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Hyperspectral Image Classification archive 2025-07-28

Kennedy Space Center Benchmark (Hyperspectral Image Classification)

14 rows 9 with code listed 6 metrics Dataset page

Hyperspectral Image Classification is a task in the field of remote sensing and computer vision. It involves the classification of pixels in hyperspectral images into different classes based on their spectral signature. Hyperspectral images contain information about the reflectance of objects in hundreds of narrow, contiguous wavelength bands, making them useful for a wide range of applications, including mineral mapping, vegetation analysis, and urban land-use mapping. The goal of this task is to accurately identify and classify different types of objects in the image, such as soil, vegetation, water, and buildings, based on their spectral properties.

The archive carries no text for this table; the description above is the archive's text for the task Hyperspectral Image Classification. archive 2025-07-28

Over time archive 2025-07-28

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Direction inferred from the metric name, not from the archive: Overall Accuracy (higher is better), F1@10%perclass (higher is better). Not inferred (points only, no best-so-far line): OA@15perclass, AA@10%perclass, Kappa@10%perclass, OA@10%perclass. Points are placed at the row's paper date; 14 of 14 rows carry one.

Results archive 2025-07-28

Archive rows end at the archive snapshot, 2025-07-28: no result published after that date is in this table. Rank is the archive's row order at that snapshot; not re-ranked here. Metric values are the archive's strings. Column headers sort the table in your browser; each row keeps its archive rank.

Paper Code Ran Syntology Report
1 RPNet-RF 98.51 – Paper Code 2023 linked, not harvested report
2 TC-GAN 98.39±0.63 – Paper – 2022 no code linked report
3 DCFSL 97.59±1.03 – Paper Code 2022 linked, not harvested report
4 S-DMM 95.83±1.68 – Paper Code 2019 linked, not harvested report
5 RPNet 95.83 – Paper Code 2018 linked, not harvested report
6 IFRF 95.07 – Paper – 2013 no code linked report
7 CA-GAN 91.17±1.54 – Paper – 2020 no code linked report
8 3D-CNN 87.18±1.00 – Paper Code 2017 linked, not harvested report
9 HSI-BERT 82.93±0.94 – Paper – 2019 no code linked report
10 2D-CNN 80.53±1.31 – Paper Code 2015 linked, not harvested report
11 3D VS-CNN 80.15±0.62 – Paper – 2020 no code linked report
12 SSBC 98.06%98.61%98.75%98.11% – Paper Code 2024 linked, not harvested report
13 CVSSN 98.29±0.45%0.9878±0.003398.90±0.30%98.90±0.30% – Paper Code 2022 linked, not harvested report
14 A2S2K-ResNet 99.34 – Paper Code 2020 linked, not harvested report

All 14 rows shown. 14 link to a paper page on this site; 0 are marked as using additional training data in the archive. No GitHub stars are tracked; "Code" is the first repository the archive lists for the row. The archive carries no row tags, review links or community-submitted rows for this table; none are shown. archive 2025-07-28

Syntology Ran reads "N of M ran · U unverified": of the M code samples Syntology harvested from repositories linked to that row's paper (joined by arXiv id), N executed on a synthesized input and the other U = M−N are unverified (harvested, no recorded run). It counts code from repositories linked to that row's paper, not this result: the row's number was not reproduced and nothing here is a correctness claim. The other cell texts mean no graph line for the row: "linked, not harvested" (the archive links code, Syntology has not harvested it), "no code linked" (no code link in the archive), "not matched" (the row's paper URL matched no paper on this site). 0 rows have a graph line, from 0 distinct papers; 0 rows (0 papers) have at least one sample that ran. Counting each paper once: Syntology ran 0 of 0 samples; 0 unverified. Separately, 0 of those 0 are pointer-only (licence): the site points at that code rather than redistributing it, a licence property recorded for ran and unverified samples alike; each cell's tooltip carries the row's own pointer-only count. Read from the graph 2026-09-25. Per-sample status is on the paper page.

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