JigsawHSI: a network for Hyperspectral Image classification

Jaime Moraga

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arXiv · 2206.02327

JigsawHSI: a network for Hyperspectral Image classification

Jaime Moraga

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Abstract

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.

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BibTeX RIS

Jaime Moraga. 2022-06-06. JigsawHSI: a network for Hyperspectral Image classification. https://arxiv.org/abs/2206.02327

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