Machine Learning Approaches for Semantic and Instance Segmentation in Remote Sensing

Dear Colleagues,

We are thrilled to announce the continuation of our successful Special Issue in the journal Remote Sensing. Building on the achievements of the first, we are excited to introduce the second volume, which aims to explore new frontiers in semantic and instance segmentation within the realm of remotely sensed images.

In the previous volume, we witnessed groundbreaking research on spectral–spatial classification, object-based image analysis, feature extraction, and machine learning. Now, we are broadening our horizons to encompass the dynamic field of remote sensing research, placing a special emphasis on machine learning approaches for semantic and instance segmentation in remote sensing.

This volume invites contributions from researchers specializing in semantic and instance segmentation for remotely sensed imagery. While we continue to appreciate the significance of integrating spectral and spatial information extraction methods for hyperspectral data analysis, this second volume embraces a broader array of methodologies and remotely sensed data types. This encompasses cutting-edge machine learning and deep learning approaches that are applicable to a wide spectrum of remote sensing data, extending beyond hyperspectral images.

Topics of Interest:

• Semantic and instance segmentation in remote sensing data;
• Supervised machine learning for image segmentation and object detection;
• Deep learning techniques for multispectral/hyperspectral image analysis;
• Novel approaches for feature extraction;
• Insights from applying state-of-the-art deep learning scenarios.

Dr. Amin Zehtabian
Dr. Roozbeh Rajabi
Prof. Dr. Pedram Ghamisi
Dr. Keshav Singh
Guest Editors

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 100 words) can be sent to the Editorial Office for announcement on this website.

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.

• semantic segmentation
• instance segmentation
• object detection
• hyperspectral imagery
• supervised machine learning
• deep learning
• feature extraction

• Spectral-Spatial Segmentation and Classification of Remotely Sensed Hyperspectral Images in Remote Sensing (9 articles)