论文标题
拼图：高光谱图像分类网络
JigsawHSI: a network for Hyperspectral Image classification
论文作者
论文摘要
本文介绍了拼图，这是一种用于地球科学的卷积神经网络（CNN），并基于Inception，但针对地球科学分析量身定制。介绍了Jigsawhsi（基于拼图），并将其用于印度松树，帕维亚大学和Salinas Hyperspectral图像数据集的土地使用土地覆盖（LULC）分类问题。将网络与Hybridsn进行比较，Hybridsn是一个光谱空间3D-CNN，然后是2D-CNN，可在数据集中获得最新的结果。这篇简短的文章证明了拼图在所有三种情况下都能达到或超过混合动力的表现。它还为任何数量的多模式输入引入了D维空间中的广义拼图体系结构。此外，强调了在地球科学中使用拼图的，而代码和工具包可用。
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.
