Development of deep learning-based hyperspectral remote sensing image unmixing

Hyperspectral remote sensing is an advanced technique for earth observation that combines physical imagery and spectral analysis technology. Therefore, hyperspectral remote sensing can obtain fine spectral and rich spatial information from imaged scenes, merging the spatial and spectral information into data cubes. These data cubes exhibit narrow spectral bands and a high spectral resolution, allowing different land cover objects to be distinguished. Hyperspectral remote sensing images, with their high spectral resolution and cube characteristics, have gradually become among the most essential supporting data in remote sensing engineering applications. However, due to spatial resolution limitations, the mixed pixel problem has hindered the development of hyperspectral remote sensing in fine-scale object information extraction. At present, hyperspectral unmixing is one of the most effective analytical techniques for dealing with mixed pixel problems, aiming to break through spatial resolution limitations by analyzing the components within pixels. Hyperspectral unmixing refers to any process that separates pixel spectra from a hyperspectral image into a collection of pure constituent spectra, called endmembers, and a set of corresponding abundance fractions. At each pixel, the endmembers are generally assumed to represent the pure materials in the scene, while the abundances represent the percentage of each endmember. For the fine-scale interpretation of object information, many unmixing methods have been developed for hyperspectral remote sensing images in the remote sensing field over the past 30 years, mitigating the impact of mixed pixel problems on quantitative remote sensing analysis. Currently, with the development of deep learning, an increasing number of deep learning theories and tools are used to deal with mixed pixel problems. Many new methods using deep learning for unmixing have been developed, and unmixing technology research has gradually entered a new stage of development with deep learning. Deep-learning-based methods make better use of hidden information, have a relatively lower dependence on prior knowledge, and have a stronger adaptability to complex scenes than traditional unmixing methods. Although deep learning-based unmixing methods have developed rapidly in recent years and are diverse, the analysis and summary of the work on such methods have not kept up with the pace of technological development. A timely summary of the latest research progress on developing a specific field of research has a significant role in promoting the technology. Thus, this paper sorts out the existing deep learning-based unmixing methods, classifying them according to the adopted spectral mixing models, the deep network training modes, and whether spectral variability is considered. Furthermore, this paper introduces these deep learning-based approaches and summarizes their characteristics, making the use of these methods in special works convenient for users or readers. Finally, the development of deep learning methods is summarized, referring to the current technical status, characteristics, and development prospects. In addition, some existing deep learning unmixing methods were tested in this study and organized to facilitate the research and application of unmixing technology. The development of deep learning will continue to promote the progress of unmixing techniques. In recent years, deep learning-based unmixing methods have developed rapidly and have been gradually used in vegetation distribution investigation and agricultural yield estimation, implying their good development prospect and application value. his paper can provide valuable references for researching unmixing technology in the future. © 2024 Science Press. All rights reserved.