JigsawHSI: a network for Hyperspectral Image classification

6 Jun 2022  ·  Jaime Moraga, H. Sebnem Duzgun ·

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. Additionally, the use of jigsaw in geosciences is highlighted, while the code and toolkit are made available.

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Results from the Paper

Task Dataset Model Metric Name Metric Value Global Rank Uses Extra
Training Data
Hyperspectral Image Classification Indian Pines JigsawHSI Overall Accuracy 99.74 # 5
Hyperspectral Image Classification Pavia University JigsawHSI Overall Accuracy 100.00 # 1
Hyperspectral Image Classification Salinas JigsawHSI OA@200 100.00 # 1