New Methods and Approaches in Airborne Hyperspectral Data Processing

Dear Colleagues,

Hyperspectral imaging technologies have been widely used in many remote sensing applications, resulting in large quantities of hyperspectral image datasets. Therefore, the efficient acquisition, storage, transmission, and analysis of these massive image datasets has become challenging, especially for many onboard applications with severely constrained computing resources and communication bandwidths. Particular challenges in the processing of hyperspectral data are how to deal with high-volume data with limited spatial or spectral (for multispectral systems) resolutions, especially for reducing data or enhancing the (spatial or spectral) resolution and fusion of the spatial and spectral information for improved data prediction (i.e., classification and regression analysis). Many algorithms and techniques have been proposed and continue to be needed to address such challenges.

The aim of this Special Issue is thus to focus on and compile the latest advances related to Airborne Hyperspectral Data Processing. All contributions to such hyperspectral sensing systems offering timely high-quality observational capabilities for better sensing that meet the end-users’ requirements and expectations for interdisciplinary applications are welcome.

This Special Issue is devoted to novel processing techniques for hyperspectral image data using hardware or software solutions. We solicit your contributions addressing hyperspectral data processing and applications based on the following methods:

• Anomaly detection and target detection;
• Hyperspectral image correction and calibration;
• Applications of multispectral/hyperspectral imaging;
• Band selection, dimensionality reduction and data compression;
• Compressive sensing, sparse representation and tensor decomposition;
• Unsupervised learning, active learning and deep learning;
• Data/sensor/information fusion;
• Endmember finding, extraction and variability;
• High-performance computing;
• Multispectral/hyperspectral image classification;
• Hyperspectral unmixing;
• Subpixel target analysis;
• Hyperspectral data visualization.

Dr. Meiping Song
Prof. Dr. Bing Zhang
Dr. Fadi Kizel
Dr. Bai Xue
Dr. Haitao Zhao
Guest Editors

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