Papers › JigsawHSI: a network for Hyperspectral Image classification
JigsawHSI: a network for Hyperspectral Image classification
Jaime Moraga
This article describes Jigsaw, a convolutional neural network (CNN) used in geosciences and based on Inception but tailored for geoscientific analyses. Introduces JigsawHSI (based on Jigsaw) and uses it on the land-use land-cover (LULC) classification problem with the Indian Pines, Pavia University and Salinas hyperspectral image data sets. The network is compared against HybridSN, a spectral-spatial 3D-CNN followed by 2D-CNN that achieves state-of-the-art results on the datasets. This short article proves that JigsawHSI is able to meet or exceed HybridSN's performance in all three cases. It also introduces a generalized Jigsaw architecture in d-dimensional space for any number of multimodal inputs. Additionally, the use of jigsaw in geosciences is highlighted, while the code and toolkit are made available.
Code
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Hyperspectral Image Classification | Indian Pines | JigsawHSI | Overall Accuracy | 99.74 | #23 of 34 | Archive leaderboard | report |
| Hyperspectral Image Classification | Pavia University | JigsawHSI | Overall Accuracy | 100.00 | #13 of 33 | Archive leaderboard | report |
| Hyperspectral Image Classification | Salinas | JigsawHSI | OA@200 | 100.00 | #1 of 3 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
Methods
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