Special Issue on Interactive Deep Learning for Hyperspectral Images
Journal of Remote Sensing (ISSN 2072-4292). 2019 Impact Factor: 4.509 (Journal Citation Reports)
Deep models have achieved remarkable results in many real-world applications based on quality-labeled training samples. However, accruing reliable training samples is expensive in many real-world applications, particularly in the remote sensing domain. Therefore, there is a need to develop interactive deep schemes that can help to attain reliable, informative, and heterogeneous samples for learning. For this Special Issue of Remote Sensing, we aim to present a collection of articles related to “Interactive Deep Learning for Hyperspectral Images” including, but not limited to, the following topics:
1. Investigation of the behavior and performance of deep models in terms of the computational cost and generalized performance for both on-shelf and novel classification methods under different experimental conditions.
2. Development of several novel strategies to limit the Hughes phenomenon for hyperspectral image classification by exploiting several on-shelf and novel sample selection methods. This includes the generation of a good spectral library, the development of suitable models to upscale the ground spectra to air and space-borne measurements, evolving application methodologies, and device algorithms for pre and post-processing hyperspectral cubes.
Dr. Muhammad Ahmad
Dr. Adil Mehmood Khan
Dr. Diego Oliva
Dr. Omar Nibouche