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A Hybrid Deep ResNet and Inception Model for Hyperspectral Image Classification
PFG-Journal of Photogrammetry, Remote Sensing and Geoinformation Science ( IF 4.1 ) Pub Date : 2020-09-03 , DOI: 10.1007/s41064-020-00124-x
Bandar Alotaibi , Munif Alotaibi

Over the past few decades, hyperspectral image (HSI) classification has garnered increasing attention from the remote sensing research community. The largest challenge faced by HSI classification is the high feature dimensions represented by the different HSI bands given the limited number of labeled samples. Deep learning and convolutional neural networks (CNNs), in particular, have been shown to be highly effective in several computer vision problems such as object detection and image classification. In terms of accuracy and computational cost, one of the best CNN architectures is the Inception model i.e., the winner of the ImageNet Large Scale Visual Recognition Competition (ILSVRC) 2014 challenge. Another architecture that has significantly improved image recognition performance is the Residual Network (ResNet) architecture i.e., the winner of the ILSVRC 2015 challenge. Inspired by the incredible performance introduced by the Inception and ResNet architectures, we investigate the possibility of combining the core ideas of these two models into a hybrid architecture to improve the HSI classification performance. We tested this combined model on four standard HSI datasets, and it shows competitive results compared with other existing HSI classification methods. Our hybrid deep ResNet-Inception architecture obtained accuracies of 95.31% on the Pavia University dataset, 99.02% on the Pavia Centre scenes dataset, 95.33% on the Salinas dataset and 90.57% on the Indian Pines dataset.



中文翻译:

高光谱图像分类的混合深度ResNet和初始模型

在过去的几十年中,高光谱图像(HSI)分类已经引起了遥感研究界的越来越多的关注。HSI分类面临的最大挑战是给定有限数量的标记样品,不同HSI谱带代表的高特征尺寸。特别是深度学习和卷积神经网络(CNN)已被证明在诸如目标检测和图像分类等若干计算机视觉问题中非常有效。就准确性和计算成本而言,最好的CNN架构之一是Inception模型,即ImageNet大型视觉识别竞赛(ILSVRC)2014挑战赛的获胜者。残差网络(ResNet)架构是另一种可显着改善图像识别性能的架构,即 2015年ILSVRC挑战赛的获胜者。受Inception和ResNet架构引入的惊人性能的启发,我们研究了将这两种模型的核心思想组合到混合架构中以改善HSI分类性能的可能性。我们在四个标准的HSI数据集上测试了此组合模型,并且与其他现有的HSI分类方法相比,它显示了竞争结果。我们的混合式深层ResNet-Inception架构在Pavia University数据集上的准确性为95.31%,在Pavia Center场景数据集上的准确性为99.02%,在Salinas数据集上为95.33%,在Indian Pines数据集上为90.57%。我们研究了将这两个模型的核心思想组合到混合体系结构中以改善HSI分类性能的可能性。我们在四个标准的HSI数据集上测试了此组合模型,并且与其他现有的HSI分类方法相比,它显示了竞争结果。我们的混合式深层ResNet-Inception架构在Pavia University数据集上的准确性为95.31%,在Pavia Center场景数据集上的准确性为99.02%,在Salinas数据集上为95.33%,在Indian Pines数据集上为90.57%。我们研究了将这两个模型的核心思想组合到混合体系结构中以改善HSI分类性能的可能性。我们在四个标准的HSI数据集上测试了此组合模型,并且与其他现有的HSI分类方法相比,它显示了竞争结果。我们的混合式深层ResNet-Inception架构在Pavia University数据集上的准确率达95.31%,在Pavia Center场景数据集上的准确率达99.02%,在Salinas数据集上的准确率达95.33%,在Indian Pines数据集上的准确率达90.57%。

更新日期:2020-09-03
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