Multi-Task Deep Learning for Image Fusion and Segmentation

Dear Colleagues,

Typically, a deep learning model is trained to perform a single task with high accuracy; for example classifying images.  Multi-task deep learning is a technique in machine learning where a deep model is trained to perform several tasks (e.g., classify an image, segment out the object, and predict the depth) with different metrics and a collection of shared representations.  By training the model across several related tasks, the model develops features which are less prone to overfitting on the training data and thus generalizes better.  This technique has shown great success in image and textual analysis.  In this special issue, we consider the applicability of this technique to problems arising in remote sensing such as scene segmentation, image fusion, image registration, object detection, super resolution, and anomaly detection.

Dr. Doster Timothy J
Dr. Brian Alan Johnson
Dr. Lei Ma
Guest Editors

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