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

TL;DR

This paper introduces a multitask deep learning model for hyperspectral image analysis that simultaneously performs classification and regression tasks, demonstrating superior performance on a large-scale dataset with diverse biophysical parameters.

Contribution

The study presents a novel multitask deep learning architecture with shared encoder and task-specific decoders, incorporating multi-scale context extraction and attention mechanisms for hyperspectral data.

Findings

Outperforms state-of-the-art methods on TAIGA dataset

Achieves higher mean performance with lower variability

Effectively handles multiple diverse tasks simultaneously

Abstract

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 task. Additionally, a dense atrous pyramid pooling layer and attention network were integrated to extract multi-scale contextual information and enable selective information processing by prioritizing task-specific features. Further, we computed multitask loss and optimized its parameters for the proposed framework to improve the model performance and efficiency across diverse tasks. A comprehensive qualitative and quantitative analysis of the results shows that the proposed method significantly outperforms other state-of-the-art methods. We trained our model across 10 seeds/trials to ensure robustness. Our proposed model demonstrates higher mean performance while maintaining lower or equivalent variability. To make the work reproducible, the codes will be available at https://github.com/Koushikey4596/Multitask-Deep-Learning-Model-for-Taiga-datatset.