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Papers/JigsawHSI: a network for Hyperspectral Image classification

JigsawHSI: a network for Hyperspectral Image classification

Jaime Moraga

2022-06-06Hyperspectral Image ClassificationImage ClassificationClassification
PaperPDFCode(official)

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.

Results

TaskDatasetMetricValueModel
HyperspectralPavia UniversityOverall Accuracy100JigsawHSI
HyperspectralIndian PinesOverall Accuracy99.74JigsawHSI
HyperspectralSalinasOA@200100JigsawHSI
Image ClassificationPavia UniversityOverall Accuracy100JigsawHSI
Image ClassificationIndian PinesOverall Accuracy99.74JigsawHSI
Image ClassificationSalinasOA@200100JigsawHSI
Hyperspectral Image SegmentationPavia UniversityOverall Accuracy100JigsawHSI
Hyperspectral Image SegmentationIndian PinesOverall Accuracy99.74JigsawHSI
Hyperspectral Image SegmentationSalinasOA@200100JigsawHSI

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