论文标题
快速的3D CNN，用于高光谱图像分类
A Fast 3D CNN for Hyperspectral Image Classification
论文作者
论文摘要
高光谱成像（HSI）已广泛用于许多现实世界应用。 HSI分类（HSIC）是一项具有挑战性的任务，这是由于高层间相似性，高层内变异性，重叠和嵌套区域。 2D卷积神经网络（CNN）是一种可行的方法，HSIC高度依赖于两个光谱空间信息，因此，由于体积和频谱维度，3D CNN可以是一种替代性但高度计算的复合物。此外，这些模型不会提取质量特征图，并且在具有类似纹理的区域的表现不佳。因此，这项工作提出了一个3D CNN模型，该模型利用两个空间光谱特征图来实现良好的性能。为了达到上述性能，HSI立方首先分为小重叠的3D贴片。后来，对这些贴片进行处理，以在多个连续频段上使用3D内核函数生成3D特征地图，这些连续频段也坚持光谱信息。认为基准HSI数据集（帕维亚大学，萨利纳斯和印度松树）被认为可以验证我们提出的方法的性能。将结果与几种最新方法进行比较。
Hyperspectral imaging (HSI) has been extensively utilized for a number of real-world applications. HSI classification (HSIC) is a challenging task due to high inter-class similarity, high intra-class variability, overlapping, and nested regions. A 2D Convolutional Neural Network (CNN) is a viable approach whereby HSIC highly depends on both Spectral-Spatial information, therefore, 3D CNN can be an alternative but highly computational complex due to the volume and spectral dimensions. Furthermore, these models do not extract quality feature maps and may underperform over the regions having similar textures. Therefore, this work proposed a 3D CNN model that utilizes both spatial-spectral feature maps to attain good performance. In order to achieve the said performance, the HSI cube is first divided into small overlapping 3D patches. Later these patches are processed to generate 3D feature maps using a 3D kernel function over multiple contiguous bands that persevere the spectral information as well. Benchmark HSI datasets (Pavia University, Salinas and Indian Pines) are considered to validate the performance of our proposed method. The results are further compared with several state-of-the-art methods.
