Dehazing Remote Sensing and UAV Imagery: A Review of Deep Learning, Prior-based, and Hybrid Approaches
It addresses the need for high-quality images in remote sensing and UAV applications, but as a review, it is incremental in summarizing existing work.
This review tackles the problem of image dehazing in remote sensing and UAV imagery by providing a comprehensive overview of deep learning, prior-based, and hybrid approaches, identifying key challenges such as dataset limitations and evaluation metrics.
High-quality images are crucial in remote sensing and UAV applications, but atmospheric haze can severely degrade image quality, making image dehazing a critical research area. Since the introduction of deep convolutional neural networks, numerous approaches have been proposed, and even more have emerged with the development of vision transformers and contrastive/few-shot learning. Simultaneously, papers describing dehazing architectures applicable to various Remote Sensing (RS) domains are also being published. This review goes beyond the traditional focus on benchmarked haze datasets, as we also explore the application of dehazing techniques to remote sensing and UAV datasets, providing a comprehensive overview of both deep learning and prior-based approaches in these domains. We identify key challenges, including the lack of large-scale RS datasets and the need for more robust evaluation metrics, and outline potential solutions and future research directions to address them. This review is the first, to our knowledge, to provide comprehensive discussions on both existing and very recent dehazing approaches (as of 2024) on benchmarked and RS datasets, including UAV-based imagery.