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首页> 外文期刊>IEEE Geoscience and Remote Sensing Letters >Hyperspectral Image Classification Using CapsNet With Well-Initialized Shallow Layers
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Hyperspectral Image Classification Using CapsNet With Well-Initialized Shallow Layers

机译：使用CapsNet和良好初始化的浅层进行高光谱图像分类

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摘要

In this letter, an alternative data-driven HSI classification model based on CapsNet is proposed rather than recently predominant convolutional neural network (CNN)-based models. To adjust the CapsNet to HSI classification, we tune a new CapsNet architecture with three convolutional layers. The added shallow layer provides higher level features to the primary capsules, which indirectly speeds up the following routing procedure. To guarantee a good convergence of the whole CapsNet, the three shallow layers are initialized by transferring convolutional parameters from a pretrained CNN model. The improved CapsNet-based models with and without vote strategy both achieve significantly superior performance in HSI classification to the state-of-the-art CNN-based methods on real hyperspectral data sets.
机译：在这封信中，提出了一种基于CapsNet的替代数据驱动的HSI分类模型，而不是最近基于卷积神经网络（CNN）的模型。为了将CapsNet调整为HSI分类，我们调整了具有三个卷积层的新CapsNet体系结构。添加的浅层为主要胶囊提供了更高级别的功能，从而间接加快了后续的布线过程。为了保证整个CapsNet的良好收敛性，通过传输来自预训练的CNN模型的卷积参数来初始化三个浅层。改进的基于CapsNet的模型（具有和不具有表决策略）均在HSI分类方面实现了明显优于在实际高光谱数据集上基于CNN的最新方法的性能。

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