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首页> 外文期刊>IEEE Transactions on Geoscience and Remote Sensing >Deep Pyramidal Residual Networks for Spectral–Spatial Hyperspectral Image Classification
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Deep Pyramidal Residual Networks for Spectral–Spatial Hyperspectral Image Classification

机译：深金字塔形残差网络用于光谱-空间高光谱图像分类

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摘要

Convolutional neural networks (CNNs) exhibit good performance in image processing tasks, pointing themselves as the current state-of-the-art of deep learning methods. However, the intrinsic complexity of remotely sensed hyperspectral images still limits the performance of many CNN models. The high dimensionality of the HSI data, together with the underlying redundancy and noise, often makes the standard CNN approaches unable to generalize discriminative spectral-spatial features. Moreover, deeper CNN architectures also find challenges when additional layers are added, which hampers the network convergence and produces low classification accuracies. In order to mitigate these issues, this paper presents a new deep CNN architecture specially designed for the HSI data. Our new model pursues to improve the spectral-spatial features uncovered by the convolutional filters of the network. Specifically, the proposed residual-based approach gradually increases the feature map dimension at all convolutional layers, grouped in pyramidal bottleneck residual blocks, in order to involve more locations as the network depth increases while balancing the workload among all units, preserving the time complexity per layer. It can be seen as a pyramid, where the deeper the blocks, the more feature maps can be extracted. Therefore, the diversity of high-level spectral-spatial attributes can be gradually increased across layers to enhance the performance of the proposed network with the HSI data. Our experiments, conducted using four well-known HSI data sets and 10 different classification techniques, reveal that our newly developed HSI pyramidal residual model is able to provide competitive advantages (in terms of both classification accuracy and computational time) over the state-of-the-art HSI classification methods.
机译：卷积神经网络（CNN）在图像处理任务中表现出良好的性能，将其视为当前深度学习方法的最新技术。但是，遥感高光谱图像的内在复杂性仍然限制了许多CNN模型的性能。 HSI数据的高维数以及潜在的冗余和噪声，经常使标准的CNN方法无法概括出可辨别的频谱空间特征。此外，当添加额外的层时，更深的CNN架构也会遇到挑战，这会阻碍网络融合并降低分类精度。为了减轻这些问题，本文提出了一种专门为HSI数据设计的新的深度CNN架构。我们的新模型致力于改善网络卷积滤波器未发现的频谱空间特征。具体来说，建议的基于残差的方法会逐渐增加所有卷积层的特征图维，并按金字塔形的瓶颈残差块进行分组，以便随着网络深度的增加而涉及更多位置，同时平衡所有单元之间的工作量，从而保留了每个单元的时间复杂度层。可以将其视为金字塔，在该金字塔中，块越深，可以提取的特征图越多。因此，可以跨层逐渐增加高级频谱空间属性的多样性，以增强具有HSI数据的拟议网络的性能。我们的实验是使用四个著名的HSI数据集和10种不同的分类技术进行的，结果表明，我们新开发的HSI金字塔残差模型能够提供优于以下状态的竞争优势（就分类精度和计算时间而言）最新的HSI分类方法。

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