JigsawHSI: a network for Hyperspectral Image classification
This work addresses land-use land-cover classification in geosciences using hyperspectral images, presenting an incremental improvement over existing methods.
The authors tackled hyperspectral image classification for land-use land-cover using a tailored convolutional neural network called JigsawHSI, achieving performance that met or exceeded the state-of-the-art HybridSN on three datasets.
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.