuity Preserving Relaxation Scheme for Spectral–Spatial Hyperspectral Image Classification
TL;DR: The newly proposed method is an iterative relaxation procedure, which exploits spatial information in such a way that it considers discontinuities existing in the data cube, which leads to state-of-the-art classification results when combined with probabilistic classifiers for several widely used hyperspectral data sets.
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A Machine Learning Framework for Drill-Core Mineral Mapping Using Hyperspectral and High-Resolution Mineralogical Data Fusion
Isabel Cecilia Contreras Acosta,Mahdi Khodadadzadeh,Laura Tusa,Pedram Ghamisi,Richard Gloaguen +4 more
TL;DR: The ability of the proposed technique to fuse and up-scale high-resolution mineralogical analysis with drill-core HS data is demonstrated, qualitatively and quantitatively.