论文标题
基于数据融合传输学习的3D非对称启动网络的高光谱分类
Hyperspectral Classification Based on 3D Asymmetric Inception Network with Data Fusion Transfer Learning
论文作者
论文摘要
近年来，通过卷积神经网络（CNN）改善了高光谱图像（HSI）分类。与RGB数据集不同，不同的HSI数据集通常由各种遥控传感器捕获，并且具有不同的光谱配置。此外，每个HSI数据集仅包含非常有限的训练样本，因此在使用深CNN时很容易过度拟合。在本文中，我们首先提供了一个3D不对称的启动网络AINET，以克服过度拟合的问题。由于强调光谱标志在HSI数据的空间环境上，AINET可以有效地传达和分类这些功能。此外，提出的数据融合传输学习策略有益于提高分类性能。广泛的实验表明，拟议的方法击败了包括帕维亚大学，印度松树和肯尼迪航天中心（KSC）在内的几个HSI基准的所有最先进方法。代码可以在以下网址找到：https：//github.com/unilaux/ainet。
Hyperspectral image(HSI) classification has been improved with convolutional neural network(CNN) in very recent years. Being different from the RGB datasets, different HSI datasets are generally captured by various remote sensors and have different spectral configurations. Moreover, each HSI dataset only contains very limited training samples and thus it is prone to overfitting when using deep CNNs. In this paper, we first deliver a 3D asymmetric inception network, AINet, to overcome the overfitting problem. With the emphasis on spectral signatures over spatial contexts of HSI data, AINet can convey and classify the features effectively. In addition, the proposed data fusion transfer learning strategy is beneficial in boosting the classification performance. Extensive experiments show that the proposed approach beat all of the state-of-art methods on several HSI benchmarks, including Pavia University, Indian Pines and Kennedy Space Center(KSC). Code can be found at: https://github.com/UniLauX/AINet.
