A configurable five-step workflow for high-throughput hyperspectral image analysis and vegetation index mapping
Jae Gyeong Jung, Kyuchan Kim, Jae Yeob Jeong, Jun Hyuck Yoon, Eun Ji Bae · 2026
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Description
Hyperspectral cameras record hundreds of colors per pixel, far more than eyes see, and the data piles up fast. Jae Gyeong Jung and co-authors lay out a five-step workflow that turns those image cubes into vegetation maps at scale. It starts by cutting out regions of interest from corrected images, then groups pixels by their spectra without supervision.
A random forest classifier then labels pixels, helped by masks that flag overbright spots. Next the workflow computes plant health indices from formulas the user can edit, and finally it compares index distributions across treatments. Settings adjust at every step to match the imaging setup. The team demonstrates it on wheat kernels and turfgrass, indoors and out, across two spectral ranges. The pitch is one pipeline from raw cubes to treatment verdicts.
Catalog record
| CREATOR | Jae Gyeong Jung, Kyuchan Kim, Jae Yeob Jeong, Jun Hyuck Yoon, Eun Ji Bae |
|---|---|
| YEAR | 2026 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 39 KB |
| VERSION | 1.0 |
| LICENSE | CC BY 4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |