Recently, deep learning has drawn broad attention in the hyperspectral image (HSI) classification task. Many works have focused on elaborately designing various spectral-spatial networks, where convolutional neural network (CNN) is one of the most popular structures. To explore the spatial information for HSI classification, pixels with its adjacent pixels are usually directly cropped from hyperspectral data to form HSI cubes in CNN-based methods. However, the spatial land-cover distributions of cropped HSI cubes are usually complicated. The land-cover label of a cropped HSI cube cannot simply be determined by its center pixel. In addition, the spatial land-cover distribution of a cropped HSI cube is fixed and has less diversity. For CNN-based methods, training with cropped HSI cubes will result in poor generalization to the changes of spatial land-cover distributions. In this paper, an end-to-end fully convolutional segmentation network (FCSN) is proposed to simultaneously identify land-cover labels of all pixels in a HSI cube. First, several experiments are conducted to demonstrate that recent CNN-based methods show the weak generalization capabilities. Second, a fine label style is proposed to label all pixels of HSI cubes to provide detailed spatial land-cover distributions of HSI cubes. Third, a HSI cube generation method is proposed to generate plentiful HSI cubes with fine labels to improve the diversity of spatial land-cover distributions. Finally, a FCSN is proposed to explore spectral-spatial features from finely labeled HSI cubes for HSI classification. Experimental results show that FCSN has the superior generalization capability to the changes of spatial land-cover distributions.

中文翻译：

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监督分割网络的高光谱图像分类

近年来，深度学习已引起广泛关注。 高光谱图像（HSI）分类任务。许多作品专注于精心设计各种频谱空间网络，其中卷积神经网络（CNN）是最受欢迎的结构之一。为了探索用于HSI分类的空间信息，通常使用基于CNN的方法直接从高光谱数据中裁剪出具有相邻像素的像素，以形成HSI立方体。然而，播种的恒生指数立方的空间土地覆盖分布通常很复杂。裁剪后的HSI多维数据集的土地覆盖标签不能简单地通过其中心像素来确定。此外，种植的恒生指数立方体的空间土地覆盖分布是固定的，并且多样性较低。对于基于CNN的方法，使用裁剪后的HSI立方体进行训练将导致对空间土地覆盖分布变化的概括性很差。在本文中，端到端全卷积分割网络建议使用（FCSN）来同时识别HSI多维数据集中的所有像素的陆地覆盖标签。首先，进行了一些实验以证明最近的基于CNN的方法显示出较弱的泛化能力。其次，提出了一种精细的标签样式来标记HSI多维数据集的所有像素，以提供HSI多维数据集的详细空间土地覆盖分布。第三，提出了一种HSI多维数据集生成方法，可以生成大量带有精细标签的HSI多维数据集，以改善空间土地覆被分布的多样性。最后，提出了FCSN来探索来自精细标记的HSI立方体的光谱空间特征，以进行HSI分类。实验结果表明，FCSN具有较好的泛化能力，对空间土地覆盖分布的变化具有重要意义。