Machine Learning and Pattern Analysis in Hyperspectral Remote Sensing

Dear Colleagues,

With the advent of new hyperspectral sensors aboard airborne or spaceborne platforms, remote sensing imaging spectroscopy can be applied to research in many fields, such as agriculture, biodiversity, mining, coastal zones, urban planning, defense. In fact, its ability to capture spectral feature characterizing the physical and chemical properties of scene materials opens the way to a better understanding and monitoring of a large variety of geographical areas.

Breakthroughs in the domain of machine learning over the past 10 years have motivated the remote sensing community to research in this direction, with results that outperform traditional approaches. In the context of hyperspectral data, thanks to its outstanding predictive capabilities, machine learning has become essential to automatically decipher the relationships between an optical/radiative property (reflectance, emissivity, radiance) and the corresponding information. Machine learning can already fulfil several tasks like target or anomaly detection, land cover classification, spectral unmixing, and physical/chemical parameter estimation. Nevertheless, several challenges to improve the performance of imaging spectroscopy with machine learning remain, such as the intrinsic dimensionality of hyperspectral images, the robustness and reliability of neural networks, spatio–temporal approaches, combinations with other measurements, imperfect and potentially large learning databases, lack of standardized datasets and experiments for benchmarking, complementarity between hyperspectral imagery and multimodal acquisitions, benefits of combining multitemporal hyperspectral images.

This Special Issue aims to present new and/or innovative methods, approaches, and products demonstrating the benefits of machine learning applied to hyperspectral imagery. Submissions will highlight how the scientific community tends to answer to these challenges.

Dr. Xavier Briottet
Dr. Thomas Corpetti
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

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.