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.
翻译:这篇文章描述了Jigsaw, 是一个用于地球科学的、基于感知的、但专门用于地球科学研究的进化神经网络(CNN),介绍了JigsawHSI(基于Jigsaw),并将其用于印度松树、帕维亚大学和萨利纳斯超光谱图像数据集的土地使用土地覆盖分类问题,与Penes、Pavia大学和Salinas超光谱图像数据集的Gigsaw比较,该网络与三维光谱空间3D-CNN、以及2D-CNN(实现数据集最新结果)的GigsawHSI相比,这篇短文证明JigsawHSI在所有三种情况下都能够满足或超过GIVSN的性能。此外,在提供代码和工具包的同时,着重介绍了在地质科学中使用jigsaw的情况。