Advancement in Hyperspectral Image Processing with Machine Learning

Dear Colleagues, 

Recent advances in machine learning (ML) have catalyzed a paradigm shift in hyperspectral image processing, moving beyond traditional model-driven methods toward data-driven architectures capable of exploiting the full spectral–spatial information cube. This Special Issue, “Advancement in Hyperspectral Image Processing With Machine Learning”, aims to present cutting-edge research that harnesses the synergy between HSI high-dimensional richness and modern ML representational power. We seek contributions exploring novel frameworks for fundamental tasks such as dimensionality reduction, supervised/unsupervised classification, spectral unmixing, and subpixel target detection. Particular emphasis is placed on deep learning paradigms, including convolutional neural networks, vision transformers, graph neural networks, and generative models, as well as strategies to mitigate limited labeled data through few-shot, self-supervised, or transfer learning. The scope also extends to emerging applications in precision agriculture, environmental monitoring, medical diagnostics, and mineralogy, where robust HSI analysis directly translates to actionable insights. By bridging the gap between algorithmic innovation and practical deployment, this collection seeks to highlight reproducible, efficient, and generalizable solutions that address the unique challenges of high-dimensional, mixed-pixel, and noisy HSI data.

Dr. Wenbo Yu
Guest Editor

Manuscript Submission Information

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