Masking and Mixing Boost Hyperspectral Classification
· newswise.com · popularity 21/100 · 0 similar stories today · Music (Music)
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Summary
A new contrastive-learning framework enables hyperspectral classification with few labels. It masks redundant spatial and spectral information, mixes samples into positive pairs, and separates spatial and spectral features. On four benchmarks, using five labels per class, it improved key accuracy metrics, offering a practical route to cost-effective, robust land-cover mapping.
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