Robust Multispectral/Hyperspectral Image Analysis and Classification
A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Remote Sensing Image Processing".
Deadline for manuscript submissions: closed (1 December 2019) | Viewed by 144313
Special Issue Editors
Interests: compressed sensing; signal and image processing; pattern recognition; computer vision; hyperspectral image analysis
Special Issues, Collections and Topics in MDPI journals
Interests: multi- and hyperspectral remote sensing image processing and analysis; super-resolution, fusion, denoising, unmixing, classification, feature extraction
Special Issues, Collections and Topics in MDPI journals
Interests: machine learning; computer vision; information fusion; image super resolution; hyperspectral image analysis; infrared imaging; image denoising
Special Issues, Collections and Topics in MDPI journals
Interests: deep learning; computer vision; remote sensing
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
We observe that satellite imagery, such as a multispectral/hyperspectral image, is a powerful source of information, as it contains different spatial, spectral and temporal resolutions, compared to traditional images. In the past decade, the remote sensing community has introduced intensive works to establish accurate remote sensing image classifiers. However, there are inherent challenges for remote sensing imagery analysis and classification. For example, the quantity of labeled data for remote sensing imagery (e.g., multispectral and hyperspectral image) is limited since it is time-consuming and expensive to obtain a large number of samples with class labels. Also, actual hyperspectral image data inevitably contain considerable noise (Gaussian noise, dead-lines, and other mixed noise) due to the physical limitations of the imaging sensors. In addition, label noise (i.e. mis-labeling of pixels) poses challenges for supervised classification algorithms. Therefore, developing robust image classification and analysis methods that can handle these issues becomes a pressing need for practical applications.
The aim of this Special Issue is to gather cutting-edge works that address the aforementioned challenges in multispectral/hyperspectral image analysis and classification. The main topics include, but not limited to:
- Robust multispectral/hyperspectral image classification algorithms and feature representations under the conditions of
- Noisy data
- Noisy label
- Small sample size
- Data imbalance
- Multispectral/hyperspectral image denoising
- Missing data reconstruction
- Multispectral/hyperspectral data unmixing
- Illumination Enhancement
- Noise robust multispectral/hyperspectral image analysis
- Compression
- Compressive sensing
- Object/target/anomaly detection
- Super-resolution
- Feature/corresponding matching
- Fusion
Dr. Junjun Jiang
Dr. Jiayi Ma
Dr. Sidike Paheding
Guest Editors
Manuscript Submission Information
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Keywords
- Multispectral/hyperspectral remote sensing
- Remote sensing image analysis
- Noise robust classification
- Data imbalance
- Computer vision
- Machine learning
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