Dr. Qian Du
Professor at Mississippi State Univ
SPIE Involvement:
Author | Editor | Instructor | Science Fair Judge
Publications (74)

Proceedings Article | 20 September 2020 Presentation + Paper
Proc. SPIE. 11533, Image and Signal Processing for Remote Sensing XXVI
KEYWORDS: Hyperspectral imaging, Computer programming, Gallium nitride

Proceedings Article | 20 September 2020 Presentation + Paper
Proc. SPIE. 11533, Image and Signal Processing for Remote Sensing XXVI
KEYWORDS: Hyperspectral imaging, Error analysis, Image classification, Error control coding

Proceedings Article | 20 May 2020 Presentation + Paper
Proc. SPIE. 11392, Algorithms, Technologies, and Applications for Multispectral and Hyperspectral Imagery XXVI
KEYWORDS: Hyperspectral imaging, Image fusion, Spatial filters, LIDAR, Feature extraction, Data fusion

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

SPIE Journal Paper | 1 September 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

Showing 5 of 74 publications
Proceedings Volume Editor (2)

Conference Committee Involvement (20)
High-Performance Computing in Geoscience and Remote Sensing
13 September 2018 | Berlin, Germany
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
Showing 5 of 20 Conference Committees
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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