Intelligent Hyperspectral Image Compression Using Machine Learning

Dear Colleagues,

Hyperspectral imaging technologies have been widely used in many remote sensing applications, resulting in large quantities of hyperspectral image datasets. Efficient acquisition, storage, and transmission of these massive image datasets becomes very challenging, especially for many onboard applications with severely constrained computing resources and communication bandwidths. Therefore, data compression techniques play a crucial role in the development of hyperspectral imaging techniques. Traditional data compression techniques provide either lossy, near lossless or strictly lossless compression on the data by identifying and using structures that exist in data of limited sizes (e.g., in individual images). Recent advancements in machine learning and artificial intelligence in general offer exciting new opportunities for compression algorithms to become “smarter” and thus more efficient, by learning and discovering structures existing in massive datasets with ever-increasing sizes.

This Special Issue is devoted to novel compression techniques for hyperspectral image data using machine learning. We solicit your contributions addressing applications of machine learning to hyperspectral data compression based some of the following methods:

• Statistical machine learning
• Supervised machine learning
• Unsupervised machine learning
• Semisupervised machine learning
• Reinforcement machine learning
• Transfer learning
• Active learning
• Online learning
• Other machine learning methods
  

Manuscript Submission Information

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Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

• hyperspectral image
• data compression
• onboard compression
• machine learning
• deep learning
• neural network
• artificial intelligence