About this Research Topic
With the rapid development of imaging sensors, hyperspectral data have been successfully applied to a plethora of applications, e.g., environmental monitoring, precision agriculture, and climate change. Recently, different airborne and spaceborne hyperspectral sensors have been developed such as already launched or planned to launch PRISMA, EnMAP, HyspIRI, Hyperion (EO-1), and Copernicus Hyperspectral Imaging Mission for The Environment (CHIME). The key goal of this Research Topic is to advance sophisticated hyperspectral imaging methodologies to ensure sustainable development of the economy and society. Some of the main challenges to tackle are hyperspectral sensor technology development to design robust low-cost hyperspectral imagers, integration and fusion of different remote sensing datasets for example hyperspectral imaging and microwave remote sensing datasets, and addressing uncertainty in ecological parameters. The key to the success of the aforementioned applications is studies on hyperspectral data processing using multivariate statistics and artificial intelligence such as machine learning and deep learning.
The aim of this Research Topic is to cover recent advances in hyperspectral imaging for environmental monitoring and analysis, including novel data processing algorithms, development, and interpretation of hyperspectral datasets. This Research Topic welcomes both original research and review articles related to hyperspectral imaging technology in diversified areas of environmental science. The main topics of this Research Topic are, but are not limited to, the following areas:
Hyperspectral Imaging Technologies and Applications:
• Precision agriculture, crop science, and phenotyping
• Natural resources (forestry, wetlands, geology, coastal regions, snow, ice, etc.)
• Hyperspectral aquatic (underwater) remote sensing
• Hyperspectral microwave remote sensing
• Proximal and UAV-based hyperspectral imaging
• Airplane-Based and Satellite-Based Hyperspectral Imaging
• Pollution and particulate monitoring (trace gases, forensics)
• Extreme environment monitoring
• Hyperspectral imaging in climate change and ecosystem modeling
• Survey and comparison study of recent technologies in hyperspectral imaging
Hyperspectral Data Processing Algorithms:
• Pre-Processing of Hyperspectral Images
• Dimensionality reduction and feature extraction methods
• Hyperspectral image classification and segmentation
• Hyperspectral mixed pixel analysis and endmember extraction algorithms
• Hyperspectral target detection methods
• Hyperspectral data fusion and decision-making strategies
• Machine learning and deep learning algorithms for hyperspectral imaging
• Survey and comparison study of recent developments in the hyperspectral data processing
Keywords: Hyperspectral Imaging, Environmental Monitoring, Image Processing, Machine Learning, Remote Sensing
Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.