A Multitask Deep Learning Model for Classification and Regression of Hyperspectral Images: Application to the large-scale dataset ...

• Main Authors: Chhapariya, Koushikey, Benoit, Alexandre, Buddhiraju, Krishna Mohan, Kumar, Anil
• Format: Report
• Language: unknown
• Published: arXiv 2024
• Subjects: Computer Vision and Pattern Recognition cs.CV; FOS Computer and information sciences; taiga
• Online Access: https://dx.doi.org/10.48550/arxiv.2407.16384; https://arxiv.org/abs/2407.16384

Multitask learning is a widely recognized technique in the field of computer vision and deep learning domain. However, it is still a research question in remote sensing, particularly for hyperspectral imaging. Moreover, most of the research in the remote sensing domain focuses on small and single-task-based annotated datasets, which limits the generalizability and scalability of the developed models to more diverse and complex real-world scenarios. Thus, in this study, we propose a multitask deep learning model designed to perform multiple classification and regression tasks simultaneously on hyperspectral images. We validated our approach on a large hyperspectral dataset called TAIGA, which contains 13 forest variables, including three categorical variables and ten continuous variables with different biophysical parameters. We design a sharing encoder and task-specific decoder network to streamline feature learning while allowing each task-specific decoder to focus on the unique aspects of its respective ...