Dr. Qian Du
Professor at Mississippi State Univ
SPIE Involvement:
Fellow status | Conference Program Committee | Conference Chair | Conference Co-Chair | Author | Editor | Instructor
Publications (73)

PROCEEDINGS ARTICLE | October 5, 2017
Proc. SPIE. 10430, High-Performance Computing in Geoscience and Remote Sensing VII
KEYWORDS: Hyperspectral imaging, Image classification, Parallel computing, Algorithm development

SPIE Journal Paper | September 1, 2017
JARS Vol. 11 Issue 04
KEYWORDS: Image classification, Image segmentation, Image fusion, Hyperspectral imaging, Spatial resolution, Lithium, Data fusion, Information fusion, Convolutional neural networks, Remote sensing

PROCEEDINGS ARTICLE | May 5, 2017
Proc. SPIE. 10198, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XXIII
KEYWORDS: Hyperspectral imaging, Binary data, Expectation maximization algorithms, Remote sensing, Critical dimension metrology, Multispectral imaging, Target detection, Independent component analysis, Algorithm development, Algorithms

PROCEEDINGS ARTICLE | May 5, 2017
Proc. SPIE. 10198, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XXIII
KEYWORDS: Neurons, Neural networks, Hyperspectral imaging, Image classification, Remote sensing, Interference (communication), L band, Optical sensors, Spectroscopy, Image sensors, Sensors

SPIE Journal Paper | September 6, 2016
JRS Vol. 10 Issue 04
KEYWORDS: Floods, Lithium, Error analysis, Satellites, Information technology, Multispectral imaging, Algorithm development, Analytical research, Vegetation, Image classification

PROCEEDINGS ARTICLE | May 19, 2016
Proc. SPIE. 9874, Remotely Sensed Data Compression, Communications, and Processing XII
KEYWORDS: Image segmentation, Signal to noise ratio, Hyperspectral imaging, Strontium, Image processing algorithms and systems, Computer simulations, Reconstruction algorithms, Lithium, Minerals, Telecommunications

Showing 5 of 73 publications
Conference Committee Involvement (19)
High-Performance Computing in Geoscience and Remote Sensing
12 September 2017 | Warsaw, Poland
High-Performance Computing in Geoscience and Remote Sensing
28 September 2016 | Edinburgh, United Kingdom
High-Performance Computing in Remote Sensing
21 September 2015 | Toulouse, France
Satellite Data Compression, Communications, and Processing XI
23 April 2015 | Baltimore, Maryland, United States
High-Performance Computing in Remote Sensing
22 September 2014 | Amsterdam, Netherlands
Showing 5 of 19 published special sections
Course Instructor
SC1161: Dimensionality Reduction for Hyperspectral Image Analysis
Hyperspectral imaging is an emerging technique in remote sensing. The very high spectral resolution in the hundreds of acquired images provides the potential of more accurate detection, classification, and quantification than that obtained using traditional broad-band multispectral imaging sensors. However, the resulting high data dimensionality poses challenges in data analysis. This course explains basic principles of dimensionality reduction that can maintain or even improve the performance of hyperspectral data analysis tasks (e.g., detection, classification). The primary goal of this course is to introduce the preferred feature extraction and feature selection algorithms for hyperspectral imagery. Specifically, two types of dimensionality reduction techniques, transformation-based and band-selection-based, will be studied. For each category, both supervised (with known class types and samples) and unsupervised (without any prior knowledge) approaches will be presented. In addition to the widely-used approaches, the state-of-the-art algorithms (e.g., manifold learning, sparse regression) will be discussed. Performance of all the techniques is evaluated using several real data experiments.
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