论文标题
CEU-NET：使用聚类的高光谱图像的合奏语义分割
CEU-Net: Ensemble Semantic Segmentation of Hyperspectral Images Using Clustering
论文作者
论文摘要
高光谱图像（HSIS）的大多数语义分割方法都使用，并需要采用补丁的形式进行预处理步骤，以在远程感知的图像中准确地对多样化的土地覆盖进行分类。这些方法使用修补程序将丰富的邻里信息纳入图像中，并利用最常见的HSI数据集的简单性和分割性。相比之下，世界上大多数地产包括重叠和扩散的类，使邻域信息比普通HSI数据集中所看到的弱。为了解决这个问题并将细分模型推广到更复杂，更多样化的HSI数据集，在这项工作中，我们提出了新型的旗舰模型：聚类集合U-NET（CEU-NET）。 CEU-NET使用集合方法结合了从景观像素群中从卷积神经网络（CNN）培训中提取的光谱信息。我们的CEU-NET模型优于现有的最先进的HSI语义分割方法，与基线模型相比，在没有补丁的情况下具有竞争性能。与Hybridsn和Aerorit方法相比，我们强调了CEU-NET在博茨瓦纳，KSC和Salinas数据集的高性能。
Most semantic segmentation approaches of Hyperspectral images (HSIs) use and require preprocessing steps in the form of patching to accurately classify diversified land cover in remotely sensed images. These approaches use patching to incorporate the rich neighborhood information in images and exploit the simplicity and segmentability of the most common HSI datasets. In contrast, most landmasses in the world consist of overlapping and diffused classes, making neighborhood information weaker than what is seen in common HSI datasets. To combat this issue and generalize the segmentation models to more complex and diverse HSI datasets, in this work, we propose our novel flagship model: Clustering Ensemble U-Net (CEU-Net). CEU-Net uses the ensemble method to combine spectral information extracted from convolutional neural network (CNN) training on a cluster of landscape pixels. Our CEU-Net model outperforms existing state-of-the-art HSI semantic segmentation methods and gets competitive performance with and without patching when compared to baseline models. We highlight CEU-Net's high performance across Botswana, KSC, and Salinas datasets compared to HybridSN and AeroRIT methods.
